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PARAMCD Paramater code AVAL, \"OS\". PARAM Parameter name AVAL, \"Overall Survival. CNSR Censoring indicator 0/1. TIME Survival time days. EVENT Event indicator 0/1.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/reference/adtte_twt.html","id":null,"dir":"Reference","previous_headings":"","what":"Survival data from two arm trial — adtte_twt","title":"Survival data from two arm trial — adtte_twt","text":"Survival data two arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/adtte_twt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Survival data from two arm trial — adtte_twt","text":"","code":"adtte_twt"},{"path":"https://hta-pharma.github.io/maicplus/reference/adtte_twt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Survival data from two arm trial — adtte_twt","text":"data frame 1000 rows 10 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm, \"\", \"C\". AVAL Analysis value dataset overall survival time days. AVALU Unit AVAL. PARAMCD Parameter code AVAL, \"OS\". PARAM Parameter name AVAL, \"Overall Survival. CNSR Censoring indicator 0/1. TIME Survival time days. EVENT Event indicator 0/1.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/reference/agd.html","id":null,"dir":"Reference","previous_headings":"","what":"Aggregate effect modifier data from published study — agd","title":"Aggregate effect modifier data from published study — agd","text":"data formatted used center_ipd().","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/agd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aggregate effect modifier data from published study — agd","text":"","code":"agd"},{"path":"https://hta-pharma.github.io/maicplus/reference/agd.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Aggregate effect modifier data from published study — agd","text":"data frame 3 rows 9 columns: STUDY study name, Study_XXXX ARM Study arm name total N Number observations study arm AGE_MEAN Mean age study arm AGE_MEDIAN Median age study arm AGE_SD Standard deviation age study arm SEX_MALE_COUNT Number male patients ECOG0_COUNT Number patients ECOG score = 0 SMOKE_COUNT Number smokers N_PR_THER_MEDIAN Median number prior therapies","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/reference/basic_kmplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Basic Kaplan Meier (KM) plot function — basic_kmplot","title":"Basic Kaplan Meier (KM) plot function — basic_kmplot","text":"function can generate basic KM plot without risk set table appended bottom. single plot, can include 4 KM curves. depends number levels 'treatment' column input data.frame kmdat","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/basic_kmplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Basic Kaplan Meier (KM) plot function — basic_kmplot","text":"","code":"basic_kmplot( kmdat, endpoint_name = \"Time to Event Endpoint\", time_scale = NULL, time_grid = NULL, show_risk_set = TRUE, main_title = \"Kaplan-Meier Curves\", subplot_heights = NULL, suppress_plot_layout = FALSE, use_colors = NULL, use_line_types = NULL, use_pch_cex = 0.65, use_pch_alpha = 100 )"},{"path":"https://hta-pharma.github.io/maicplus/reference/basic_kmplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Basic Kaplan Meier (KM) plot function — basic_kmplot","text":"kmdat data.frame, must consist treatment, time (unit days), n.risk, censor, surv, similar output maicplus:::survfit_makeup endpoint_name string, name time event endpoint, show last line title time_scale string, time unit median survival time, taking value 'years', 'months', 'weeks' 'days' time_grid numeric vector unit time_scale, risk set table x axis km plot defined based time grid show_risk_set logical, show risk set table , TRUE default main_title string, main title KM plot subplot_heights numeric vector, heights argument graphic::layout(),NULL default means user use default setting suppress_plot_layout logical, suppress layout setting function user can specify layout outside function, FALSE default use_colors character vector length 4, colors KM curves, passed col lines() use_line_types numeric vector length 4, line type KM curves, passed lty lines() use_pch_cex scalar 0 1, point size indicate censored individuals KM curves, passed cex points() use_pch_alpha scalar 0 255, degree color transparency points indicate censored individuals KM curves, passed cex points()","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/basic_kmplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Basic Kaplan Meier (KM) plot function — basic_kmplot","text":"KM plot without risk set table appended bottom, 4 KM curves","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/basic_kmplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Basic Kaplan Meier (KM) plot function — basic_kmplot","text":"","code":"library(survival) data(adtte_sat) data(pseudo_ipd_sat) combined_data <- rbind(adtte_sat[, c(\"TIME\", \"EVENT\", \"ARM\")], pseudo_ipd_sat) kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data, conf.type = \"log-log\") kmdat <- do.call(rbind, survfit_makeup(kmobj)) kmdat$treatment <- factor(kmdat$treatment) # without risk set table basic_kmplot(kmdat, time_scale = \"month\", time_grid = seq(0, 20, by = 2), show_risk_set = FALSE, main_title = \"Kaplan-Meier Curves\", subplot_heights = NULL, suppress_plot_layout = FALSE, use_colors = NULL, use_line_types = NULL ) # with risk set table basic_kmplot(kmdat, time_scale = \"month\", time_grid = seq(0, 20, by = 2), show_risk_set = TRUE, main_title = \"Kaplan-Meier Curves\", subplot_heights = NULL, suppress_plot_layout = FALSE, use_colors = NULL, use_line_types = NULL )"},{"path":"https://hta-pharma.github.io/maicplus/reference/basic_kmplot2.html","id":null,"dir":"Reference","previous_headings":"","what":"Basic Kaplan Meier (KM) plot function using ggplot — basic_kmplot2","title":"Basic Kaplan Meier (KM) plot function using ggplot — basic_kmplot2","text":"function generates basic KM plot using ggplot.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/basic_kmplot2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Basic Kaplan Meier (KM) plot function using ggplot — basic_kmplot2","text":"","code":"basic_kmplot2( kmlist, kmlist_name, endpoint_name = \"Time to Event Endpoint\", show_risk_set = TRUE, main_title = \"Kaplan-Meier Curves\", break_x_by = NULL, censor = TRUE, xlab = \"Time\", xlim = NULL, use_colors = NULL, use_line_types = NULL )"},{"path":"https://hta-pharma.github.io/maicplus/reference/basic_kmplot2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Basic Kaplan Meier (KM) plot function using ggplot — basic_kmplot2","text":"kmlist list survfit object kmlist_name vector indicating treatment names survfit object endpoint_name string, name time event endpoint, show last line title show_risk_set logical, show risk set table , TRUE default main_title string, main title KM plot break_x_by bin parameter survminer censor indicator include censor information xlab label name x-axis plot xlim x limit x-axis plot use_colors character vector length 4, colors KM curves, passed 'col' lines() use_line_types numeric vector length 4, line type KM curves, passed lty lines()","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/basic_kmplot2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Basic Kaplan Meier (KM) plot function using ggplot — basic_kmplot2","text":"","code":"library(survival) data(adtte_sat) data(pseudo_ipd_sat) kmobj_A <- survfit(Surv(TIME, EVENT) ~ ARM, data = adtte_sat, conf.type = \"log-log\" ) kmobj_B <- survfit(Surv(TIME, EVENT) ~ ARM, data = pseudo_ipd_sat, conf.type = \"log-log\" ) kmlist <- list(kmobj_A = kmobj_A, kmobj_B = kmobj_B) kmlist_name <- c(\"A\", \"B\") basic_kmplot2(kmlist, kmlist_name) #> Warning: There was 1 warning in `mutate()`. #> ℹ In argument: `survtable = purrr::map2(...)`. #> Caused by warning: #> ! `select_()` was deprecated in dplyr 0.7.0. #> ℹ Please use `select()` instead. #> ℹ The deprecated feature was likely used in the survminer package. #> Please report the issue at ."},{"path":"https://hta-pharma.github.io/maicplus/reference/bucher.html","id":null,"dir":"Reference","previous_headings":"","what":"Bucher method for combining treatment effects — bucher","title":"Bucher method for combining treatment effects — bucher","text":"Given two treatment effects vs. C B vs. C derive treatment effects vs. B using Bucher method. Two-sided confidence interval Z-test p-value also calculated. Treatment effects standard errors log scale hazard ratio, odds ratio, risk ratio. Treatment effects standard errors natural scale risk difference mean difference.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/bucher.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bucher method for combining treatment effects — bucher","text":"","code":"bucher(trt, com, conf_lv = 0.95) # S3 method for class 'maicplus_bucher' print(x, ci_digits = 2, pval_digits = 3, exponentiate = FALSE, ...)"},{"path":"https://hta-pharma.github.io/maicplus/reference/bucher.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bucher method for combining treatment effects — bucher","text":"trt list two scalars study experimental arm. 'est' point estimate 'se' standard error treatment effect. time--event data, 'est' 'se' point estimate standard error log hazard ratio. binary data, 'est' 'se' point estimate standard error log odds ratio, log risk ratio, risk difference. continuous data, 'est' 'se' point estimate standard error mean difference. com trt, study control arm conf_lv numerical scalar, prescribe confidence level derive two-sided confidence interval treatment effect x maicplus_bucher object ci_digits integer, number decimal places point estimate derived confidence limits pval_digits integer, number decimal places display Z-test p-value exponentiate whether treatment effect confidence interval exponentiated. applies relative treatment effects. Default set false. ... used","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/bucher.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bucher method for combining treatment effects — bucher","text":"list 5 elements, est scalar, point estimate treatment effect se scalar, standard error treatment effect ci_l scalar, lower confidence limit two-sided CI prescribed nominal level conf_lv ci_u scalar, upper confidence limit two-sided CI prescribed nominal level conf_lv pval p-value Z-test, null hypothesis est zero","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/bucher.html","id":"methods-by-generic-","dir":"Reference","previous_headings":"","what":"Methods (by generic)","title":"Bucher method for combining treatment effects — bucher","text":"print(maicplus_bucher): Print method maicplus_bucher objects","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/bucher.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bucher method for combining treatment effects — bucher","text":"","code":"trt <- list(est = log(1.1), se = 0.2) com <- list(est = log(1.3), se = 0.18) result <- bucher(trt, com, conf_lv = 0.9) print(result, ci_digits = 3, pval_digits = 3) #> result pvalue #> \"-0.167 [-0.610; 0.276]\" \"0.535\""},{"path":"https://hta-pharma.github.io/maicplus/reference/calculate_weights_legend.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","title":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","text":"Calculates ESS reduction median weights used create legend weights plot","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/calculate_weights_legend.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","text":"","code":"calculate_weights_legend(weighted_data)"},{"path":"https://hta-pharma.github.io/maicplus/reference/calculate_weights_legend.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","text":"weighted_data object returned calculating weights using estimate_weights","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/calculate_weights_legend.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","text":"list ESS, ESS reduction, median value scaled unscaled weights, missing count","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/calculate_weights_legend.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","text":"","code":"data(\"weighted_sat\") calculate_weights_legend(weighted_sat) #> $ess #> [1] 121.99 #> #> $ess_reduction #> [1] 75.6 #> #> $wt_median #> [1] 0.0567 #> #> $wt_scaled_median #> [1] 0.1635 #> #> $nr_na #> [1] 0 #>"},{"path":"https://hta-pharma.github.io/maicplus/reference/center_ipd.html","id":null,"dir":"Reference","previous_headings":"","what":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","title":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","text":"function subtracts IPD variables (prognostic variables /effect modifiers) aggregate data averages. centering needed order calculate weights. IPD aggregate data variable names match.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/center_ipd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","text":"","code":"center_ipd(ipd, agd)"},{"path":"https://hta-pharma.github.io/maicplus/reference/center_ipd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","text":"ipd IPD variable names match aggregate data names without suffix. involve either changing aggregate data name ipd name. instance, binarize SEX variable MALE reference using dummize_ipd, function names new variable SEX_MALE. case, SEX_MALE also available aggregate data. agd pre-processed aggregate data contain STUDY, ARM, N. Variable names followed legal suffixes (.e. MEAN, MEDIAN, SD, PROP). Note COUNT suffix longer accepted.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/center_ipd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","text":"centered ipd using aggregate level data averages","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/center_ipd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","text":"","code":"data(adsl_sat) data(agd) agd <- process_agd(agd) ipd_centered <- center_ipd(ipd = adsl_sat, agd = agd)"},{"path":"https://hta-pharma.github.io/maicplus/reference/centered_ipd_sat.html","id":null,"dir":"Reference","previous_headings":"","what":"Centered patient data from single arm trial — centered_ipd_sat","title":"Centered patient data from single arm trial — centered_ipd_sat","text":"Centered patient data single arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/centered_ipd_sat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Centered patient data from single arm trial — centered_ipd_sat","text":"","code":"centered_ipd_sat"},{"path":"https://hta-pharma.github.io/maicplus/reference/centered_ipd_sat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Centered patient data from single arm trial — centered_ipd_sat","text":"data frame 500 rows 14 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm. AGE Age years baseline. SEX Sex patient recorded character \"Male\"/\"Female\". SMOKE Smoking status baseline integer 1/0. ECOG0 Indicator ECOG score = 0 baseline integer 1/0. N_PR_THER Number prior therapies received integer 1, 2, 3, 4. SEX_MALE Indicator SEX == \"Male\" numeric 1/0. AGE_CENTERED Age years baseline relative average aggregate data agd. AGE_MEDIAN_CENTERED AGE greater/less MEDIAN_AGE agd coded 1/0 centered 0.5. AGE_SQUARED_CENTERED AGE squared centered respect AGE agd. squared age aggregate data derived \\(E(X^2)\\) term variance formula. SEX_MALE_CENTERED SEX_MALE centered proportion male patients agd ECOG0_CENTERED ECOG0 centered proportion ECOG0 agd SMOKE_CENTERED SMOKE centered proportion SMOKE agd N_PR_THER_MEDIAN_CENTERED N_PR_THER centered median agd.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/reference/centered_ipd_twt.html","id":null,"dir":"Reference","previous_headings":"","what":"Centered patient data from two arm trial — centered_ipd_twt","title":"Centered patient data from two arm trial — centered_ipd_twt","text":"Centered patient data two arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/centered_ipd_twt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Centered patient data from two arm trial — centered_ipd_twt","text":"","code":"centered_ipd_twt"},{"path":"https://hta-pharma.github.io/maicplus/reference/centered_ipd_twt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Centered patient data from two arm trial — centered_ipd_twt","text":"data frame 1000 rows 14 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm. AGE Age years baseline. SEX Sex patient recorded character \"Male\"/\"Female\". SMOKE Smoking status baseline integer 1/0. ECOG0 Indicator ECOG score = 0 baseline integer 1/0. N_PR_THER Number prior therapies received integer 1, 2, 3, 4. SEX_MALE Indicator SEX == \"Male\" numeric 1/0. AGE_CENTERED Age years baseline relative average aggregate data agd. AGE_MEDIAN_CENTERED AGE greater/less MEDIAN_AGE agd coded 1/0 centered 0.5. AGE_SQUARED_CENTERED AGE squared centered respect AGE agd. squared age aggregate data derived \\(E(X^2)\\) term variance formula. SEX_MALE_CENTERED SEX_MALE centered proportion male patients agd ECOG0_CENTERED ECOG0 centered proportion ECOG0 agd SMOKE_CENTERED SMOKE centered proportion SMOKE agd N_PR_THER_MEDIAN_CENTERED N_PR_THER centered median agd.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/reference/check_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Check to see if weights are optimized correctly — check_weights","title":"Check to see if weights are optimized correctly — check_weights","text":"function checks see optimization done properly checking covariate averages adjustment.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/check_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check to see if weights are optimized correctly — check_weights","text":"","code":"check_weights(weighted_data, processed_agd) # S3 method for class 'maicplus_check_weights' print( x, mean_digits = 2, prop_digits = 2, sd_digits = 3, digits = getOption(\"digits\"), ... )"},{"path":"https://hta-pharma.github.io/maicplus/reference/check_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check to see if weights are optimized correctly — check_weights","text":"weighted_data object returned calculating weights using estimate_weights processed_agd data frame, object returned using process_agd aggregated data following naming convention x object check_weights mean_digits number digits rounding mean columns output prop_digits number digits rounding proportion columns output sd_digits number digits rounding mean columns output digits minimal number significant digits, see print.default. ... arguments print.data.frame","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/check_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check to see if weights are optimized correctly — check_weights","text":"data.frame weighted unweighted covariate averages IPD, average aggregate data, sum inner products covariate \\(x_i\\) weights (\\(exp(x_i\\beta)\\))","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/check_weights.html","id":"methods-by-generic-","dir":"Reference","previous_headings":"","what":"Methods (by generic)","title":"Check to see if weights are optimized correctly — check_weights","text":"print(maicplus_check_weights): Print method check_weights objects","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/check_weights.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check to see if weights are optimized correctly — check_weights","text":"","code":"data(weighted_sat) data(agd) check_weights(weighted_sat, process_agd(agd)) #> covariate match_stat internal_trial internal_trial_after_weighted #> 1 AGE Mean 59.850 51.00 #> 2 AGE Median 59.000 49.00 #> 3 AGE SD 9.011 3.25 #> 4 SEX_MALE Prop 0.380 0.49 #> 5 ECOG0 Prop 0.410 0.35 #> 6 SMOKE Prop 0.320 0.19 #> external_trial sum_centered_IPD_with_weights #> 1 51.00 0.0000 #> 2 49.00 0.0000 #> 3 3.25 -0.0045 #> 4 0.49 0.0000 #> 5 0.35 0.0000 #> 6 0.19 0.0000"},{"path":"https://hta-pharma.github.io/maicplus/reference/complete_agd.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate pooled arm statistics in Aggregated Data (AgD) based on arm-specific statistics — complete_agd","title":"Calculate pooled arm statistics in Aggregated Data (AgD) based on arm-specific statistics — complete_agd","text":"convenient function pool arm statistics. function called within process_agd ARM equal \"Total\". Note pooled median calculated approximation.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/complete_agd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate pooled arm statistics in Aggregated Data (AgD) based on arm-specific statistics — complete_agd","text":"","code":"complete_agd(use_agd)"},{"path":"https://hta-pharma.github.io/maicplus/reference/complete_agd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate pooled arm statistics in Aggregated Data (AgD) based on arm-specific statistics — complete_agd","text":"use_agd aggregated data processed within process_agd","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/complete_agd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate pooled arm statistics in Aggregated Data (AgD) based on arm-specific statistics — complete_agd","text":"Complete N, count, mean, sd, median pooled arm","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/dummize_ipd.html","id":null,"dir":"Reference","previous_headings":"","what":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","title":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","text":"convenient function convert categorical variables dummy binary variables. especially useful variable two factors. Note original variable kept variable dummized.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/dummize_ipd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","text":"","code":"dummize_ipd(raw_ipd, dummize_cols, dummize_ref_level)"},{"path":"https://hta-pharma.github.io/maicplus/reference/dummize_ipd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","text":"raw_ipd ipd data contains variable dummize dummize_cols vector column names binarize dummize_ref_level vector reference level variables binarize","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/dummize_ipd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","text":"ipd dummized columns","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/dummize_ipd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","text":"","code":"data(adsl_twt) dummize_ipd(adsl_twt, dummize_cols = c(\"SEX\"), dummize_ref_level = c(\"Male\")) #> USUBJID ARM AGE SEX SMOKE ECOG0 N_PR_THER SEX_MALE SEX_FEMALE #> 1 xx1 A 45 Male 0 0 4 1 0 #> 2 xx2 A 71 Male 0 0 3 1 0 #> 3 xx3 A 58 Male 1 1 2 1 0 #> 4 xx4 A 48 Female 0 1 4 0 1 #> 5 xx5 A 69 Male 0 1 4 1 0 #> 6 xx6 A 48 Female 0 1 4 0 1 #> 7 xx7 A 47 Male 1 0 3 1 0 #> 8 xx8 A 61 Male 1 0 1 1 0 #> 9 xx9 A 54 Female 1 1 1 0 1 #> 10 xx10 A 56 Female 1 0 3 0 1 #> 11 xx11 A 63 Female 0 0 4 0 1 #> 12 xx12 A 50 Female 0 0 1 0 1 #> 13 xx13 A 57 Male 0 1 3 1 0 #> 14 xx14 A 62 Female 1 1 1 0 1 #> 15 xx15 A 57 Female 0 1 3 0 1 #> 16 xx16 A 66 Male 0 0 2 1 0 #> 17 xx17 A 75 Male 1 1 3 1 0 #> 18 xx18 A 47 Female 0 0 4 0 1 #> 19 xx19 A 57 Male 0 0 3 1 0 #> 20 xx20 A 54 Male 0 0 3 1 0 #> 21 xx21 A 55 Male 1 0 3 1 0 #> 22 xx22 A 64 Male 0 1 3 1 0 #> 23 xx23 A 53 Female 1 0 3 0 1 #> 24 xx24 A 58 Male 1 1 2 1 0 #> 25 xx25 A 47 Male 0 0 1 1 0 #> 26 xx26 A 60 Female 1 0 1 0 1 #> 27 xx27 A 49 Female 0 1 3 0 1 #> 28 xx28 A 55 Female 0 0 1 0 1 #> 29 xx29 A 66 Female 0 1 2 0 1 #> 30 xx30 A 58 Male 0 1 4 1 0 #> 31 xx31 A 49 Male 0 1 4 1 0 #> 32 xx32 A 61 Male 0 0 4 1 0 #> 33 xx33 A 66 Male 1 0 3 1 0 #> 34 xx34 A 45 Male 0 0 1 1 0 #> 35 xx35 A 59 Female 1 1 2 0 1 #> 36 xx36 A 74 Female 1 0 4 0 1 #> 37 xx37 A 73 Female 0 0 3 0 1 #> 38 xx38 A 74 Male 0 1 4 1 0 #> 39 xx39 A 54 Male 0 0 1 1 0 #> 40 xx40 A 58 Female 1 1 1 0 1 #> 41 xx41 A 61 Female 0 1 3 0 1 #> 42 xx42 A 47 Female 1 1 2 0 1 #> 43 xx43 A 73 Female 1 1 2 0 1 #> 44 xx44 A 68 Male 0 0 1 1 0 #> 45 xx45 A 49 Female 0 0 3 0 1 #> 46 xx46 A 71 Female 0 0 2 0 1 #> 47 xx47 A 70 Male 0 1 4 1 0 #> 48 xx48 A 62 Female 1 0 1 0 1 #> 49 xx49 A 49 Male 0 0 1 1 0 #> 50 xx50 A 74 Female 0 0 1 0 1 #> 51 xx51 A 46 Female 0 1 3 0 1 #> 52 xx52 A 68 Female 1 0 3 0 1 #> 53 xx53 A 46 Male 1 0 2 1 0 #> 54 xx54 A 75 Female 1 1 3 0 1 #> 55 xx55 A 47 Female 0 0 3 0 1 #> 56 xx56 A 56 Male 0 1 3 1 0 #> 57 xx57 A 72 Female 0 0 3 0 1 #> 58 xx58 A 57 Male 1 1 4 1 0 #> 59 xx59 A 46 Male 0 0 1 1 0 #> 60 xx60 A 56 Female 1 1 1 0 1 #> 61 xx61 A 73 Male 0 1 2 1 0 #> 62 xx62 A 60 Female 1 1 3 0 1 #> 63 xx63 A 75 Male 0 0 2 1 0 #> 64 xx64 A 69 Female 1 1 2 0 1 #> 65 xx65 A 47 Female 0 1 1 0 1 #> 66 xx66 A 74 Male 0 0 4 1 0 #> 67 xx67 A 71 Female 0 1 1 0 1 #> 68 xx68 A 49 Female 1 1 1 0 1 #> 69 xx69 A 68 Male 0 0 3 1 0 #> 70 xx70 A 49 Male 0 1 1 1 0 #> 71 xx71 A 70 Male 0 1 1 1 0 #> 72 xx72 A 45 Female 0 0 2 0 1 #> 73 xx73 A 47 Female 0 1 3 0 1 #> 74 xx74 A 58 Male 0 1 3 1 0 #> 75 xx75 A 49 Female 0 1 4 0 1 #> 76 xx76 A 68 Female 0 0 1 0 1 #> 77 xx77 A 60 Male 0 0 4 1 0 #> 78 xx78 A 45 Female 1 0 1 0 1 #> 79 xx79 A 57 Female 0 0 1 0 1 #> 80 xx80 A 50 Female 0 1 1 0 1 #> 81 xx81 A 63 Male 0 1 3 1 0 #> 82 xx82 A 47 Female 0 0 2 0 1 #> 83 xx83 A 68 Female 0 1 4 0 1 #> 84 xx84 A 51 Male 0 0 4 1 0 #> 85 xx85 A 60 Male 0 0 1 1 0 #> 86 xx86 A 52 Female 1 0 4 0 1 #> 87 xx87 A 69 Male 1 1 1 1 0 #> 88 xx88 A 70 Female 0 1 4 0 1 #> 89 xx89 A 72 Male 0 0 2 1 0 #> 90 xx90 A 46 Female 0 1 1 0 1 #> 91 xx91 A 51 Male 1 0 4 1 0 #> 92 xx92 A 69 Female 0 1 1 0 1 #> 93 xx93 A 66 Male 0 0 1 1 0 #> 94 xx94 A 73 Male 0 1 1 1 0 #> 95 xx95 A 73 Female 1 0 3 0 1 #> 96 xx96 A 62 Female 0 1 2 0 1 #> 97 xx97 A 55 Female 0 0 4 0 1 #> 98 xx98 A 67 Male 1 0 3 1 0 #> 99 xx99 A 54 Female 1 0 3 0 1 #> 100 xx100 A 52 Female 1 1 4 0 1 #> 101 xx101 A 57 Male 0 0 2 1 0 #> 102 xx102 A 57 Female 1 1 3 0 1 #> 103 xx103 A 57 Male 0 0 3 1 0 #> 104 xx104 A 67 Female 1 1 2 0 1 #> 105 xx105 A 67 Female 1 1 2 0 1 #> 106 xx106 A 74 Female 1 1 2 0 1 #> 107 xx107 A 72 Female 1 0 2 0 1 #> 108 xx108 A 73 Female 0 0 3 0 1 #> 109 xx109 A 57 Female 0 0 4 0 1 #> 110 xx110 A 69 Female 1 0 1 0 1 #> 111 xx111 A 55 Male 0 0 1 1 0 #> 112 xx112 A 74 Female 0 0 4 0 1 #> 113 xx113 A 68 Female 0 0 4 0 1 #> 114 xx114 A 53 Male 0 0 2 1 0 #> 115 xx115 A 69 Male 0 0 2 1 0 #> 116 xx116 A 68 Male 0 1 2 1 0 #> 117 xx117 A 58 Male 0 0 1 1 0 #> 118 xx118 A 64 Female 0 0 3 0 1 #> 119 xx119 A 71 Male 0 0 1 1 0 #> 120 xx120 A 69 Female 0 1 2 0 1 #> 121 xx121 A 64 Female 1 0 4 0 1 #> 122 xx122 A 64 Male 1 0 1 1 0 #> 123 xx123 A 55 Male 0 1 3 1 0 #> 124 xx124 A 74 Male 0 0 3 1 0 #> 125 xx125 A 50 Male 0 1 3 1 0 #> 126 xx126 A 68 Male 0 1 1 1 0 #> 127 xx127 A 60 Male 0 0 2 1 0 #> 128 xx128 A 59 Female 0 0 3 0 1 #> 129 xx129 A 71 Female 1 0 3 0 1 #> 130 xx130 A 69 Male 0 1 4 1 0 #> 131 xx131 A 56 Female 0 1 1 0 1 #> 132 xx132 A 51 Male 0 1 3 1 0 #> 133 xx133 A 65 Male 0 1 2 1 0 #> 134 xx134 A 45 Male 1 1 3 1 0 #> 135 xx135 A 49 Female 0 0 2 0 1 #> 136 xx136 A 74 Female 1 0 4 0 1 #> 137 xx137 A 71 Female 1 1 3 0 1 #> 138 xx138 A 65 Male 0 0 1 1 0 #> 139 xx139 A 67 Female 0 0 2 0 1 #> 140 xx140 A 48 Female 0 0 3 0 1 #> 141 xx141 A 70 Female 0 1 4 0 1 #> 142 xx142 A 72 Female 0 0 1 0 1 #> 143 xx143 A 53 Male 1 1 4 1 0 #> 144 xx144 A 68 Female 1 0 3 0 1 #> 145 xx145 A 65 Male 0 1 1 1 0 #> 146 xx146 A 70 Female 1 1 4 0 1 #> 147 xx147 A 58 Male 0 0 2 1 0 #> 148 xx148 A 68 Female 0 1 4 0 1 #> 149 xx149 A 54 Female 0 0 4 0 1 #> 150 xx150 A 75 Female 1 1 4 0 1 #> 151 xx151 A 49 Female 1 1 3 0 1 #> 152 xx152 A 60 Male 0 0 4 1 0 #> 153 xx153 A 45 Female 1 0 4 0 1 #> 154 xx154 A 73 Female 1 0 3 0 1 #> 155 xx155 A 71 Female 0 1 3 0 1 #> 156 xx156 A 73 Female 0 0 3 0 1 #> 157 xx157 A 58 Female 0 0 4 0 1 #> 158 xx158 A 59 Female 0 1 2 0 1 #> 159 xx159 A 75 Female 0 1 2 0 1 #> 160 xx160 A 65 Male 0 0 2 1 0 #> 161 xx161 A 48 Male 1 0 2 1 0 #> 162 xx162 A 63 Female 0 1 2 0 1 #> 163 xx163 A 74 Female 0 0 3 0 1 #> 164 xx164 A 64 Female 0 0 3 0 1 #> 165 xx165 A 49 Female 0 0 1 0 1 #> 166 xx166 A 56 Female 0 1 4 0 1 #> 167 xx167 A 56 Female 0 1 2 0 1 #> 168 xx168 A 48 Female 0 0 1 0 1 #> 169 xx169 A 65 Male 0 1 3 1 0 #> 170 xx170 A 53 Female 0 0 3 0 1 #> 171 xx171 A 72 Male 0 0 2 1 0 #> 172 xx172 A 75 Female 1 0 4 0 1 #> 173 xx173 A 70 Female 0 1 2 0 1 #> 174 xx174 A 53 Female 1 0 2 0 1 #> 175 xx175 A 45 Female 1 1 3 0 1 #> 176 xx176 A 53 Female 1 1 3 0 1 #> 177 xx177 A 52 Female 0 1 2 0 1 #> 178 xx178 A 61 Female 1 0 4 0 1 #> 179 xx179 A 70 Male 0 0 3 1 0 #> 180 xx180 A 58 Female 0 0 3 0 1 #> 181 xx181 A 54 Male 0 0 1 1 0 #> 182 xx182 A 53 Male 0 0 2 1 0 #> 183 xx183 A 74 Male 0 0 2 1 0 #> 184 xx184 A 64 Male 0 1 3 1 0 #> 185 xx185 A 52 Male 0 0 1 1 0 #> 186 xx186 A 73 Female 0 0 3 0 1 #> 187 xx187 A 55 Female 1 0 4 0 1 #> 188 xx188 A 71 Female 0 0 3 0 1 #> 189 xx189 A 57 Female 1 0 3 0 1 #> 190 xx190 A 49 Female 1 0 2 0 1 #> 191 xx191 A 69 Male 0 0 4 1 0 #> 192 xx192 A 74 Female 1 0 2 0 1 #> 193 xx193 A 59 Female 0 0 3 0 1 #> 194 xx194 A 53 Male 0 0 4 1 0 #> 195 xx195 A 52 Female 0 1 3 0 1 #> 196 xx196 A 47 Female 1 1 1 0 1 #> 197 xx197 A 61 Female 0 0 4 0 1 #> 198 xx198 A 51 Female 0 0 2 0 1 #> 199 xx199 A 62 Female 1 0 1 0 1 #> 200 xx200 A 59 Female 1 0 2 0 1 #> 201 xx201 A 58 Male 0 0 2 1 0 #> 202 xx202 A 61 Female 0 1 4 0 1 #> 203 xx203 A 45 Female 0 0 1 0 1 #> 204 xx204 A 59 Male 1 1 2 1 0 #> 205 xx205 A 58 Female 1 0 2 0 1 #> 206 xx206 A 67 Male 0 0 1 1 0 #> 207 xx207 A 51 Female 1 0 2 0 1 #> 208 xx208 A 68 Male 1 1 3 1 0 #> 209 xx209 A 53 Female 1 0 2 0 1 #> 210 xx210 A 64 Male 1 0 3 1 0 #> 211 xx211 A 61 Male 0 1 1 1 0 #> 212 xx212 A 52 Male 1 0 2 1 0 #> 213 xx213 A 57 Female 0 1 4 0 1 #> 214 xx214 A 57 Female 1 0 2 0 1 #> 215 xx215 A 49 Female 0 0 4 0 1 #> 216 xx216 A 51 Male 1 1 3 1 0 #> 217 xx217 A 60 Female 0 0 2 0 1 #> 218 xx218 A 60 Female 1 0 1 0 1 #> 219 xx219 A 71 Female 0 0 3 0 1 #> 220 xx220 A 54 Female 0 0 1 0 1 #> 221 xx221 A 51 Male 1 0 1 1 0 #> 222 xx222 A 71 Male 0 1 3 1 0 #> 223 xx223 A 47 Male 0 0 2 1 0 #> 224 xx224 A 65 Male 0 0 1 1 0 #> 225 xx225 A 53 Female 0 0 1 0 1 #> 226 xx226 A 56 Female 0 0 1 0 1 #> 227 xx227 A 51 Male 1 0 4 1 0 #> 228 xx228 A 68 Female 0 0 2 0 1 #> 229 xx229 A 75 Female 0 1 1 0 1 #> 230 xx230 A 49 Female 0 0 2 0 1 #> 231 xx231 A 74 Male 1 0 1 1 0 #> 232 xx232 A 66 Female 0 0 1 0 1 #> 233 xx233 A 74 Female 0 0 1 0 1 #> 234 xx234 A 62 Female 0 1 2 0 1 #> 235 xx235 A 53 Female 0 0 1 0 1 #> 236 xx236 A 62 Female 0 1 1 0 1 #> 237 xx237 A 70 Female 1 0 3 0 1 #> 238 xx238 A 60 Female 1 0 2 0 1 #> 239 xx239 A 72 Male 0 0 1 1 0 #> 240 xx240 A 74 Female 1 0 3 0 1 #> 241 xx241 A 47 Female 1 0 3 0 1 #> 242 xx242 A 54 Female 0 1 3 0 1 #> 243 xx243 A 65 Female 0 1 2 0 1 #> 244 xx244 A 74 Male 0 1 1 1 0 #> 245 xx245 A 61 Male 0 1 2 1 0 #> 246 xx246 A 54 Female 1 0 3 0 1 #> 247 xx247 A 65 Male 0 0 3 1 0 #> 248 xx248 A 72 Female 1 0 3 0 1 #> 249 xx249 A 71 Female 1 1 2 0 1 #> 250 xx250 A 65 Male 0 0 3 1 0 #> 251 xx251 A 58 Male 0 1 1 1 0 #> 252 xx252 A 46 Male 0 0 1 1 0 #> 253 xx253 A 53 Female 1 0 4 0 1 #> 254 xx254 A 71 Female 1 0 4 0 1 #> 255 xx255 A 47 Male 0 1 3 1 0 #> 256 xx256 A 45 Male 0 0 1 1 0 #> 257 xx257 A 50 Male 0 0 3 1 0 #> 258 xx258 A 67 Female 1 0 3 0 1 #> 259 xx259 A 72 Male 1 0 2 1 0 #> 260 xx260 A 45 Female 0 1 2 0 1 #> 261 xx261 A 75 Female 1 0 1 0 1 #> 262 xx262 A 65 Male 0 0 3 1 0 #> 263 xx263 A 60 Female 0 1 3 0 1 #> 264 xx264 A 75 Female 0 1 4 0 1 #> 265 xx265 A 60 Female 1 0 4 0 1 #> 266 xx266 A 49 Female 1 0 2 0 1 #> 267 xx267 A 58 Female 0 1 1 0 1 #> 268 xx268 A 57 Male 1 1 3 1 0 #> 269 xx269 A 69 Male 1 0 2 1 0 #> 270 xx270 A 51 Male 0 1 1 1 0 #> 271 xx271 A 54 Female 1 0 4 0 1 #> 272 xx272 A 55 Male 0 1 3 1 0 #> 273 xx273 A 49 Female 0 0 4 0 1 #> 274 xx274 A 74 Female 1 0 1 0 1 #> 275 xx275 A 55 Male 1 0 1 1 0 #> 276 xx276 A 52 Female 1 0 1 0 1 #> 277 xx277 A 65 Male 0 0 2 1 0 #> 278 xx278 A 70 Female 1 0 1 0 1 #> 279 xx279 A 66 Female 1 1 2 0 1 #> 280 xx280 A 63 Female 0 1 4 0 1 #> 281 xx281 A 61 Female 0 1 3 0 1 #> 282 xx282 A 65 Male 0 1 2 1 0 #> 283 xx283 A 73 Male 0 0 2 1 0 #> 284 xx284 A 55 Female 1 1 4 0 1 #> 285 xx285 A 56 Female 1 1 4 0 1 #> 286 xx286 A 68 Female 0 1 1 0 1 #> 287 xx287 A 74 Female 1 0 4 0 1 #> 288 xx288 A 67 Female 0 0 2 0 1 #> 289 xx289 A 66 Male 0 1 3 1 0 #> 290 xx290 A 48 Female 0 0 3 0 1 #> 291 xx291 A 49 Female 1 1 3 0 1 #> 292 xx292 A 60 Female 1 1 1 0 1 #> 293 xx293 A 69 Female 0 1 4 0 1 #> 294 xx294 A 58 Female 0 1 3 0 1 #> 295 xx295 A 45 Female 1 1 4 0 1 #> 296 xx296 A 49 Female 0 1 1 0 1 #> 297 xx297 A 67 Female 1 0 4 0 1 #> 298 xx298 A 63 Male 0 0 4 1 0 #> 299 xx299 A 50 Female 0 0 2 0 1 #> 300 xx300 A 68 Female 0 1 3 0 1 #> 301 xx301 A 53 Male 0 1 2 1 0 #> 302 xx302 A 63 Male 0 1 2 1 0 #> 303 xx303 A 58 Male 0 0 4 1 0 #> 304 xx304 A 70 Female 0 1 4 0 1 #> 305 xx305 A 56 Female 0 0 1 0 1 #> 306 xx306 A 56 Male 0 1 3 1 0 #> 307 xx307 A 61 Female 0 0 3 0 1 #> 308 xx308 A 72 Male 0 0 2 1 0 #> 309 xx309 A 51 Male 0 1 1 1 0 #> 310 xx310 A 72 Male 0 0 4 1 0 #> 311 xx311 A 64 Female 0 0 3 0 1 #> 312 xx312 A 59 Male 0 0 2 1 0 #> 313 xx313 A 75 Female 0 0 3 0 1 #> 314 xx314 A 75 Female 0 0 1 0 1 #> 315 xx315 A 74 Male 0 0 2 1 0 #> 316 xx316 A 54 Male 0 1 4 1 0 #> 317 xx317 A 55 Female 0 0 3 0 1 #> 318 xx318 A 52 Female 1 0 1 0 1 #> 319 xx319 A 46 Female 0 1 1 0 1 #> 320 xx320 A 53 Male 0 0 1 1 0 #> 321 xx321 A 54 Female 0 1 1 0 1 #> 322 xx322 A 62 Female 0 0 2 0 1 #> 323 xx323 A 54 Male 1 0 4 1 0 #> 324 xx324 A 56 Female 0 0 4 0 1 #> 325 xx325 A 48 Female 0 0 1 0 1 #> 326 xx326 A 52 Female 0 1 1 0 1 #> 327 xx327 A 55 Female 0 1 3 0 1 #> 328 xx328 A 69 Female 0 0 2 0 1 #> 329 xx329 A 48 Female 0 0 1 0 1 #> 330 xx330 A 48 Female 1 1 3 0 1 #> 331 xx331 A 60 Male 0 0 3 1 0 #> 332 xx332 A 74 Female 0 1 2 0 1 #> 333 xx333 A 45 Female 0 0 1 0 1 #> 334 xx334 A 64 Male 1 0 1 1 0 #> 335 xx335 A 75 Female 1 1 3 0 1 #> 336 xx336 A 62 Female 0 0 3 0 1 #> 337 xx337 A 71 Male 0 0 4 1 0 #> 338 xx338 A 48 Female 1 1 3 0 1 #> 339 xx339 A 53 Female 0 0 2 0 1 #> 340 xx340 A 62 Male 0 0 3 1 0 #> 341 xx341 A 69 Female 0 0 2 0 1 #> 342 xx342 A 72 Female 0 0 1 0 1 #> 343 xx343 A 61 Female 0 0 2 0 1 #> 344 xx344 A 47 Male 0 0 2 1 0 #> 345 xx345 A 58 Male 0 1 1 1 0 #> 346 xx346 A 52 Female 0 0 4 0 1 #> 347 xx347 A 49 Female 0 0 4 0 1 #> 348 xx348 A 51 Female 0 0 1 0 1 #> 349 xx349 A 51 Female 1 1 2 0 1 #> 350 xx350 A 72 Male 0 0 3 1 0 #> 351 xx351 A 68 Male 0 0 3 1 0 #> 352 xx352 A 49 Female 0 0 4 0 1 #> 353 xx353 A 45 Female 0 0 3 0 1 #> 354 xx354 A 49 Female 0 0 3 0 1 #> 355 xx355 A 65 Male 0 1 2 1 0 #> 356 xx356 A 56 Male 0 0 2 1 0 #> 357 xx357 A 45 Female 1 1 1 0 1 #> 358 xx358 A 57 Male 1 0 2 1 0 #> 359 xx359 A 53 Male 1 1 2 1 0 #> 360 xx360 A 65 Female 0 0 2 0 1 #> 361 xx361 A 57 Male 0 1 4 1 0 #> 362 xx362 A 55 Female 0 1 4 0 1 #> 363 xx363 A 57 Male 0 1 2 1 0 #> 364 xx364 A 46 Female 0 1 4 0 1 #> 365 xx365 A 69 Female 0 1 1 0 1 #> 366 xx366 A 67 Female 0 1 3 0 1 #> 367 xx367 A 55 Male 0 0 1 1 0 #> 368 xx368 A 53 Female 0 1 3 0 1 #> 369 xx369 A 46 Female 0 0 3 0 1 #> 370 xx370 A 71 Male 0 0 4 1 0 #> 371 xx371 A 68 Male 0 1 1 1 0 #> 372 xx372 A 49 Female 0 1 3 0 1 #> 373 xx373 A 51 Female 0 0 3 0 1 #> 374 xx374 A 65 Female 1 1 3 0 1 #> 375 xx375 A 55 Female 0 0 4 0 1 #> 376 xx376 A 53 Male 0 0 4 1 0 #> 377 xx377 A 65 Female 0 1 4 0 1 #> 378 xx378 A 72 Female 1 1 4 0 1 #> 379 xx379 A 61 Male 0 1 1 1 0 #> 380 xx380 A 73 Female 0 0 1 0 1 #> 381 xx381 A 62 Female 1 1 2 0 1 #> 382 xx382 A 46 Male 0 0 2 1 0 #> 383 xx383 A 51 Male 0 1 4 1 0 #> 384 xx384 A 60 Male 1 1 4 1 0 #> 385 xx385 A 56 Female 0 1 3 0 1 #> 386 xx386 A 69 Female 0 1 1 0 1 #> 387 xx387 A 58 Female 1 1 2 0 1 #> 388 xx388 A 58 Female 1 1 3 0 1 #> 389 xx389 A 53 Female 0 1 2 0 1 #> 390 xx390 A 47 Female 0 0 2 0 1 #> 391 xx391 A 59 Male 0 1 2 1 0 #> 392 xx392 A 47 Female 0 0 4 0 1 #> 393 xx393 A 60 Female 1 0 4 0 1 #> 394 xx394 A 73 Female 0 0 4 0 1 #> 395 xx395 A 60 Male 0 0 1 1 0 #> 396 xx396 A 75 Male 0 0 4 1 0 #> 397 xx397 A 65 Female 0 0 3 0 1 #> 398 xx398 A 68 Male 0 0 1 1 0 #> 399 xx399 A 55 Female 0 0 4 0 1 #> 400 xx400 A 46 Female 1 0 4 0 1 #> 401 xx401 A 45 Female 0 1 1 0 1 #> 402 xx402 A 70 Male 1 1 3 1 0 #> 403 xx403 A 56 Female 0 1 4 0 1 #> 404 xx404 A 62 Female 0 0 3 0 1 #> 405 xx405 A 49 Male 0 0 4 1 0 #> 406 xx406 A 52 Female 0 0 4 0 1 #> 407 xx407 A 67 Female 0 1 4 0 1 #> 408 xx408 A 50 Female 1 1 1 0 1 #> 409 xx409 A 68 Female 1 1 2 0 1 #> 410 xx410 A 54 Female 0 0 4 0 1 #> 411 xx411 A 65 Male 0 0 4 1 0 #> 412 xx412 A 55 Female 0 1 2 0 1 #> 413 xx413 A 53 Female 1 1 4 0 1 #> 414 xx414 A 71 Female 0 0 2 0 1 #> 415 xx415 A 48 Male 0 0 1 1 0 #> 416 xx416 A 54 Female 0 1 3 0 1 #> 417 xx417 A 75 Male 1 0 3 1 0 #> 418 xx418 A 53 Female 0 0 2 0 1 #> 419 xx419 A 50 Female 1 0 4 0 1 #> 420 xx420 A 64 Female 1 0 1 0 1 #> 421 xx421 A 65 Female 0 0 2 0 1 #> 422 xx422 A 65 Female 0 1 4 0 1 #> 423 xx423 A 60 Female 1 0 2 0 1 #> 424 xx424 A 70 Female 1 0 3 0 1 #> 425 xx425 A 51 Female 0 0 2 0 1 #> 426 xx426 A 45 Female 0 1 1 0 1 #> 427 xx427 A 75 Female 1 0 2 0 1 #> 428 xx428 A 52 Female 1 0 1 0 1 #> 429 xx429 A 70 Male 0 0 4 1 0 #> 430 xx430 A 69 Female 1 1 3 0 1 #> 431 xx431 A 64 Female 0 0 2 0 1 #> 432 xx432 A 68 Female 1 0 1 0 1 #> 433 xx433 A 51 Male 1 0 1 1 0 #> 434 xx434 A 59 Female 0 1 2 0 1 #> 435 xx435 A 57 Female 0 0 1 0 1 #> 436 xx436 A 47 Male 0 0 2 1 0 #> 437 xx437 A 65 Male 0 1 1 1 0 #> 438 xx438 A 65 Male 0 1 1 1 0 #> 439 xx439 A 65 Male 0 0 2 1 0 #> 440 xx440 A 46 Male 0 0 2 1 0 #> 441 xx441 A 64 Female 0 0 3 0 1 #> 442 xx442 A 57 Female 0 1 4 0 1 #> 443 xx443 A 67 Female 0 1 3 0 1 #> 444 xx444 A 61 Female 1 0 3 0 1 #> 445 xx445 A 56 Male 0 0 4 1 0 #> 446 xx446 A 52 Male 0 0 3 1 0 #> 447 xx447 A 74 Female 1 1 3 0 1 #> 448 xx448 A 75 Male 0 1 3 1 0 #> 449 xx449 A 58 Male 0 1 3 1 0 #> 450 xx450 A 57 Female 0 1 4 0 1 #> 451 xx451 A 55 Female 0 0 1 0 1 #> 452 xx452 A 53 Female 1 0 1 0 1 #> 453 xx453 A 75 Male 1 0 4 1 0 #> 454 xx454 A 65 Female 0 1 3 0 1 #> 455 xx455 A 65 Female 0 0 4 0 1 #> 456 xx456 A 58 Male 0 0 4 1 0 #> 457 xx457 A 71 Female 0 0 3 0 1 #> 458 xx458 A 71 Male 0 0 4 1 0 #> 459 xx459 A 59 Male 1 0 4 1 0 #> 460 xx460 A 46 Male 1 0 4 1 0 #> 461 xx461 A 51 Female 0 0 4 0 1 #> 462 xx462 A 56 Female 0 1 4 0 1 #> 463 xx463 A 66 Female 0 0 1 0 1 #> 464 xx464 A 59 Female 1 0 2 0 1 #> 465 xx465 A 48 Female 1 0 2 0 1 #> 466 xx466 A 68 Female 0 0 4 0 1 #> 467 xx467 A 57 Female 0 0 4 0 1 #> 468 xx468 A 63 Male 0 0 4 1 0 #> 469 xx469 A 62 Male 0 1 3 1 0 #> 470 xx470 A 70 Female 0 0 3 0 1 #> 471 xx471 A 55 Female 0 1 2 0 1 #> 472 xx472 A 56 Male 1 0 3 1 0 #> 473 xx473 A 51 Male 1 0 1 1 0 #> 474 xx474 A 51 Female 0 0 4 0 1 #> 475 xx475 A 46 Female 1 1 3 0 1 #> 476 xx476 A 52 Male 0 0 4 1 0 #> 477 xx477 A 71 Male 0 1 4 1 0 #> 478 xx478 A 54 Male 1 0 3 1 0 #> 479 xx479 A 55 Male 0 1 2 1 0 #> 480 xx480 A 46 Female 0 0 3 0 1 #> 481 xx481 A 70 Female 0 0 1 0 1 #> 482 xx482 A 68 Female 0 1 3 0 1 #> 483 xx483 A 50 Male 0 1 1 1 0 #> 484 xx484 A 45 Female 0 0 2 0 1 #> 485 xx485 A 68 Male 0 0 1 1 0 #> 486 xx486 A 56 Male 0 1 2 1 0 #> 487 xx487 A 59 Male 0 1 3 1 0 #> 488 xx488 A 51 Male 1 1 1 1 0 #> 489 xx489 A 61 Female 1 0 3 0 1 #> 490 xx490 A 60 Female 0 1 3 0 1 #> 491 xx491 A 68 Female 1 0 2 0 1 #> 492 xx492 A 67 Male 1 1 4 1 0 #> 493 xx493 A 45 Male 1 1 2 1 0 #> 494 xx494 A 71 Female 0 1 2 0 1 #> 495 xx495 A 55 Male 0 1 3 1 0 #> 496 xx496 A 72 Female 0 0 4 0 1 #> 497 xx497 A 48 Female 1 0 3 0 1 #> 498 xx498 A 68 Female 1 0 1 0 1 #> 499 xx499 A 45 Female 0 0 1 0 1 #> 500 xx500 A 58 Female 1 1 3 0 1 #> 4361 yy1 C 47 Male 0 0 2 1 0 #> 771 yy10 C 60 Male 0 0 4 1 0 #> 2611 yy100 C 75 Female 1 0 1 0 1 #> 4100 yy101 C 48 Female 0 1 4 0 1 #> 1091 yy102 C 57 Female 0 0 4 0 1 #> 1310 yy103 C 57 Male 0 1 3 1 0 #> 2201 yy104 C 54 Female 0 0 1 0 1 #> 2461 yy105 C 54 Female 1 0 3 0 1 #> 2721 yy106 C 55 Male 0 1 3 1 0 #> 1801 yy107 C 58 Female 0 0 3 0 1 #> 3910 yy108 C 54 Male 0 0 1 1 0 #> 4961 yy109 C 72 Female 0 0 4 0 1 #> 4041 yy11 C 62 Female 0 0 3 0 1 #> 4761 yy110 C 52 Male 0 0 4 1 0 #> 610 yy111 C 48 Female 0 1 4 0 1 #> 4921 yy112 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(\\n).","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ess_footnote_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Note on Expected Sample Size Reduction — ess_footnote_text","text":"character string","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/estimate_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive individual weights in the matching step of MAIC — estimate_weights","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"Assuming data properly processed, function takes individual patient data (IPD) centered covariates (effect modifiers /prognostic variables) input, generates weights individual IPD trial match covariates aggregate data. plot function displays individuals weights key summary top right legend includes median weight, effective sample size (ESS), reduction percentage (percent ESS reduced original sample size). two options plotting: base R plot ggplot. default base R plot plot unscaled scaled separately. default ggplot plot unscaled scaled weights plot.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/estimate_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"","code":"estimate_weights( data, centered_colnames = NULL, start_val = 0, method = \"BFGS\", n_boot_iteration = NULL, set_seed_boot = 1234, boot_strata = \"ARM\", ... ) # S3 method for class 'maicplus_estimate_weights' plot( x, ggplot = FALSE, bin_col = \"#6ECEB2\", vline_col = \"#688CE8\", main_title = NULL, scaled_weights = TRUE, bins = 50, ... )"},{"path":"https://hta-pharma.github.io/maicplus/reference/estimate_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"data numeric matrix, centered covariates IPD, missing value cell allowed centered_colnames character numeric vector (column indicators) centered covariates start_val scalar, starting value coefficients propensity score regression method string, name optimization algorithm (see 'method' argument base::optim()) default \"BFGS\", options \"Nelder-Mead\", \"CG\", \"L-BFGS-B\", \"SANN\", \"Brent\" n_boot_iteration integer, number bootstrap iterations. default NULL means bootstrapping procedure triggered, hence element \"boot\" output list object NULL. set_seed_boot scalar, random seed conducting bootstrapping, relevant n_boot_iteration NULL. default, use seed 1234 boot_strata character vector column names data defines strata bootstrapping. ensures samples drawn proportionally defined stratum. NULL, stratification bootstrapping process. default, \"ARM\" ... Additional control parameters passed stats::optim. x object estimate_weights ggplot indicator print base weights plot ggplot weights plot bin_col string, color bins histogram vline_col string, color vertical line histogram main_title title plot. ggplot, name scaled weights plot unscaled weights plot, respectively. scaled_weights (base plot ) indicator using scaled weights instead regular weights bins (ggplot ) number bin parameter use","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/estimate_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"list following 4 elements, data data.frame, includes input data appended column 'weights' 'scaled_weights'. Scaled weights summation number rows data missing value effect modifiers centered_colnames column names centered effect modifiers data nr_missing number rows data least 1 missing value specified centered effect modifiers ess effective sample size, square sum divided sum squares opt R object returned base::optim(), assess convergence details boot_strata 'strata' boot::boot object boot_seed column names data stratification factors boot n 2 k array NA, n equals number rows data, k equals n_boot_iteration. 2 columns second dimension include column numeric indexes rows data selected bootstrapping iteration column weights. boot NA argument n_boot_iteration set NULL","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/estimate_weights.html","id":"methods-by-generic-","dir":"Reference","previous_headings":"","what":"Methods (by generic)","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"plot(maicplus_estimate_weights): Plot method estimate_weights objects","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/estimate_weights.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"","code":"data(centered_ipd_sat) centered_colnames <- grep(\"_CENTERED\", colnames(centered_ipd_sat), value = TRUE) weighted_data <- estimate_weights(data = centered_ipd_sat, centered_colnames = centered_colnames) # \\donttest{ # To later estimate bootstrap confidence intervals, we calculate the weights # for the bootstrap samples: weighted_data_boot <- estimate_weights( data = centered_ipd_sat, centered_colnames = centered_colnames, n_boot_iteration = 100 ) # } plot(weighted_sat) if (requireNamespace(\"ggplot2\")) { plot(weighted_sat, ggplot = TRUE) }"},{"path":"https://hta-pharma.github.io/maicplus/reference/ext_tte_transfer.html","id":null,"dir":"Reference","previous_headings":"","what":"helper function: transform TTE ADaM data to suitable input for survival R package — ext_tte_transfer","title":"helper function: transform TTE ADaM data to suitable input for survival R package — ext_tte_transfer","text":"helper function: transform TTE ADaM data suitable input survival R package","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ext_tte_transfer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"helper function: transform TTE ADaM data to suitable input for survival R package — ext_tte_transfer","text":"","code":"ext_tte_transfer(dd, time_scale = \"months\", trt = NULL)"},{"path":"https://hta-pharma.github.io/maicplus/reference/ext_tte_transfer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"helper function: transform TTE ADaM data to suitable input for survival R package — ext_tte_transfer","text":"dd data frame, ADTTE read via haven::read_sas time_scale character string, 'years', 'months', 'weeks' 'days', time unit median survival time trt values include treatment column","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ext_tte_transfer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"helper function: transform TTE ADaM data to suitable input for survival R package — ext_tte_transfer","text":"data frame can used input survival::Surv","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/find_SE_from_CI.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","title":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","text":"Comparator studies often report confidence interval treatment effects. function calculates standard error treatment effect given reported confidence interval. relative treatment effect (.e. hazard ratio, odds ratio, risk ratio), function log confidence interval. risk difference mean difference, log confidence interval. option log confidence interval controlled 'logged' parameter.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/find_SE_from_CI.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","text":"","code":"find_SE_from_CI( CI_lower = NULL, CI_upper = NULL, CI_perc = 0.95, logged = TRUE )"},{"path":"https://hta-pharma.github.io/maicplus/reference/find_SE_from_CI.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","text":"CI_lower Reported lower percentile value treatment effect CI_upper Reported upper percentile value treatment effect CI_perc Percentage confidence interval reported logged Whether confidence interval logged. relative treatment effect, log applied estimated log treatment effect approximately normally distributed.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/find_SE_from_CI.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","text":"Standard error log relative treatment effect 'logged' true standard error treatment effect 'logged' false","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/find_SE_from_CI.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","text":"","code":"find_SE_from_CI(CI_lower = 0.55, CI_upper = 0.90, CI_perc = 0.95) #> [1] 0.1256341"},{"path":"https://hta-pharma.github.io/maicplus/reference/get_pseudo_ipd_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","title":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","text":"Create pseudo IPD given aggregated binary data","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/get_pseudo_ipd_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","text":"","code":"get_pseudo_ipd_binary(binary_agd, format = c(\"stacked\", \"unstacked\"))"},{"path":"https://hta-pharma.github.io/maicplus/reference/get_pseudo_ipd_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","text":"binary_agd data.frame take different formats depending format format string, \"stacked\" \"unstacked\"","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/get_pseudo_ipd_binary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","text":"data.frame pseudo binary IPD, columns USUBJID, ARM, RESPONSE","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/get_pseudo_ipd_binary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","text":"","code":"# example of unstacked testdat <- data.frame(Yes = 280, No = 120) rownames(testdat) <- \"B\" get_pseudo_ipd_binary( binary_agd = testdat, format = \"unstacked\" ) #> USUBJID ARM RESPONSE #> 1 pseudo_binary_subj_1 B TRUE #> 2 pseudo_binary_subj_2 B TRUE #> 3 pseudo_binary_subj_3 B TRUE #> 4 pseudo_binary_subj_4 B TRUE #> 5 pseudo_binary_subj_5 B TRUE #> 6 pseudo_binary_subj_6 B TRUE #> 7 pseudo_binary_subj_7 B TRUE #> 8 pseudo_binary_subj_8 B TRUE #> 9 pseudo_binary_subj_9 B TRUE #> 10 pseudo_binary_subj_10 B TRUE #> 11 pseudo_binary_subj_11 B TRUE #> 12 pseudo_binary_subj_12 B TRUE #> 13 pseudo_binary_subj_13 B TRUE #> 14 pseudo_binary_subj_14 B TRUE #> 15 pseudo_binary_subj_15 B TRUE #> 16 pseudo_binary_subj_16 B TRUE #> 17 pseudo_binary_subj_17 B TRUE #> 18 pseudo_binary_subj_18 B TRUE #> 19 pseudo_binary_subj_19 B TRUE #> 20 pseudo_binary_subj_20 B TRUE #> 21 pseudo_binary_subj_21 B TRUE #> 22 pseudo_binary_subj_22 B TRUE #> 23 pseudo_binary_subj_23 B TRUE #> 24 pseudo_binary_subj_24 B TRUE #> 25 pseudo_binary_subj_25 B TRUE #> 26 pseudo_binary_subj_26 B TRUE #> 27 pseudo_binary_subj_27 B TRUE #> 28 pseudo_binary_subj_28 B TRUE #> 29 pseudo_binary_subj_29 B TRUE #> 30 pseudo_binary_subj_30 B TRUE #> 31 pseudo_binary_subj_31 B TRUE #> 32 pseudo_binary_subj_32 B TRUE #> 33 pseudo_binary_subj_33 B TRUE #> 34 pseudo_binary_subj_34 B TRUE #> 35 pseudo_binary_subj_35 B TRUE #> 36 pseudo_binary_subj_36 B TRUE #> 37 pseudo_binary_subj_37 B TRUE #> 38 pseudo_binary_subj_38 B TRUE #> 39 pseudo_binary_subj_39 B TRUE #> 40 pseudo_binary_subj_40 B TRUE #> 41 pseudo_binary_subj_41 B TRUE #> 42 pseudo_binary_subj_42 B TRUE #> 43 pseudo_binary_subj_43 B TRUE #> 44 pseudo_binary_subj_44 B TRUE #> 45 pseudo_binary_subj_45 B TRUE #> 46 pseudo_binary_subj_46 B TRUE #> 47 pseudo_binary_subj_47 B TRUE #> 48 pseudo_binary_subj_48 B TRUE #> 49 pseudo_binary_subj_49 B TRUE #> 50 pseudo_binary_subj_50 B TRUE #> 51 pseudo_binary_subj_51 B TRUE #> 52 pseudo_binary_subj_52 B TRUE #> 53 pseudo_binary_subj_53 B TRUE #> 54 pseudo_binary_subj_54 B TRUE #> 55 pseudo_binary_subj_55 B TRUE #> 56 pseudo_binary_subj_56 B TRUE #> 57 pseudo_binary_subj_57 B TRUE #> 58 pseudo_binary_subj_58 B TRUE #> 59 pseudo_binary_subj_59 B TRUE #> 60 pseudo_binary_subj_60 B TRUE #> 61 pseudo_binary_subj_61 B TRUE #> 62 pseudo_binary_subj_62 B TRUE #> 63 pseudo_binary_subj_63 B TRUE #> 64 pseudo_binary_subj_64 B TRUE #> 65 pseudo_binary_subj_65 B TRUE #> 66 pseudo_binary_subj_66 B TRUE #> 67 pseudo_binary_subj_67 B TRUE #> 68 pseudo_binary_subj_68 B TRUE #> 69 pseudo_binary_subj_69 B TRUE #> 70 pseudo_binary_subj_70 B TRUE #> 71 pseudo_binary_subj_71 B TRUE #> 72 pseudo_binary_subj_72 B TRUE #> 73 pseudo_binary_subj_73 B TRUE #> 74 pseudo_binary_subj_74 B TRUE #> 75 pseudo_binary_subj_75 B TRUE #> 76 pseudo_binary_subj_76 B TRUE #> 77 pseudo_binary_subj_77 B TRUE #> 78 pseudo_binary_subj_78 B TRUE #> 79 pseudo_binary_subj_79 B TRUE #> 80 pseudo_binary_subj_80 B TRUE #> 81 pseudo_binary_subj_81 B TRUE #> 82 pseudo_binary_subj_82 B TRUE #> 83 pseudo_binary_subj_83 B TRUE #> 84 pseudo_binary_subj_84 B TRUE #> 85 pseudo_binary_subj_85 B TRUE #> 86 pseudo_binary_subj_86 B TRUE #> 87 pseudo_binary_subj_87 B TRUE #> 88 pseudo_binary_subj_88 B TRUE #> 89 pseudo_binary_subj_89 B TRUE #> 90 pseudo_binary_subj_90 B TRUE #> 91 pseudo_binary_subj_91 B TRUE #> 92 pseudo_binary_subj_92 B TRUE #> 93 pseudo_binary_subj_93 B TRUE #> 94 pseudo_binary_subj_94 B TRUE #> 95 pseudo_binary_subj_95 B TRUE #> 96 pseudo_binary_subj_96 B TRUE #> 97 pseudo_binary_subj_97 B TRUE #> 98 pseudo_binary_subj_98 B TRUE #> 99 pseudo_binary_subj_99 B TRUE #> 100 pseudo_binary_subj_100 B TRUE #> 101 pseudo_binary_subj_101 B TRUE #> 102 pseudo_binary_subj_102 B TRUE #> 103 pseudo_binary_subj_103 B TRUE #> 104 pseudo_binary_subj_104 B TRUE #> 105 pseudo_binary_subj_105 B TRUE #> 106 pseudo_binary_subj_106 B TRUE #> 107 pseudo_binary_subj_107 B TRUE #> 108 pseudo_binary_subj_108 B TRUE #> 109 pseudo_binary_subj_109 B TRUE #> 110 pseudo_binary_subj_110 B TRUE #> 111 pseudo_binary_subj_111 B TRUE #> 112 pseudo_binary_subj_112 B TRUE #> 113 pseudo_binary_subj_113 B TRUE #> 114 pseudo_binary_subj_114 B TRUE #> 115 pseudo_binary_subj_115 B TRUE #> 116 pseudo_binary_subj_116 B TRUE #> 117 pseudo_binary_subj_117 B TRUE #> 118 pseudo_binary_subj_118 B TRUE #> 119 pseudo_binary_subj_119 B TRUE #> 120 pseudo_binary_subj_120 B TRUE #> 121 pseudo_binary_subj_121 B TRUE #> 122 pseudo_binary_subj_122 B TRUE #> 123 pseudo_binary_subj_123 B TRUE #> 124 pseudo_binary_subj_124 B TRUE #> 125 pseudo_binary_subj_125 B TRUE #> 126 pseudo_binary_subj_126 B TRUE #> 127 pseudo_binary_subj_127 B TRUE #> 128 pseudo_binary_subj_128 B TRUE #> 129 pseudo_binary_subj_129 B TRUE #> 130 pseudo_binary_subj_130 B TRUE #> 131 pseudo_binary_subj_131 B TRUE #> 132 pseudo_binary_subj_132 B TRUE #> 133 pseudo_binary_subj_133 B TRUE #> 134 pseudo_binary_subj_134 B TRUE #> 135 pseudo_binary_subj_135 B TRUE #> 136 pseudo_binary_subj_136 B TRUE #> 137 pseudo_binary_subj_137 B TRUE #> 138 pseudo_binary_subj_138 B TRUE #> 139 pseudo_binary_subj_139 B TRUE #> 140 pseudo_binary_subj_140 B TRUE #> 141 pseudo_binary_subj_141 B TRUE #> 142 pseudo_binary_subj_142 B TRUE #> 143 pseudo_binary_subj_143 B TRUE #> 144 pseudo_binary_subj_144 B TRUE #> 145 pseudo_binary_subj_145 B TRUE #> 146 pseudo_binary_subj_146 B TRUE #> 147 pseudo_binary_subj_147 B TRUE #> 148 pseudo_binary_subj_148 B TRUE #> 149 pseudo_binary_subj_149 B TRUE #> 150 pseudo_binary_subj_150 B TRUE #> 151 pseudo_binary_subj_151 B TRUE #> 152 pseudo_binary_subj_152 B TRUE #> 153 pseudo_binary_subj_153 B TRUE #> 154 pseudo_binary_subj_154 B TRUE #> 155 pseudo_binary_subj_155 B TRUE #> 156 pseudo_binary_subj_156 B TRUE #> 157 pseudo_binary_subj_157 B TRUE #> 158 pseudo_binary_subj_158 B TRUE #> 159 pseudo_binary_subj_159 B TRUE #> 160 pseudo_binary_subj_160 B TRUE #> 161 pseudo_binary_subj_161 B TRUE #> 162 pseudo_binary_subj_162 B TRUE #> 163 pseudo_binary_subj_163 B TRUE #> 164 pseudo_binary_subj_164 B TRUE #> 165 pseudo_binary_subj_165 B TRUE #> 166 pseudo_binary_subj_166 B TRUE #> 167 pseudo_binary_subj_167 B TRUE #> 168 pseudo_binary_subj_168 B TRUE #> 169 pseudo_binary_subj_169 B TRUE #> 170 pseudo_binary_subj_170 B TRUE #> 171 pseudo_binary_subj_171 B TRUE #> 172 pseudo_binary_subj_172 B TRUE #> 173 pseudo_binary_subj_173 B TRUE #> 174 pseudo_binary_subj_174 B TRUE #> 175 pseudo_binary_subj_175 B TRUE #> 176 pseudo_binary_subj_176 B TRUE #> 177 pseudo_binary_subj_177 B TRUE #> 178 pseudo_binary_subj_178 B TRUE #> 179 pseudo_binary_subj_179 B TRUE #> 180 pseudo_binary_subj_180 B TRUE #> 181 pseudo_binary_subj_181 B TRUE #> 182 pseudo_binary_subj_182 B TRUE #> 183 pseudo_binary_subj_183 B TRUE #> 184 pseudo_binary_subj_184 B TRUE #> 185 pseudo_binary_subj_185 B TRUE #> 186 pseudo_binary_subj_186 B TRUE #> 187 pseudo_binary_subj_187 B TRUE #> 188 pseudo_binary_subj_188 B TRUE #> 189 pseudo_binary_subj_189 B TRUE #> 190 pseudo_binary_subj_190 B TRUE #> 191 pseudo_binary_subj_191 B TRUE #> 192 pseudo_binary_subj_192 B TRUE #> 193 pseudo_binary_subj_193 B TRUE #> 194 pseudo_binary_subj_194 B TRUE #> 195 pseudo_binary_subj_195 B TRUE #> 196 pseudo_binary_subj_196 B TRUE #> 197 pseudo_binary_subj_197 B TRUE #> 198 pseudo_binary_subj_198 B TRUE #> 199 pseudo_binary_subj_199 B TRUE #> 200 pseudo_binary_subj_200 B TRUE #> 201 pseudo_binary_subj_201 B TRUE #> 202 pseudo_binary_subj_202 B TRUE #> 203 pseudo_binary_subj_203 B TRUE #> 204 pseudo_binary_subj_204 B TRUE #> 205 pseudo_binary_subj_205 B TRUE #> 206 pseudo_binary_subj_206 B TRUE #> 207 pseudo_binary_subj_207 B TRUE #> 208 pseudo_binary_subj_208 B TRUE #> 209 pseudo_binary_subj_209 B TRUE #> 210 pseudo_binary_subj_210 B TRUE #> 211 pseudo_binary_subj_211 B TRUE #> 212 pseudo_binary_subj_212 B TRUE #> 213 pseudo_binary_subj_213 B TRUE #> 214 pseudo_binary_subj_214 B TRUE #> 215 pseudo_binary_subj_215 B TRUE #> 216 pseudo_binary_subj_216 B TRUE #> 217 pseudo_binary_subj_217 B TRUE #> 218 pseudo_binary_subj_218 B TRUE #> 219 pseudo_binary_subj_219 B TRUE #> 220 pseudo_binary_subj_220 B TRUE #> 221 pseudo_binary_subj_221 B TRUE #> 222 pseudo_binary_subj_222 B TRUE #> 223 pseudo_binary_subj_223 B TRUE #> 224 pseudo_binary_subj_224 B TRUE #> 225 pseudo_binary_subj_225 B TRUE #> 226 pseudo_binary_subj_226 B TRUE #> 227 pseudo_binary_subj_227 B TRUE #> 228 pseudo_binary_subj_228 B TRUE #> 229 pseudo_binary_subj_229 B TRUE #> 230 pseudo_binary_subj_230 B TRUE #> 231 pseudo_binary_subj_231 B TRUE #> 232 pseudo_binary_subj_232 B TRUE #> 233 pseudo_binary_subj_233 B TRUE #> 234 pseudo_binary_subj_234 B TRUE #> 235 pseudo_binary_subj_235 B TRUE #> 236 pseudo_binary_subj_236 B TRUE #> 237 pseudo_binary_subj_237 B TRUE #> 238 pseudo_binary_subj_238 B TRUE #> 239 pseudo_binary_subj_239 B TRUE #> 240 pseudo_binary_subj_240 B TRUE #> 241 pseudo_binary_subj_241 B TRUE #> 242 pseudo_binary_subj_242 B TRUE #> 243 pseudo_binary_subj_243 B TRUE #> 244 pseudo_binary_subj_244 B TRUE #> 245 pseudo_binary_subj_245 B TRUE #> 246 pseudo_binary_subj_246 B TRUE #> 247 pseudo_binary_subj_247 B TRUE #> 248 pseudo_binary_subj_248 B TRUE #> 249 pseudo_binary_subj_249 B TRUE #> 250 pseudo_binary_subj_250 B TRUE #> 251 pseudo_binary_subj_251 B TRUE #> 252 pseudo_binary_subj_252 B TRUE #> 253 pseudo_binary_subj_253 B TRUE #> 254 pseudo_binary_subj_254 B TRUE #> 255 pseudo_binary_subj_255 B TRUE #> 256 pseudo_binary_subj_256 B TRUE #> 257 pseudo_binary_subj_257 B TRUE #> 258 pseudo_binary_subj_258 B TRUE #> 259 pseudo_binary_subj_259 B TRUE #> 260 pseudo_binary_subj_260 B TRUE #> 261 pseudo_binary_subj_261 B TRUE #> 262 pseudo_binary_subj_262 B TRUE #> 263 pseudo_binary_subj_263 B TRUE #> 264 pseudo_binary_subj_264 B TRUE #> 265 pseudo_binary_subj_265 B TRUE #> 266 pseudo_binary_subj_266 B TRUE #> 267 pseudo_binary_subj_267 B TRUE #> 268 pseudo_binary_subj_268 B TRUE #> 269 pseudo_binary_subj_269 B TRUE #> 270 pseudo_binary_subj_270 B TRUE #> 271 pseudo_binary_subj_271 B TRUE #> 272 pseudo_binary_subj_272 B TRUE #> 273 pseudo_binary_subj_273 B TRUE #> 274 pseudo_binary_subj_274 B TRUE #> 275 pseudo_binary_subj_275 B TRUE #> 276 pseudo_binary_subj_276 B TRUE #> 277 pseudo_binary_subj_277 B TRUE #> 278 pseudo_binary_subj_278 B TRUE #> 279 pseudo_binary_subj_279 B TRUE #> 280 pseudo_binary_subj_280 B TRUE #> 281 pseudo_binary_subj_281 B FALSE #> 282 pseudo_binary_subj_282 B FALSE #> 283 pseudo_binary_subj_283 B FALSE #> 284 pseudo_binary_subj_284 B FALSE #> 285 pseudo_binary_subj_285 B FALSE #> 286 pseudo_binary_subj_286 B FALSE #> 287 pseudo_binary_subj_287 B FALSE #> 288 pseudo_binary_subj_288 B FALSE #> 289 pseudo_binary_subj_289 B FALSE #> 290 pseudo_binary_subj_290 B FALSE #> 291 pseudo_binary_subj_291 B FALSE #> 292 pseudo_binary_subj_292 B FALSE #> 293 pseudo_binary_subj_293 B FALSE #> 294 pseudo_binary_subj_294 B FALSE #> 295 pseudo_binary_subj_295 B FALSE #> 296 pseudo_binary_subj_296 B FALSE #> 297 pseudo_binary_subj_297 B FALSE #> 298 pseudo_binary_subj_298 B FALSE #> 299 pseudo_binary_subj_299 B FALSE #> 300 pseudo_binary_subj_300 B FALSE #> 301 pseudo_binary_subj_301 B FALSE #> 302 pseudo_binary_subj_302 B FALSE #> 303 pseudo_binary_subj_303 B FALSE #> 304 pseudo_binary_subj_304 B FALSE #> 305 pseudo_binary_subj_305 B FALSE #> 306 pseudo_binary_subj_306 B FALSE #> 307 pseudo_binary_subj_307 B FALSE #> 308 pseudo_binary_subj_308 B FALSE #> 309 pseudo_binary_subj_309 B FALSE #> 310 pseudo_binary_subj_310 B FALSE #> 311 pseudo_binary_subj_311 B FALSE #> 312 pseudo_binary_subj_312 B FALSE #> 313 pseudo_binary_subj_313 B FALSE #> 314 pseudo_binary_subj_314 B FALSE #> 315 pseudo_binary_subj_315 B FALSE #> 316 pseudo_binary_subj_316 B FALSE #> 317 pseudo_binary_subj_317 B FALSE #> 318 pseudo_binary_subj_318 B FALSE #> 319 pseudo_binary_subj_319 B FALSE #> 320 pseudo_binary_subj_320 B FALSE #> 321 pseudo_binary_subj_321 B FALSE #> 322 pseudo_binary_subj_322 B FALSE #> 323 pseudo_binary_subj_323 B FALSE #> 324 pseudo_binary_subj_324 B FALSE #> 325 pseudo_binary_subj_325 B FALSE #> 326 pseudo_binary_subj_326 B FALSE #> 327 pseudo_binary_subj_327 B FALSE #> 328 pseudo_binary_subj_328 B FALSE #> 329 pseudo_binary_subj_329 B FALSE #> 330 pseudo_binary_subj_330 B FALSE #> 331 pseudo_binary_subj_331 B FALSE #> 332 pseudo_binary_subj_332 B FALSE #> 333 pseudo_binary_subj_333 B FALSE #> 334 pseudo_binary_subj_334 B FALSE #> 335 pseudo_binary_subj_335 B FALSE #> 336 pseudo_binary_subj_336 B FALSE #> 337 pseudo_binary_subj_337 B FALSE #> 338 pseudo_binary_subj_338 B FALSE #> 339 pseudo_binary_subj_339 B FALSE #> 340 pseudo_binary_subj_340 B FALSE #> 341 pseudo_binary_subj_341 B FALSE #> 342 pseudo_binary_subj_342 B FALSE #> 343 pseudo_binary_subj_343 B FALSE #> 344 pseudo_binary_subj_344 B FALSE #> 345 pseudo_binary_subj_345 B FALSE #> 346 pseudo_binary_subj_346 B FALSE #> 347 pseudo_binary_subj_347 B FALSE #> 348 pseudo_binary_subj_348 B FALSE #> 349 pseudo_binary_subj_349 B FALSE #> 350 pseudo_binary_subj_350 B FALSE #> 351 pseudo_binary_subj_351 B FALSE #> 352 pseudo_binary_subj_352 B FALSE #> 353 pseudo_binary_subj_353 B FALSE #> 354 pseudo_binary_subj_354 B FALSE #> 355 pseudo_binary_subj_355 B FALSE #> 356 pseudo_binary_subj_356 B FALSE #> 357 pseudo_binary_subj_357 B FALSE #> 358 pseudo_binary_subj_358 B FALSE #> 359 pseudo_binary_subj_359 B FALSE #> 360 pseudo_binary_subj_360 B FALSE #> 361 pseudo_binary_subj_361 B FALSE #> 362 pseudo_binary_subj_362 B FALSE #> 363 pseudo_binary_subj_363 B FALSE #> 364 pseudo_binary_subj_364 B FALSE #> 365 pseudo_binary_subj_365 B FALSE #> 366 pseudo_binary_subj_366 B FALSE #> 367 pseudo_binary_subj_367 B FALSE #> 368 pseudo_binary_subj_368 B FALSE #> 369 pseudo_binary_subj_369 B FALSE #> 370 pseudo_binary_subj_370 B FALSE #> 371 pseudo_binary_subj_371 B FALSE #> 372 pseudo_binary_subj_372 B FALSE #> 373 pseudo_binary_subj_373 B FALSE #> 374 pseudo_binary_subj_374 B FALSE #> 375 pseudo_binary_subj_375 B FALSE #> 376 pseudo_binary_subj_376 B FALSE #> 377 pseudo_binary_subj_377 B FALSE #> 378 pseudo_binary_subj_378 B FALSE #> 379 pseudo_binary_subj_379 B FALSE #> 380 pseudo_binary_subj_380 B FALSE #> 381 pseudo_binary_subj_381 B FALSE #> 382 pseudo_binary_subj_382 B FALSE #> 383 pseudo_binary_subj_383 B FALSE #> 384 pseudo_binary_subj_384 B FALSE #> 385 pseudo_binary_subj_385 B FALSE #> 386 pseudo_binary_subj_386 B FALSE #> 387 pseudo_binary_subj_387 B FALSE #> 388 pseudo_binary_subj_388 B FALSE #> 389 pseudo_binary_subj_389 B FALSE #> 390 pseudo_binary_subj_390 B FALSE #> 391 pseudo_binary_subj_391 B FALSE #> 392 pseudo_binary_subj_392 B FALSE #> 393 pseudo_binary_subj_393 B FALSE #> 394 pseudo_binary_subj_394 B FALSE #> 395 pseudo_binary_subj_395 B FALSE #> 396 pseudo_binary_subj_396 B FALSE #> 397 pseudo_binary_subj_397 B FALSE #> 398 pseudo_binary_subj_398 B FALSE #> 399 pseudo_binary_subj_399 B FALSE #> 400 pseudo_binary_subj_400 B FALSE # example of stacked get_pseudo_ipd_binary( binary_agd = data.frame( ARM = rep(\"B\", 2), RESPONSE = c(\"YES\", \"NO\"), COUNT = c(280, 120) ), format = \"stacked\" ) #> USUBJID ARM RESPONSE #> 1 pseudo_binary_subj_1 B TRUE #> 2 pseudo_binary_subj_2 B TRUE #> 3 pseudo_binary_subj_3 B TRUE #> 4 pseudo_binary_subj_4 B TRUE #> 5 pseudo_binary_subj_5 B TRUE #> 6 pseudo_binary_subj_6 B TRUE #> 7 pseudo_binary_subj_7 B TRUE #> 8 pseudo_binary_subj_8 B TRUE #> 9 pseudo_binary_subj_9 B TRUE #> 10 pseudo_binary_subj_10 B TRUE #> 11 pseudo_binary_subj_11 B TRUE #> 12 pseudo_binary_subj_12 B TRUE #> 13 pseudo_binary_subj_13 B TRUE #> 14 pseudo_binary_subj_14 B TRUE #> 15 pseudo_binary_subj_15 B TRUE #> 16 pseudo_binary_subj_16 B TRUE #> 17 pseudo_binary_subj_17 B TRUE #> 18 pseudo_binary_subj_18 B TRUE #> 19 pseudo_binary_subj_19 B TRUE #> 20 pseudo_binary_subj_20 B TRUE #> 21 pseudo_binary_subj_21 B TRUE #> 22 pseudo_binary_subj_22 B TRUE #> 23 pseudo_binary_subj_23 B TRUE #> 24 pseudo_binary_subj_24 B TRUE #> 25 pseudo_binary_subj_25 B TRUE #> 26 pseudo_binary_subj_26 B TRUE #> 27 pseudo_binary_subj_27 B TRUE #> 28 pseudo_binary_subj_28 B TRUE #> 29 pseudo_binary_subj_29 B TRUE #> 30 pseudo_binary_subj_30 B TRUE #> 31 pseudo_binary_subj_31 B TRUE #> 32 pseudo_binary_subj_32 B TRUE #> 33 pseudo_binary_subj_33 B TRUE #> 34 pseudo_binary_subj_34 B TRUE #> 35 pseudo_binary_subj_35 B TRUE #> 36 pseudo_binary_subj_36 B TRUE #> 37 pseudo_binary_subj_37 B TRUE #> 38 pseudo_binary_subj_38 B TRUE #> 39 pseudo_binary_subj_39 B TRUE #> 40 pseudo_binary_subj_40 B TRUE #> 41 pseudo_binary_subj_41 B TRUE #> 42 pseudo_binary_subj_42 B TRUE #> 43 pseudo_binary_subj_43 B TRUE #> 44 pseudo_binary_subj_44 B TRUE #> 45 pseudo_binary_subj_45 B TRUE #> 46 pseudo_binary_subj_46 B TRUE #> 47 pseudo_binary_subj_47 B TRUE #> 48 pseudo_binary_subj_48 B TRUE #> 49 pseudo_binary_subj_49 B TRUE #> 50 pseudo_binary_subj_50 B TRUE #> 51 pseudo_binary_subj_51 B TRUE #> 52 pseudo_binary_subj_52 B TRUE #> 53 pseudo_binary_subj_53 B TRUE #> 54 pseudo_binary_subj_54 B TRUE #> 55 pseudo_binary_subj_55 B TRUE #> 56 pseudo_binary_subj_56 B TRUE #> 57 pseudo_binary_subj_57 B TRUE #> 58 pseudo_binary_subj_58 B TRUE #> 59 pseudo_binary_subj_59 B TRUE #> 60 pseudo_binary_subj_60 B TRUE #> 61 pseudo_binary_subj_61 B TRUE #> 62 pseudo_binary_subj_62 B TRUE #> 63 pseudo_binary_subj_63 B TRUE #> 64 pseudo_binary_subj_64 B TRUE #> 65 pseudo_binary_subj_65 B TRUE #> 66 pseudo_binary_subj_66 B TRUE #> 67 pseudo_binary_subj_67 B TRUE #> 68 pseudo_binary_subj_68 B TRUE #> 69 pseudo_binary_subj_69 B TRUE #> 70 pseudo_binary_subj_70 B TRUE #> 71 pseudo_binary_subj_71 B TRUE #> 72 pseudo_binary_subj_72 B TRUE #> 73 pseudo_binary_subj_73 B TRUE #> 74 pseudo_binary_subj_74 B TRUE #> 75 pseudo_binary_subj_75 B TRUE #> 76 pseudo_binary_subj_76 B TRUE #> 77 pseudo_binary_subj_77 B TRUE #> 78 pseudo_binary_subj_78 B TRUE #> 79 pseudo_binary_subj_79 B TRUE #> 80 pseudo_binary_subj_80 B TRUE #> 81 pseudo_binary_subj_81 B TRUE #> 82 pseudo_binary_subj_82 B TRUE #> 83 pseudo_binary_subj_83 B TRUE #> 84 pseudo_binary_subj_84 B TRUE #> 85 pseudo_binary_subj_85 B TRUE #> 86 pseudo_binary_subj_86 B TRUE #> 87 pseudo_binary_subj_87 B TRUE #> 88 pseudo_binary_subj_88 B TRUE #> 89 pseudo_binary_subj_89 B TRUE #> 90 pseudo_binary_subj_90 B TRUE #> 91 pseudo_binary_subj_91 B TRUE #> 92 pseudo_binary_subj_92 B TRUE #> 93 pseudo_binary_subj_93 B TRUE #> 94 pseudo_binary_subj_94 B TRUE #> 95 pseudo_binary_subj_95 B TRUE #> 96 pseudo_binary_subj_96 B TRUE #> 97 pseudo_binary_subj_97 B TRUE #> 98 pseudo_binary_subj_98 B TRUE #> 99 pseudo_binary_subj_99 B TRUE #> 100 pseudo_binary_subj_100 B TRUE #> 101 pseudo_binary_subj_101 B TRUE #> 102 pseudo_binary_subj_102 B TRUE #> 103 pseudo_binary_subj_103 B TRUE #> 104 pseudo_binary_subj_104 B TRUE #> 105 pseudo_binary_subj_105 B TRUE #> 106 pseudo_binary_subj_106 B TRUE #> 107 pseudo_binary_subj_107 B TRUE #> 108 pseudo_binary_subj_108 B TRUE #> 109 pseudo_binary_subj_109 B TRUE #> 110 pseudo_binary_subj_110 B TRUE #> 111 pseudo_binary_subj_111 B TRUE #> 112 pseudo_binary_subj_112 B TRUE #> 113 pseudo_binary_subj_113 B TRUE #> 114 pseudo_binary_subj_114 B TRUE #> 115 pseudo_binary_subj_115 B TRUE #> 116 pseudo_binary_subj_116 B TRUE #> 117 pseudo_binary_subj_117 B TRUE #> 118 pseudo_binary_subj_118 B TRUE #> 119 pseudo_binary_subj_119 B TRUE #> 120 pseudo_binary_subj_120 B TRUE #> 121 pseudo_binary_subj_121 B TRUE #> 122 pseudo_binary_subj_122 B TRUE #> 123 pseudo_binary_subj_123 B TRUE #> 124 pseudo_binary_subj_124 B TRUE #> 125 pseudo_binary_subj_125 B TRUE #> 126 pseudo_binary_subj_126 B TRUE #> 127 pseudo_binary_subj_127 B TRUE #> 128 pseudo_binary_subj_128 B TRUE #> 129 pseudo_binary_subj_129 B TRUE #> 130 pseudo_binary_subj_130 B TRUE #> 131 pseudo_binary_subj_131 B TRUE #> 132 pseudo_binary_subj_132 B TRUE #> 133 pseudo_binary_subj_133 B TRUE #> 134 pseudo_binary_subj_134 B TRUE #> 135 pseudo_binary_subj_135 B TRUE #> 136 pseudo_binary_subj_136 B TRUE #> 137 pseudo_binary_subj_137 B TRUE #> 138 pseudo_binary_subj_138 B TRUE #> 139 pseudo_binary_subj_139 B TRUE #> 140 pseudo_binary_subj_140 B TRUE #> 141 pseudo_binary_subj_141 B TRUE #> 142 pseudo_binary_subj_142 B TRUE #> 143 pseudo_binary_subj_143 B TRUE #> 144 pseudo_binary_subj_144 B TRUE #> 145 pseudo_binary_subj_145 B TRUE #> 146 pseudo_binary_subj_146 B TRUE #> 147 pseudo_binary_subj_147 B TRUE #> 148 pseudo_binary_subj_148 B TRUE #> 149 pseudo_binary_subj_149 B TRUE #> 150 pseudo_binary_subj_150 B TRUE #> 151 pseudo_binary_subj_151 B TRUE #> 152 pseudo_binary_subj_152 B TRUE #> 153 pseudo_binary_subj_153 B TRUE #> 154 pseudo_binary_subj_154 B TRUE #> 155 pseudo_binary_subj_155 B TRUE #> 156 pseudo_binary_subj_156 B TRUE #> 157 pseudo_binary_subj_157 B TRUE #> 158 pseudo_binary_subj_158 B TRUE #> 159 pseudo_binary_subj_159 B TRUE #> 160 pseudo_binary_subj_160 B TRUE #> 161 pseudo_binary_subj_161 B TRUE #> 162 pseudo_binary_subj_162 B TRUE #> 163 pseudo_binary_subj_163 B TRUE #> 164 pseudo_binary_subj_164 B TRUE #> 165 pseudo_binary_subj_165 B TRUE #> 166 pseudo_binary_subj_166 B TRUE #> 167 pseudo_binary_subj_167 B TRUE #> 168 pseudo_binary_subj_168 B TRUE #> 169 pseudo_binary_subj_169 B TRUE #> 170 pseudo_binary_subj_170 B TRUE #> 171 pseudo_binary_subj_171 B TRUE #> 172 pseudo_binary_subj_172 B TRUE #> 173 pseudo_binary_subj_173 B TRUE #> 174 pseudo_binary_subj_174 B TRUE #> 175 pseudo_binary_subj_175 B TRUE #> 176 pseudo_binary_subj_176 B TRUE #> 177 pseudo_binary_subj_177 B TRUE #> 178 pseudo_binary_subj_178 B TRUE #> 179 pseudo_binary_subj_179 B TRUE #> 180 pseudo_binary_subj_180 B TRUE #> 181 pseudo_binary_subj_181 B TRUE #> 182 pseudo_binary_subj_182 B TRUE #> 183 pseudo_binary_subj_183 B TRUE #> 184 pseudo_binary_subj_184 B TRUE #> 185 pseudo_binary_subj_185 B TRUE #> 186 pseudo_binary_subj_186 B TRUE #> 187 pseudo_binary_subj_187 B TRUE #> 188 pseudo_binary_subj_188 B TRUE #> 189 pseudo_binary_subj_189 B TRUE #> 190 pseudo_binary_subj_190 B TRUE #> 191 pseudo_binary_subj_191 B TRUE #> 192 pseudo_binary_subj_192 B TRUE #> 193 pseudo_binary_subj_193 B TRUE #> 194 pseudo_binary_subj_194 B TRUE #> 195 pseudo_binary_subj_195 B TRUE #> 196 pseudo_binary_subj_196 B TRUE #> 197 pseudo_binary_subj_197 B TRUE #> 198 pseudo_binary_subj_198 B TRUE #> 199 pseudo_binary_subj_199 B TRUE #> 200 pseudo_binary_subj_200 B TRUE #> 201 pseudo_binary_subj_201 B TRUE #> 202 pseudo_binary_subj_202 B TRUE #> 203 pseudo_binary_subj_203 B TRUE #> 204 pseudo_binary_subj_204 B TRUE #> 205 pseudo_binary_subj_205 B TRUE #> 206 pseudo_binary_subj_206 B TRUE #> 207 pseudo_binary_subj_207 B TRUE #> 208 pseudo_binary_subj_208 B TRUE #> 209 pseudo_binary_subj_209 B TRUE #> 210 pseudo_binary_subj_210 B TRUE #> 211 pseudo_binary_subj_211 B TRUE #> 212 pseudo_binary_subj_212 B TRUE #> 213 pseudo_binary_subj_213 B TRUE #> 214 pseudo_binary_subj_214 B TRUE #> 215 pseudo_binary_subj_215 B TRUE #> 216 pseudo_binary_subj_216 B TRUE #> 217 pseudo_binary_subj_217 B TRUE #> 218 pseudo_binary_subj_218 B TRUE #> 219 pseudo_binary_subj_219 B TRUE #> 220 pseudo_binary_subj_220 B TRUE #> 221 pseudo_binary_subj_221 B TRUE #> 222 pseudo_binary_subj_222 B TRUE #> 223 pseudo_binary_subj_223 B TRUE #> 224 pseudo_binary_subj_224 B TRUE #> 225 pseudo_binary_subj_225 B TRUE #> 226 pseudo_binary_subj_226 B TRUE #> 227 pseudo_binary_subj_227 B TRUE #> 228 pseudo_binary_subj_228 B TRUE #> 229 pseudo_binary_subj_229 B TRUE #> 230 pseudo_binary_subj_230 B TRUE #> 231 pseudo_binary_subj_231 B TRUE #> 232 pseudo_binary_subj_232 B TRUE #> 233 pseudo_binary_subj_233 B TRUE #> 234 pseudo_binary_subj_234 B TRUE #> 235 pseudo_binary_subj_235 B TRUE #> 236 pseudo_binary_subj_236 B TRUE #> 237 pseudo_binary_subj_237 B TRUE #> 238 pseudo_binary_subj_238 B TRUE #> 239 pseudo_binary_subj_239 B TRUE #> 240 pseudo_binary_subj_240 B TRUE #> 241 pseudo_binary_subj_241 B TRUE #> 242 pseudo_binary_subj_242 B TRUE #> 243 pseudo_binary_subj_243 B TRUE #> 244 pseudo_binary_subj_244 B TRUE #> 245 pseudo_binary_subj_245 B TRUE #> 246 pseudo_binary_subj_246 B TRUE #> 247 pseudo_binary_subj_247 B TRUE #> 248 pseudo_binary_subj_248 B TRUE #> 249 pseudo_binary_subj_249 B TRUE #> 250 pseudo_binary_subj_250 B TRUE #> 251 pseudo_binary_subj_251 B TRUE #> 252 pseudo_binary_subj_252 B TRUE #> 253 pseudo_binary_subj_253 B TRUE #> 254 pseudo_binary_subj_254 B TRUE #> 255 pseudo_binary_subj_255 B TRUE #> 256 pseudo_binary_subj_256 B TRUE #> 257 pseudo_binary_subj_257 B TRUE #> 258 pseudo_binary_subj_258 B TRUE #> 259 pseudo_binary_subj_259 B TRUE #> 260 pseudo_binary_subj_260 B TRUE #> 261 pseudo_binary_subj_261 B TRUE #> 262 pseudo_binary_subj_262 B TRUE #> 263 pseudo_binary_subj_263 B TRUE #> 264 pseudo_binary_subj_264 B TRUE #> 265 pseudo_binary_subj_265 B TRUE #> 266 pseudo_binary_subj_266 B TRUE #> 267 pseudo_binary_subj_267 B TRUE #> 268 pseudo_binary_subj_268 B TRUE #> 269 pseudo_binary_subj_269 B TRUE #> 270 pseudo_binary_subj_270 B TRUE #> 271 pseudo_binary_subj_271 B TRUE #> 272 pseudo_binary_subj_272 B TRUE #> 273 pseudo_binary_subj_273 B TRUE #> 274 pseudo_binary_subj_274 B TRUE #> 275 pseudo_binary_subj_275 B TRUE #> 276 pseudo_binary_subj_276 B TRUE #> 277 pseudo_binary_subj_277 B TRUE #> 278 pseudo_binary_subj_278 B TRUE #> 279 pseudo_binary_subj_279 B TRUE #> 280 pseudo_binary_subj_280 B TRUE #> 281 pseudo_binary_subj_281 B FALSE #> 282 pseudo_binary_subj_282 B FALSE #> 283 pseudo_binary_subj_283 B FALSE #> 284 pseudo_binary_subj_284 B FALSE #> 285 pseudo_binary_subj_285 B FALSE #> 286 pseudo_binary_subj_286 B FALSE #> 287 pseudo_binary_subj_287 B FALSE #> 288 pseudo_binary_subj_288 B FALSE #> 289 pseudo_binary_subj_289 B FALSE #> 290 pseudo_binary_subj_290 B FALSE #> 291 pseudo_binary_subj_291 B FALSE #> 292 pseudo_binary_subj_292 B FALSE #> 293 pseudo_binary_subj_293 B FALSE #> 294 pseudo_binary_subj_294 B FALSE #> 295 pseudo_binary_subj_295 B FALSE #> 296 pseudo_binary_subj_296 B FALSE #> 297 pseudo_binary_subj_297 B FALSE #> 298 pseudo_binary_subj_298 B FALSE #> 299 pseudo_binary_subj_299 B FALSE #> 300 pseudo_binary_subj_300 B FALSE #> 301 pseudo_binary_subj_301 B FALSE #> 302 pseudo_binary_subj_302 B FALSE #> 303 pseudo_binary_subj_303 B FALSE #> 304 pseudo_binary_subj_304 B FALSE #> 305 pseudo_binary_subj_305 B FALSE #> 306 pseudo_binary_subj_306 B FALSE #> 307 pseudo_binary_subj_307 B FALSE #> 308 pseudo_binary_subj_308 B FALSE #> 309 pseudo_binary_subj_309 B FALSE #> 310 pseudo_binary_subj_310 B FALSE #> 311 pseudo_binary_subj_311 B FALSE #> 312 pseudo_binary_subj_312 B FALSE #> 313 pseudo_binary_subj_313 B FALSE #> 314 pseudo_binary_subj_314 B FALSE #> 315 pseudo_binary_subj_315 B FALSE #> 316 pseudo_binary_subj_316 B FALSE #> 317 pseudo_binary_subj_317 B FALSE #> 318 pseudo_binary_subj_318 B FALSE #> 319 pseudo_binary_subj_319 B FALSE #> 320 pseudo_binary_subj_320 B FALSE #> 321 pseudo_binary_subj_321 B FALSE #> 322 pseudo_binary_subj_322 B FALSE #> 323 pseudo_binary_subj_323 B FALSE #> 324 pseudo_binary_subj_324 B FALSE #> 325 pseudo_binary_subj_325 B FALSE #> 326 pseudo_binary_subj_326 B FALSE #> 327 pseudo_binary_subj_327 B FALSE #> 328 pseudo_binary_subj_328 B FALSE #> 329 pseudo_binary_subj_329 B FALSE #> 330 pseudo_binary_subj_330 B FALSE #> 331 pseudo_binary_subj_331 B FALSE #> 332 pseudo_binary_subj_332 B FALSE #> 333 pseudo_binary_subj_333 B FALSE #> 334 pseudo_binary_subj_334 B FALSE #> 335 pseudo_binary_subj_335 B FALSE #> 336 pseudo_binary_subj_336 B FALSE #> 337 pseudo_binary_subj_337 B FALSE #> 338 pseudo_binary_subj_338 B FALSE #> 339 pseudo_binary_subj_339 B FALSE #> 340 pseudo_binary_subj_340 B FALSE #> 341 pseudo_binary_subj_341 B FALSE #> 342 pseudo_binary_subj_342 B FALSE #> 343 pseudo_binary_subj_343 B FALSE #> 344 pseudo_binary_subj_344 B FALSE #> 345 pseudo_binary_subj_345 B FALSE #> 346 pseudo_binary_subj_346 B FALSE #> 347 pseudo_binary_subj_347 B FALSE #> 348 pseudo_binary_subj_348 B FALSE #> 349 pseudo_binary_subj_349 B FALSE #> 350 pseudo_binary_subj_350 B FALSE #> 351 pseudo_binary_subj_351 B FALSE #> 352 pseudo_binary_subj_352 B FALSE #> 353 pseudo_binary_subj_353 B FALSE #> 354 pseudo_binary_subj_354 B FALSE #> 355 pseudo_binary_subj_355 B FALSE #> 356 pseudo_binary_subj_356 B FALSE #> 357 pseudo_binary_subj_357 B FALSE #> 358 pseudo_binary_subj_358 B FALSE #> 359 pseudo_binary_subj_359 B FALSE #> 360 pseudo_binary_subj_360 B FALSE #> 361 pseudo_binary_subj_361 B FALSE #> 362 pseudo_binary_subj_362 B FALSE #> 363 pseudo_binary_subj_363 B FALSE #> 364 pseudo_binary_subj_364 B FALSE #> 365 pseudo_binary_subj_365 B FALSE #> 366 pseudo_binary_subj_366 B FALSE #> 367 pseudo_binary_subj_367 B FALSE #> 368 pseudo_binary_subj_368 B FALSE #> 369 pseudo_binary_subj_369 B FALSE #> 370 pseudo_binary_subj_370 B FALSE #> 371 pseudo_binary_subj_371 B FALSE #> 372 pseudo_binary_subj_372 B FALSE #> 373 pseudo_binary_subj_373 B FALSE #> 374 pseudo_binary_subj_374 B FALSE #> 375 pseudo_binary_subj_375 B FALSE #> 376 pseudo_binary_subj_376 B FALSE #> 377 pseudo_binary_subj_377 B FALSE #> 378 pseudo_binary_subj_378 B FALSE #> 379 pseudo_binary_subj_379 B FALSE #> 380 pseudo_binary_subj_380 B FALSE #> 381 pseudo_binary_subj_381 B FALSE #> 382 pseudo_binary_subj_382 B FALSE #> 383 pseudo_binary_subj_383 B FALSE #> 384 pseudo_binary_subj_384 B FALSE #> 385 pseudo_binary_subj_385 B FALSE #> 386 pseudo_binary_subj_386 B FALSE #> 387 pseudo_binary_subj_387 B FALSE #> 388 pseudo_binary_subj_388 B FALSE #> 389 pseudo_binary_subj_389 B FALSE #> 390 pseudo_binary_subj_390 B FALSE #> 391 pseudo_binary_subj_391 B FALSE #> 392 pseudo_binary_subj_392 B FALSE #> 393 pseudo_binary_subj_393 B FALSE #> 394 pseudo_binary_subj_394 B FALSE #> 395 pseudo_binary_subj_395 B FALSE #> 396 pseudo_binary_subj_396 B FALSE #> 397 pseudo_binary_subj_397 B FALSE #> 398 pseudo_binary_subj_398 B FALSE #> 399 pseudo_binary_subj_399 B FALSE #> 400 pseudo_binary_subj_400 B FALSE"},{"path":"https://hta-pharma.github.io/maicplus/reference/get_time_as.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert Time Values Using Scaling Factors — get_time_as","title":"Convert Time Values Using Scaling Factors — get_time_as","text":"Convert Time Values Using Scaling Factors","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/get_time_as.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert Time Values Using Scaling Factors — get_time_as","text":"","code":"get_time_as(times, as = NULL)"},{"path":"https://hta-pharma.github.io/maicplus/reference/get_time_as.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert Time Values Using Scaling Factors — get_time_as","text":"times Numeric time values time scale convert . One \"days\", \"weeks\", \"months\", \"years\"","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/get_time_as.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert Time Values Using Scaling Factors — get_time_as","text":"Returns numeric vector calculated times / get_time_conversion(factor = )","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/get_time_as.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert Time Values Using Scaling Factors — get_time_as","text":"","code":"get_time_as(50, as = \"months\") #> months #> 1.64271"},{"path":"https://hta-pharma.github.io/maicplus/reference/kmplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","title":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","text":"wrapper function basic_kmplot. argument setting similar maic_anchored maic_unanchored, used two functions.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/kmplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","text":"","code":"kmplot( weights_object, tte_ipd, tte_pseudo_ipd, trt_ipd, trt_agd, trt_common = NULL, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", km_conf_type = \"log-log\", km_layout = c(\"all\", \"by_trial\", \"by_arm\"), ... )"},{"path":"https://hta-pharma.github.io/maicplus/reference/kmplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","text":"weights_object object returned estimate_weight tte_ipd data frame individual patient data (IPD) internal trial, contain least \"USUBJID\", \"EVENT\", \"TIME\" columns column indicating treatment assignment tte_pseudo_ipd data frame pseudo IPD digitized KM curves external trial (time--event endpoint), contain least \"EVENT\", \"TIME\" trt_ipd string, name interested investigation arm internal trial dat_igd (real IPD) trt_agd string, name interested investigation arm external trial dat_pseudo (pseudo IPD) trt_common string, name common comparator internal external trial, default NULL, indicating unanchored case trt_var_ipd string, column name tte_ipd contains treatment assignment trt_var_agd string, column name tte_pseudo_ipd contains treatment assignment km_conf_type string, pass conf.type survfit km_layout string, applicable unanchored case (trt_common = NULL), indicated desired layout output KM curve. ... arguments basic_kmplot","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/kmplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","text":"unanchored case, KM plot risk set table. anchored case, depending km_layout, \"by_trial\", 2 1 plot, first KM curves (incl. weighted) IPD trial, KM curves AgD trial, risk set table. \"by_arm\", 2 1 plot, first KM curves trt_agd trt_ipd (without weights), KM curves trt_common AgD trial IPD trial (without weights). Risk set table appended. \"\", 2 2 plot, plots \"by_trial\" \"by_arm\" without risk set table appended.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/kmplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","text":"","code":"# unanchored example using kmplot data(weighted_sat) data(adtte_sat) data(pseudo_ipd_sat) kmplot( weights_object = weighted_sat, tte_ipd = adtte_sat, tte_pseudo_ipd = pseudo_ipd_sat, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_name = \"Overall Survival\", trt_ipd = \"A\", trt_agd = \"B\", trt_common = NULL, km_conf_type = \"log-log\", time_scale = \"month\", time_grid = seq(0, 20, by = 2), use_colors = NULL, use_line_types = NULL, use_pch_cex = 0.65, use_pch_alpha = 100 ) # anchored example using kmplot data(weighted_twt) data(adtte_twt) data(pseudo_ipd_twt) # plot by trial kmplot( weights_object = weighted_twt, tte_ipd = adtte_twt, tte_pseudo_ipd = pseudo_ipd_twt, trt_ipd = \"A\", trt_agd = \"B\", trt_common = \"C\", trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_name = \"Overall Survival\", km_conf_type = \"log-log\", km_layout = \"by_trial\", time_scale = \"month\", time_grid = seq(0, 20, by = 2), use_colors = NULL, use_line_types = NULL, use_pch_cex = 0.65, use_pch_alpha = 100 ) # plot by arm kmplot( weights_object = weighted_twt, tte_ipd = adtte_twt, tte_pseudo_ipd = pseudo_ipd_twt, trt_ipd = \"A\", trt_agd = \"B\", trt_common = \"C\", trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_name = \"Overall Survival\", km_conf_type = \"log-log\", km_layout = \"by_arm\", time_scale = \"month\", time_grid = seq(0, 20, by = 2), use_colors = NULL, use_line_types = NULL, use_pch_cex = 0.65, use_pch_alpha = 100 ) # plot all kmplot( weights_object = weighted_twt, tte_ipd = adtte_twt, tte_pseudo_ipd = pseudo_ipd_twt, trt_ipd = \"A\", trt_agd = \"B\", trt_common = \"C\", trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_name = \"Overall Survival\", km_conf_type = \"log-log\", km_layout = \"all\", time_scale = \"month\", time_grid = seq(0, 20, by = 2), use_colors = NULL, use_line_types = NULL, use_pch_cex = 0.65, use_pch_alpha = 100 )"},{"path":"https://hta-pharma.github.io/maicplus/reference/kmplot2.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","title":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","text":"wrapper function basic_kmplot2. argument setting similar maic_anchored maic_unanchored, used two functions.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/kmplot2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","text":"","code":"kmplot2( weights_object, tte_ipd, tte_pseudo_ipd, trt_ipd, trt_agd, trt_common = NULL, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", km_conf_type = \"log-log\", km_layout = c(\"all\", \"by_trial\", \"by_arm\"), time_scale, ... )"},{"path":"https://hta-pharma.github.io/maicplus/reference/kmplot2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","text":"weights_object object returned estimate_weight tte_ipd data frame individual patient data (IPD) internal trial, contain least \"USUBJID\", \"EVENT\", \"TIME\" columns column indicating treatment assignment tte_pseudo_ipd data frame pseudo IPD digitized KM curves external trial (time--event endpoint), contain least \"EVENT\", \"TIME\" trt_ipd string, name interested investigation arm internal trial dat_igd (real IPD) trt_agd string, name interested investigation arm external trial dat_pseudo (pseudo IPD) trt_common string, name common comparator internal external trial, default NULL, indicating unanchored case trt_var_ipd string, column name tte_ipd contains treatment assignment trt_var_agd string, column name tte_pseudo_ipd contains treatment assignment km_conf_type string, pass conf.type survfit km_layout string, applicable unanchored case (trt_common = NULL), indicated desired layout output KM curve. time_scale string, time unit median survival time, taking value 'years', 'months', weeks' 'days' ... arguments basic_kmplot2","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/kmplot2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","text":"unanchored case, KM plot risk set table. anchored case, depending km_layout, \"by_trial\", 2 1 plot, first KM curves (incl. weighted) IPD trial, KM curves AgD trial, risk set table. \"by_arm\", 2 1 plot, first KM curves trt_agd trt_ipd (without weights), KM curves trt_common AgD trial IPD trial (without weights). Risk set table appended. \"\", 2 2 plot, plots \"by_trial\" \"by_arm\" without risk set table appended.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/kmplot2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","text":"","code":"# unanchored example using kmplot2 data(weighted_sat) data(adtte_sat) data(pseudo_ipd_sat) kmplot2( weights_object = weighted_sat, tte_ipd = adtte_sat, tte_pseudo_ipd = pseudo_ipd_sat, trt_ipd = \"A\", trt_agd = \"B\", trt_common = NULL, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_name = \"Overall Survival\", km_conf_type = \"log-log\", time_scale = \"month\", break_x_by = 2, xlim = c(0, 20) ) # anchored example using kmplot2 data(weighted_twt) data(adtte_twt) data(pseudo_ipd_twt) # plot by trial kmplot2( weights_object = weighted_twt, tte_ipd = adtte_twt, tte_pseudo_ipd = pseudo_ipd_twt, trt_ipd = \"A\", trt_agd = \"B\", trt_common = \"C\", trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_name = \"Overall Survival\", km_conf_type = \"log-log\", km_layout = \"by_trial\", time_scale = \"month\", break_x_by = 2 ) # plot by arm kmplot2( weights_object = weighted_twt, tte_ipd = adtte_twt, tte_pseudo_ipd = pseudo_ipd_twt, trt_ipd = \"A\", trt_agd = \"B\", trt_common = \"C\", trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_name = \"Overall Survival\", km_conf_type = \"log-log\", km_layout = \"by_arm\", time_scale = \"month\", break_x_by = 2 ) # plot all kmplot2( weights_object = weighted_twt, tte_ipd = adtte_twt, tte_pseudo_ipd = pseudo_ipd_twt, trt_ipd = \"A\", trt_agd = \"B\", trt_common = \"C\", trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_name = \"Overall Survival\", km_conf_type = \"log-log\", km_layout = \"all\", time_scale = \"month\", break_x_by = 2, xlim = c(0, 20), show_risk_set = FALSE )"},{"path":"https://hta-pharma.github.io/maicplus/reference/maic_anchored.html","id":null,"dir":"Reference","previous_headings":"","what":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","title":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","text":"wrapper function provide adjusted effect estimates relevant statistics anchored case (.e. common comparator arm internal external trial).","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/maic_anchored.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","text":"","code":"maic_anchored( weights_object, ipd, pseudo_ipd, trt_ipd, trt_agd, trt_common, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_type = \"tte\", endpoint_name = \"Time to Event Endpoint\", eff_measure = c(\"HR\", \"OR\", \"RR\", \"RD\"), boot_ci_type = c(\"norm\", \"basic\", \"stud\", \"perc\", \"bca\"), time_scale = \"months\", km_conf_type = \"log-log\", binary_robust_cov_type = \"HC3\" )"},{"path":"https://hta-pharma.github.io/maicplus/reference/maic_anchored.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","text":"weights_object object returned estimate_weight ipd data frame meet format requirements 'Details', individual patient data (IPD) internal trial pseudo_ipd data frame, pseudo IPD digitized KM curve external trial (time--event endpoint) contingency table (binary endpoint) trt_ipd string, name interested investigation arm internal trial ipd (internal IPD) trt_agd string, name interested investigation arm external trial pseudo_ipd (pseudo IPD) trt_common string, name common comparator internal external trial trt_var_ipd string, column name ipd contains treatment assignment trt_var_agd string, column name ipd contains treatment assignment endpoint_type string, one following \"binary\", \"tte\" (time event) endpoint_name string, name time event endpoint, show last line title eff_measure string, \"RD\" (risk difference), \"\" (odds ratio), \"RR\" (relative risk) binary endpoint; \"HR\" time--event endpoint. default NULL, \"\" used binary case, otherwise \"HR\" used. boot_ci_type string, one c(\"norm\",\"basic\", \"stud\", \"perc\", \"bca\") select type bootstrap confidence interval. See boot::boot.ci details. time_scale string, time unit median survival time, taking value 'years', 'months', 'weeks' 'days'. NOTE: assumed values TIME column ipd pseudo_ipd unit days km_conf_type string, pass conf.type survfit binary_robust_cov_type string pass argument type sandwich::vcovHC, see possible options documentation function. Default \"HC3\"","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/maic_anchored.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","text":"list, contains 'descriptive' 'inferential'","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/maic_anchored.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","text":"required input ipd pseudo_ipd following columns. function sensitive upper lower case letters column names. USUBJID - character, unique subject ID ARM - character factor, treatment indicator, column name 'ARM'. User specify trt_var_ipd trt_var_agd time--event analysis, follow columns required: EVENT - numeric, 1 censored/death, 0 otherwise TIME - numeric column, observation time EVENT; unit days binary outcomes: RESPONSE - numeric, 1 event occurred, 0 otherwise","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/maic_anchored.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","text":"","code":"# Anchored example using maic_anchored for time-to-event data data(weighted_twt) data(adtte_twt) data(pseudo_ipd_twt) result_tte <- maic_anchored( weights_object = weighted_twt, ipd = adtte_twt, pseudo_ipd = pseudo_ipd_twt, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", trt_ipd = \"A\", trt_agd = \"B\", trt_common = \"C\", endpoint_name = \"Overall Survival\", endpoint_type = \"tte\", eff_measure = \"HR\", time_scale = \"month\", km_conf_type = \"log-log\", ) result_tte$inferential$report_median_surv #> treatment type records n.max n.start events rmean #> 1 ARM=C IPD, before matching 500 500.0000 500.0000 500.0000 2.564797 #> 2 ARM=A IPD, before matching 500 500.0000 500.0000 190.0000 8.709690 #> 3 ARM=C IPD, after matching 500 173.4208 173.4208 173.4208 2.690665 #> 4 ARM=A IPD, after matching 500 173.4208 173.4208 55.5418 10.575301 #> 5 ARM=C AgD, external 500 500.0000 500.0000 500.0000 2.455272 #> 6 ARM=B AgD, external 300 300.0000 300.0000 178.0000 4.303551 #> se(rmean) median 0.95LCL 0.95UCL #> 1 0.11366994 1.836467 1.644765 2.045808 #> 2 0.35514766 7.587627 6.278691 10.288538 #> 3 0.20750373 1.818345 1.457222 2.352181 #> 4 0.57325902 12.166430 10.244293 NA #> 5 0.09848888 1.851987 1.670540 2.009650 #> 6 0.33672602 2.746131 2.261125 3.320857 result_tte$inferential$report_overall_robustCI #> Matching treatment N n.events(%) median[95% CI] #> 2 IPD/Overall Survival ARM=A 500 190( 38.0) 7.6[6.3;10.3] #> 1 ARM=C 500 500(100.0) 1.8[1.6; 2.0] #> 21 weighted IPD/Overall Survival ARM=A 173.4 55.5( 32.0) 12.2[10.2; NA] #> 11 ARM=C 173.4 173.4(100.0) 1.8[ 1.5;2.4] #> 22 Agd/Overall Survival ARM=B 300 178( 59.3) 2.7[2.3;3.3] #> 12 ARM=C 500 500(100.0) 1.9[1.7;2.0] #> 7 ** adj.A vs B -- -- -- -- #> HR[95% CI] p-Value #> 2 0.22[0.19;0.26] <0.001 #> 1 #> 21 0.16[0.11;0.24] <0.001 #> 11 #> 22 0.57[0.48;0.68] <0.001 #> 12 #> 7 0.29 [0.19; 0.44] <0.001 result_tte$inferential$report_overall_bootCI #> Matching treatment N n.events(%) median[95% CI] #> 2 IPD/Overall Survival ARM=A 500 190( 38.0) 7.6[6.3;10.3] #> 1 ARM=C 500 500(100.0) 1.8[1.6; 2.0] #> 21 weighted IPD/Overall Survival ARM=A 173.4 55.5( 32.0) 12.2[10.2; NA] #> 11 ARM=C 173.4 173.4(100.0) 1.8[ 1.5;2.4] #> 22 AgD/Overall Survival ARM=B 300 178( 59.3) 2.7[2.3;3.3] #> 12 ARM=C 500 500(100.0) 1.9[1.7;2.0] #> 7 ** adj.A vs B -- -- -- -- #> HR[95% CI] p-Value #> 2 0.22[0.19;0.26] <0.001 #> 1 #> 21 0.16[0.11;0.24] <0.001 #> 11 #> 22 0.57[0.48;0.68] <0.001 #> 12 #> 7 0.29 [0.20; 0.43] # Anchored example using maic_anchored for binary outcome data(weighted_twt) data(adrs_twt) # Reported summary data pseudo_adrs <- get_pseudo_ipd_binary( binary_agd = data.frame( ARM = c(\"B\", \"C\", \"B\", \"C\"), RESPONSE = c(\"YES\", \"YES\", \"NO\", \"NO\"), COUNT = c(280, 120, 200, 200) ), format = \"stacked\" ) # inferential result result_binary <- maic_anchored( weights_object = weighted_twt, ipd = adrs_twt, pseudo_ipd = pseudo_adrs, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", trt_ipd = \"A\", trt_agd = \"B\", trt_common = \"C\", endpoint_name = \"Binary Event\", endpoint_type = \"binary\", eff_measure = \"OR\" ) #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Waiting for profiling to be done... #> Waiting for profiling to be done... #> Waiting for profiling to be done... #> Waiting for profiling to be done... result_binary$inferential$report_overall_robustCI #> Matching treatment N n.events(%) OR[95% CI] #> A IPD/Binary Event A 500 390(78.0) 1.70[1.28;2.26] #> C C 500 338(67.6) #> A1 weighted IPD/Binary Event A 500 128.8(25.8) 1.14[0.67;1.95] #> C1 C 500 124.2(24.8) #> B AgD/Binary Event B 480 280(58.3) 2.33[1.75;3.12] #> C2 C 320 120(37.5) #> 7 ** adj.A vs B -- -- -- 0.49 [0.27; 0.90] #> p-Value #> A <0.001 #> C #> A1 0.624 #> C1 #> B <0.001 #> C2 #> 7 0.022 result_binary$inferential$report_overall_bootCI #> Matching treatment N n.events(%) OR[95% CI] #> A IPD/Binary Event A 500 390(78.0) 1.70[1.28;2.26] #> C C 500 338(67.6) #> A1 weighted IPD/Binary Event A 500 128.8(25.8) 1.14[0.33;0.98] #> C1 C 500 124.2(24.8) #> B AgD/Binary Event B 480 280(58.3) 2.33[1.75;3.12] #> C2 C 320 120(37.5) #> 7 ** adj.A vs B -- -- -- 0.49 [0.14; 0.42] #> p-Value #> A <0.001 #> C #> A1 NA #> C1 #> B <0.001 #> C2 #> 7 "},{"path":"https://hta-pharma.github.io/maicplus/reference/maic_unanchored.html","id":null,"dir":"Reference","previous_headings":"","what":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","title":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","text":"wrapper function provide adjusted effect estimates relevant statistics unanchored case (.e. common comparator arm internal external trial).","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/maic_unanchored.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","text":"","code":"maic_unanchored( weights_object, ipd, pseudo_ipd, trt_ipd, trt_agd, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_type = \"tte\", endpoint_name = \"Time to Event Endpoint\", eff_measure = c(\"HR\", \"OR\", \"RR\", \"RD\"), boot_ci_type = c(\"norm\", \"basic\", \"stud\", \"perc\", \"bca\"), time_scale = \"months\", km_conf_type = \"log-log\", binary_robust_cov_type = \"HC3\" )"},{"path":"https://hta-pharma.github.io/maicplus/reference/maic_unanchored.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","text":"weights_object object returned estimate_weight ipd data frame meet format requirements 'Details', individual patient data (IPD) internal trial pseudo_ipd data frame, pseudo IPD digitized KM curve external trial (time--event endpoint) contingency table (binary endpoint) trt_ipd string, name interested investigation arm internal trial dat_igd (real IPD) trt_agd string, name interested investigation arm external trial pseudo_ipd (pseudo IPD) trt_var_ipd string, column name ipd contains treatment assignment trt_var_agd string, column name ipd contains treatment assignment endpoint_type string, one following \"binary\", \"tte\" (time event) endpoint_name string, name time event endpoint, show last line title eff_measure string, \"RD\" (risk difference), \"\" (odds ratio), \"RR\" (relative risk) binary endpoint; \"HR\" time--event endpoint. default NULL, \"\" used binary case, otherwise \"HR\" used. boot_ci_type string, one c(\"norm\",\"basic\", \"stud\", \"perc\", \"bca\") select type bootstrap confidence interval. See boot::boot.ci details. time_scale string, time unit median survival time, taking value 'years', 'months', 'weeks' 'days'. NOTE: assumed values TIME column ipd pseudo_ipd unit days km_conf_type string, pass conf.type survfit binary_robust_cov_type string pass argument type sandwich::vcovHC, see possible options documentation function. Default \"HC3\"","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/maic_unanchored.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","text":"list, contains 'descriptive' 'inferential'","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/maic_unanchored.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","text":"time--event analysis, required input ipd pseudo_ipd following columns. function sensitive upper lower case letters column names. USUBJID - character, unique subject ID ARM - character factor, treatment indicator, column name 'ARM'. User specify trt_var_ipd trt_var_agd EVENT - numeric, 1 censored/death, 0 otherwise TIME - numeric column, observation time EVENT; unit days","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/maic_unanchored.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","text":"","code":"# unanchored example using maic_unanchored for time-to-event data data(centered_ipd_sat) data(adtte_sat) data(pseudo_ipd_sat) #### derive weights weighted_data <- estimate_weights( data = centered_ipd_sat, centered_colnames = grep(\"_CENTERED$\", names(centered_ipd_sat)), start_val = 0, method = \"BFGS\" ) weighted_data2 <- estimate_weights( data = centered_ipd_sat, centered_colnames = grep(\"_CENTERED$\", names(centered_ipd_sat)), start_val = 0, method = \"BFGS\", n_boot_iteration = 500, set_seed_boot = 1234 ) # inferential result result <- maic_unanchored( weights_object = weighted_data, ipd = adtte_sat, pseudo_ipd = pseudo_ipd_sat, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", trt_ipd = \"A\", trt_agd = \"B\", endpoint_name = \"Overall Survival\", endpoint_type = \"tte\", eff_measure = \"HR\", time_scale = \"month\", km_conf_type = \"log-log\" ) result$inferential$report_median_surv #> treatment type records n.max n.start events rmean #> 1 ARM=B Before matching 300 300.0000 300.0000 178.00000 4.303551 #> 2 ARM=A Before matching 500 500.0000 500.0000 190.00000 8.709690 #> 3 ARM=B After matching 300 300.0000 300.0000 178.00000 4.303551 #> 4 ARM=A After matching 500 173.3137 173.3137 55.37392 10.584605 #> se(rmean) median 0.95LCL 0.95UCL #> 1 0.3367260 2.746131 2.261125 3.320857 #> 2 0.3551477 7.587627 6.278691 10.288538 #> 3 0.3367260 2.746131 2.261125 3.320857 #> 4 0.5739799 12.166430 10.244293 NA result$inferential$report_overall_robustCI #> Matching treatment N n.events(%) median[95% CI] #> 2 Before matching/Overall Survival ARM=A 500.0 190(38.0) 7.6[6.3;10.3] #> 1 ARM=B 300.0 178(59.3) 2.7[2.3; 3.3] #> 21 After matching/Overall Survival ARM=A 173.3 55.4(32.0) 12.2[10.2; NA] #> 11 ARM=B 300.0 178(59.3) 2.7[ 2.3;3.3] #> HR[95% CI] p-Value #> 2 0.37[0.30;0.46] <0.001 #> 1 #> 21 0.26[0.18;0.38] <0.001 #> 11 result_boot <- maic_unanchored( weights_object = weighted_data2, ipd = adtte_sat, pseudo_ipd = pseudo_ipd_sat, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", trt_ipd = \"A\", trt_agd = \"B\", endpoint_name = \"Overall Survival\", endpoint_type = \"tte\", eff_measure = \"HR\", time_scale = \"month\", km_conf_type = \"log-log\" ) result_boot$inferential$report_median_surv #> treatment type records n.max n.start events rmean #> 1 ARM=B Before matching 300 300.0000 300.0000 178.00000 4.303551 #> 2 ARM=A Before matching 500 500.0000 500.0000 190.00000 8.709690 #> 3 ARM=B After matching 300 300.0000 300.0000 178.00000 4.303551 #> 4 ARM=A After matching 500 173.3137 173.3137 55.37392 10.584605 #> se(rmean) median 0.95LCL 0.95UCL #> 1 0.3367260 2.746131 2.261125 3.320857 #> 2 0.3551477 7.587627 6.278691 10.288538 #> 3 0.3367260 2.746131 2.261125 3.320857 #> 4 0.5739799 12.166430 10.244293 NA result_boot$inferential$report_overall_robustCI #> Matching treatment N n.events(%) median[95% CI] #> 2 Before matching/Overall Survival ARM=A 500.0 190(38.0) 7.6[6.3;10.3] #> 1 ARM=B 300.0 178(59.3) 2.7[2.3; 3.3] #> 21 After matching/Overall Survival ARM=A 173.3 55.4(32.0) 12.2[10.2; NA] #> 11 ARM=B 300.0 178(59.3) 2.7[ 2.3;3.3] #> HR[95% CI] p-Value #> 2 0.37[0.30;0.46] <0.001 #> 1 #> 21 0.26[0.18;0.38] <0.001 #> 11 result_boot$inferential$report_overall_bootCI #> Matching treatment N n.events(%) median[95% CI] #> 2 Before matching/Overall Survival ARM=A 500.0 190(38.0) 7.6[6.3;10.3] #> 1 ARM=B 300.0 178(59.3) 2.7[2.3; 3.3] #> 21 After matching/Overall Survival ARM=A 173.3 55.4(32.0) 12.2[10.2; NA] #> 11 ARM=B 300.0 178(59.3) 2.7[ 2.3;3.3] #> HR[95% CI] p-Value #> 2 0.37[0.30;0.46] <0.001 #> 1 #> 21 0.26[NA;0.19] #> 11 # unanchored example using maic_unanchored for binary outcome data(centered_ipd_sat) data(adrs_sat) centered_ipd_sat #> USUBJID ARM AGE SEX SMOKE ECOG0 N_PR_THER SEX_MALE AGE_CENTERED #> 1 1 A 45 Male 0 0 4 1 -6 #> 2 2 A 71 Male 0 0 3 1 20 #> 3 3 A 58 Male 1 1 2 1 7 #> 4 4 A 48 Female 0 1 4 0 -3 #> 5 5 A 69 Male 0 1 4 1 18 #> 6 6 A 48 Female 0 1 4 0 -3 #> 7 7 A 47 Male 1 0 3 1 -4 #> 8 8 A 61 Male 1 0 1 1 10 #> 9 9 A 54 Female 1 1 1 0 3 #> 10 10 A 56 Female 1 0 3 0 5 #> 11 11 A 63 Female 0 0 4 0 12 #> 12 12 A 50 Female 0 0 1 0 -1 #> 13 13 A 57 Male 0 1 3 1 6 #> 14 14 A 62 Female 1 1 1 0 11 #> 15 15 A 57 Female 0 1 3 0 6 #> 16 16 A 66 Male 0 0 2 1 15 #> 17 17 A 75 Male 1 1 3 1 24 #> 18 18 A 47 Female 0 0 4 0 -4 #> 19 19 A 57 Male 0 0 3 1 6 #> 20 20 A 54 Male 0 0 3 1 3 #> 21 21 A 55 Male 1 0 3 1 4 #> 22 22 A 64 Male 0 1 3 1 13 #> 23 23 A 53 Female 1 0 3 0 2 #> 24 24 A 58 Male 1 1 2 1 7 #> 25 25 A 47 Male 0 0 1 1 -4 #> 26 26 A 60 Female 1 0 1 0 9 #> 27 27 A 49 Female 0 1 3 0 -2 #> 28 28 A 55 Female 0 0 1 0 4 #> 29 29 A 66 Female 0 1 2 0 15 #> 30 30 A 58 Male 0 1 4 1 7 #> 31 31 A 49 Male 0 1 4 1 -2 #> 32 32 A 61 Male 0 0 4 1 10 #> 33 33 A 66 Male 1 0 3 1 15 #> 34 34 A 45 Male 0 0 1 1 -6 #> 35 35 A 59 Female 1 1 2 0 8 #> 36 36 A 74 Female 1 0 4 0 23 #> 37 37 A 73 Female 0 0 3 0 22 #> 38 38 A 74 Male 0 1 4 1 23 #> 39 39 A 54 Male 0 0 1 1 3 #> 40 40 A 58 Female 1 1 1 0 7 #> 41 41 A 61 Female 0 1 3 0 10 #> 42 42 A 47 Female 1 1 2 0 -4 #> 43 43 A 73 Female 1 1 2 0 22 #> 44 44 A 68 Male 0 0 1 1 17 #> 45 45 A 49 Female 0 0 3 0 -2 #> 46 46 A 71 Female 0 0 2 0 20 #> 47 47 A 70 Male 0 1 4 1 19 #> 48 48 A 62 Female 1 0 1 0 11 #> 49 49 A 49 Male 0 0 1 1 -2 #> 50 50 A 74 Female 0 0 1 0 23 #> 51 51 A 46 Female 0 1 3 0 -5 #> 52 52 A 68 Female 1 0 3 0 17 #> 53 53 A 46 Male 1 0 2 1 -5 #> 54 54 A 75 Female 1 1 3 0 24 #> 55 55 A 47 Female 0 0 3 0 -4 #> 56 56 A 56 Male 0 1 3 1 5 #> 57 57 A 72 Female 0 0 3 0 21 #> 58 58 A 57 Male 1 1 4 1 6 #> 59 59 A 46 Male 0 0 1 1 -5 #> 60 60 A 56 Female 1 1 1 0 5 #> 61 61 A 73 Male 0 1 2 1 22 #> 62 62 A 60 Female 1 1 3 0 9 #> 63 63 A 75 Male 0 0 2 1 24 #> 64 64 A 69 Female 1 1 2 0 18 #> 65 65 A 47 Female 0 1 1 0 -4 #> 66 66 A 74 Male 0 0 4 1 23 #> 67 67 A 71 Female 0 1 1 0 20 #> 68 68 A 49 Female 1 1 1 0 -2 #> 69 69 A 68 Male 0 0 3 1 17 #> 70 70 A 49 Male 0 1 1 1 -2 #> 71 71 A 70 Male 0 1 1 1 19 #> 72 72 A 45 Female 0 0 2 0 -6 #> 73 73 A 47 Female 0 1 3 0 -4 #> 74 74 A 58 Male 0 1 3 1 7 #> 75 75 A 49 Female 0 1 4 0 -2 #> 76 76 A 68 Female 0 0 1 0 17 #> 77 77 A 60 Male 0 0 4 1 9 #> 78 78 A 45 Female 1 0 1 0 -6 #> 79 79 A 57 Female 0 0 1 0 6 #> 80 80 A 50 Female 0 1 1 0 -1 #> 81 81 A 63 Male 0 1 3 1 12 #> 82 82 A 47 Female 0 0 2 0 -4 #> 83 83 A 68 Female 0 1 4 0 17 #> 84 84 A 51 Male 0 0 4 1 0 #> 85 85 A 60 Male 0 0 1 1 9 #> 86 86 A 52 Female 1 0 4 0 1 #> 87 87 A 69 Male 1 1 1 1 18 #> 88 88 A 70 Female 0 1 4 0 19 #> 89 89 A 72 Male 0 0 2 1 21 #> 90 90 A 46 Female 0 1 1 0 -5 #> 91 91 A 51 Male 1 0 4 1 0 #> 92 92 A 69 Female 0 1 1 0 18 #> 93 93 A 66 Male 0 0 1 1 15 #> 94 94 A 73 Male 0 1 1 1 22 #> 95 95 A 73 Female 1 0 3 0 22 #> 96 96 A 62 Female 0 1 2 0 11 #> 97 97 A 55 Female 0 0 4 0 4 #> 98 98 A 67 Male 1 0 3 1 16 #> 99 99 A 54 Female 1 0 3 0 3 #> 100 100 A 52 Female 1 1 4 0 1 #> 101 101 A 57 Male 0 0 2 1 6 #> 102 102 A 57 Female 1 1 3 0 6 #> 103 103 A 57 Male 0 0 3 1 6 #> 104 104 A 67 Female 1 1 2 0 16 #> 105 105 A 67 Female 1 1 2 0 16 #> 106 106 A 74 Female 1 1 2 0 23 #> 107 107 A 72 Female 1 0 2 0 21 #> 108 108 A 73 Female 0 0 3 0 22 #> 109 109 A 57 Female 0 0 4 0 6 #> 110 110 A 69 Female 1 0 1 0 18 #> 111 111 A 55 Male 0 0 1 1 4 #> 112 112 A 74 Female 0 0 4 0 23 #> 113 113 A 68 Female 0 0 4 0 17 #> 114 114 A 53 Male 0 0 2 1 2 #> 115 115 A 69 Male 0 0 2 1 18 #> 116 116 A 68 Male 0 1 2 1 17 #> 117 117 A 58 Male 0 0 1 1 7 #> 118 118 A 64 Female 0 0 3 0 13 #> 119 119 A 71 Male 0 0 1 1 20 #> 120 120 A 69 Female 0 1 2 0 18 #> 121 121 A 64 Female 1 0 4 0 13 #> 122 122 A 64 Male 1 0 1 1 13 #> 123 123 A 55 Male 0 1 3 1 4 #> 124 124 A 74 Male 0 0 3 1 23 #> 125 125 A 50 Male 0 1 3 1 -1 #> 126 126 A 68 Male 0 1 1 1 17 #> 127 127 A 60 Male 0 0 2 1 9 #> 128 128 A 59 Female 0 0 3 0 8 #> 129 129 A 71 Female 1 0 3 0 20 #> 130 130 A 69 Male 0 1 4 1 18 #> 131 131 A 56 Female 0 1 1 0 5 #> 132 132 A 51 Male 0 1 3 1 0 #> 133 133 A 65 Male 0 1 2 1 14 #> 134 134 A 45 Male 1 1 3 1 -6 #> 135 135 A 49 Female 0 0 2 0 -2 #> 136 136 A 74 Female 1 0 4 0 23 #> 137 137 A 71 Female 1 1 3 0 20 #> 138 138 A 65 Male 0 0 1 1 14 #> 139 139 A 67 Female 0 0 2 0 16 #> 140 140 A 48 Female 0 0 3 0 -3 #> 141 141 A 70 Female 0 1 4 0 19 #> 142 142 A 72 Female 0 0 1 0 21 #> 143 143 A 53 Male 1 1 4 1 2 #> 144 144 A 68 Female 1 0 3 0 17 #> 145 145 A 65 Male 0 1 1 1 14 #> 146 146 A 70 Female 1 1 4 0 19 #> 147 147 A 58 Male 0 0 2 1 7 #> 148 148 A 68 Female 0 1 4 0 17 #> 149 149 A 54 Female 0 0 4 0 3 #> 150 150 A 75 Female 1 1 4 0 24 #> 151 151 A 49 Female 1 1 3 0 -2 #> 152 152 A 60 Male 0 0 4 1 9 #> 153 153 A 45 Female 1 0 4 0 -6 #> 154 154 A 73 Female 1 0 3 0 22 #> 155 155 A 71 Female 0 1 3 0 20 #> 156 156 A 73 Female 0 0 3 0 22 #> 157 157 A 58 Female 0 0 4 0 7 #> 158 158 A 59 Female 0 1 2 0 8 #> 159 159 A 75 Female 0 1 2 0 24 #> 160 160 A 65 Male 0 0 2 1 14 #> 161 161 A 48 Male 1 0 2 1 -3 #> 162 162 A 63 Female 0 1 2 0 12 #> 163 163 A 74 Female 0 0 3 0 23 #> 164 164 A 64 Female 0 0 3 0 13 #> 165 165 A 49 Female 0 0 1 0 -2 #> 166 166 A 56 Female 0 1 4 0 5 #> 167 167 A 56 Female 0 1 2 0 5 #> 168 168 A 48 Female 0 0 1 0 -3 #> 169 169 A 65 Male 0 1 3 1 14 #> 170 170 A 53 Female 0 0 3 0 2 #> 171 171 A 72 Male 0 0 2 1 21 #> 172 172 A 75 Female 1 0 4 0 24 #> 173 173 A 70 Female 0 1 2 0 19 #> 174 174 A 53 Female 1 0 2 0 2 #> 175 175 A 45 Female 1 1 3 0 -6 #> 176 176 A 53 Female 1 1 3 0 2 #> 177 177 A 52 Female 0 1 2 0 1 #> 178 178 A 61 Female 1 0 4 0 10 #> 179 179 A 70 Male 0 0 3 1 19 #> 180 180 A 58 Female 0 0 3 0 7 #> 181 181 A 54 Male 0 0 1 1 3 #> 182 182 A 53 Male 0 0 2 1 2 #> 183 183 A 74 Male 0 0 2 1 23 #> 184 184 A 64 Male 0 1 3 1 13 #> 185 185 A 52 Male 0 0 1 1 1 #> 186 186 A 73 Female 0 0 3 0 22 #> 187 187 A 55 Female 1 0 4 0 4 #> 188 188 A 71 Female 0 0 3 0 20 #> 189 189 A 57 Female 1 0 3 0 6 #> 190 190 A 49 Female 1 0 2 0 -2 #> 191 191 A 69 Male 0 0 4 1 18 #> 192 192 A 74 Female 1 0 2 0 23 #> 193 193 A 59 Female 0 0 3 0 8 #> 194 194 A 53 Male 0 0 4 1 2 #> 195 195 A 52 Female 0 1 3 0 1 #> 196 196 A 47 Female 1 1 1 0 -4 #> 197 197 A 61 Female 0 0 4 0 10 #> 198 198 A 51 Female 0 0 2 0 0 #> 199 199 A 62 Female 1 0 1 0 11 #> 200 200 A 59 Female 1 0 2 0 8 #> 201 201 A 58 Male 0 0 2 1 7 #> 202 202 A 61 Female 0 1 4 0 10 #> 203 203 A 45 Female 0 0 1 0 -6 #> 204 204 A 59 Male 1 1 2 1 8 #> 205 205 A 58 Female 1 0 2 0 7 #> 206 206 A 67 Male 0 0 1 1 16 #> 207 207 A 51 Female 1 0 2 0 0 #> 208 208 A 68 Male 1 1 3 1 17 #> 209 209 A 53 Female 1 0 2 0 2 #> 210 210 A 64 Male 1 0 3 1 13 #> 211 211 A 61 Male 0 1 1 1 10 #> 212 212 A 52 Male 1 0 2 1 1 #> 213 213 A 57 Female 0 1 4 0 6 #> 214 214 A 57 Female 1 0 2 0 6 #> 215 215 A 49 Female 0 0 4 0 -2 #> 216 216 A 51 Male 1 1 3 1 0 #> 217 217 A 60 Female 0 0 2 0 9 #> 218 218 A 60 Female 1 0 1 0 9 #> 219 219 A 71 Female 0 0 3 0 20 #> 220 220 A 54 Female 0 0 1 0 3 #> 221 221 A 51 Male 1 0 1 1 0 #> 222 222 A 71 Male 0 1 3 1 20 #> 223 223 A 47 Male 0 0 2 1 -4 #> 224 224 A 65 Male 0 0 1 1 14 #> 225 225 A 53 Female 0 0 1 0 2 #> 226 226 A 56 Female 0 0 1 0 5 #> 227 227 A 51 Male 1 0 4 1 0 #> 228 228 A 68 Female 0 0 2 0 17 #> 229 229 A 75 Female 0 1 1 0 24 #> 230 230 A 49 Female 0 0 2 0 -2 #> 231 231 A 74 Male 1 0 1 1 23 #> 232 232 A 66 Female 0 0 1 0 15 #> 233 233 A 74 Female 0 0 1 0 23 #> 234 234 A 62 Female 0 1 2 0 11 #> 235 235 A 53 Female 0 0 1 0 2 #> 236 236 A 62 Female 0 1 1 0 11 #> 237 237 A 70 Female 1 0 3 0 19 #> 238 238 A 60 Female 1 0 2 0 9 #> 239 239 A 72 Male 0 0 1 1 21 #> 240 240 A 74 Female 1 0 3 0 23 #> 241 241 A 47 Female 1 0 3 0 -4 #> 242 242 A 54 Female 0 1 3 0 3 #> 243 243 A 65 Female 0 1 2 0 14 #> 244 244 A 74 Male 0 1 1 1 23 #> 245 245 A 61 Male 0 1 2 1 10 #> 246 246 A 54 Female 1 0 3 0 3 #> 247 247 A 65 Male 0 0 3 1 14 #> 248 248 A 72 Female 1 0 3 0 21 #> 249 249 A 71 Female 1 1 2 0 20 #> 250 250 A 65 Male 0 0 3 1 14 #> 251 251 A 58 Male 0 1 1 1 7 #> 252 252 A 46 Male 0 0 1 1 -5 #> 253 253 A 53 Female 1 0 4 0 2 #> 254 254 A 71 Female 1 0 4 0 20 #> 255 255 A 47 Male 0 1 3 1 -4 #> 256 256 A 45 Male 0 0 1 1 -6 #> 257 257 A 50 Male 0 0 3 1 -1 #> 258 258 A 67 Female 1 0 3 0 16 #> 259 259 A 72 Male 1 0 2 1 21 #> 260 260 A 45 Female 0 1 2 0 -6 #> 261 261 A 75 Female 1 0 1 0 24 #> 262 262 A 65 Male 0 0 3 1 14 #> 263 263 A 60 Female 0 1 3 0 9 #> 264 264 A 75 Female 0 1 4 0 24 #> 265 265 A 60 Female 1 0 4 0 9 #> 266 266 A 49 Female 1 0 2 0 -2 #> 267 267 A 58 Female 0 1 1 0 7 #> 268 268 A 57 Male 1 1 3 1 6 #> 269 269 A 69 Male 1 0 2 1 18 #> 270 270 A 51 Male 0 1 1 1 0 #> 271 271 A 54 Female 1 0 4 0 3 #> 272 272 A 55 Male 0 1 3 1 4 #> 273 273 A 49 Female 0 0 4 0 -2 #> 274 274 A 74 Female 1 0 1 0 23 #> 275 275 A 55 Male 1 0 1 1 4 #> 276 276 A 52 Female 1 0 1 0 1 #> 277 277 A 65 Male 0 0 2 1 14 #> 278 278 A 70 Female 1 0 1 0 19 #> 279 279 A 66 Female 1 1 2 0 15 #> 280 280 A 63 Female 0 1 4 0 12 #> 281 281 A 61 Female 0 1 3 0 10 #> 282 282 A 65 Male 0 1 2 1 14 #> 283 283 A 73 Male 0 0 2 1 22 #> 284 284 A 55 Female 1 1 4 0 4 #> 285 285 A 56 Female 1 1 4 0 5 #> 286 286 A 68 Female 0 1 1 0 17 #> 287 287 A 74 Female 1 0 4 0 23 #> 288 288 A 67 Female 0 0 2 0 16 #> 289 289 A 66 Male 0 1 3 1 15 #> 290 290 A 48 Female 0 0 3 0 -3 #> 291 291 A 49 Female 1 1 3 0 -2 #> 292 292 A 60 Female 1 1 1 0 9 #> 293 293 A 69 Female 0 1 4 0 18 #> 294 294 A 58 Female 0 1 3 0 7 #> 295 295 A 45 Female 1 1 4 0 -6 #> 296 296 A 49 Female 0 1 1 0 -2 #> 297 297 A 67 Female 1 0 4 0 16 #> 298 298 A 63 Male 0 0 4 1 12 #> 299 299 A 50 Female 0 0 2 0 -1 #> 300 300 A 68 Female 0 1 3 0 17 #> 301 301 A 53 Male 0 1 2 1 2 #> 302 302 A 63 Male 0 1 2 1 12 #> 303 303 A 58 Male 0 0 4 1 7 #> 304 304 A 70 Female 0 1 4 0 19 #> 305 305 A 56 Female 0 0 1 0 5 #> 306 306 A 56 Male 0 1 3 1 5 #> 307 307 A 61 Female 0 0 3 0 10 #> 308 308 A 72 Male 0 0 2 1 21 #> 309 309 A 51 Male 0 1 1 1 0 #> 310 310 A 72 Male 0 0 4 1 21 #> 311 311 A 64 Female 0 0 3 0 13 #> 312 312 A 59 Male 0 0 2 1 8 #> 313 313 A 75 Female 0 0 3 0 24 #> 314 314 A 75 Female 0 0 1 0 24 #> 315 315 A 74 Male 0 0 2 1 23 #> 316 316 A 54 Male 0 1 4 1 3 #> 317 317 A 55 Female 0 0 3 0 4 #> 318 318 A 52 Female 1 0 1 0 1 #> 319 319 A 46 Female 0 1 1 0 -5 #> 320 320 A 53 Male 0 0 1 1 2 #> 321 321 A 54 Female 0 1 1 0 3 #> 322 322 A 62 Female 0 0 2 0 11 #> 323 323 A 54 Male 1 0 4 1 3 #> 324 324 A 56 Female 0 0 4 0 5 #> 325 325 A 48 Female 0 0 1 0 -3 #> 326 326 A 52 Female 0 1 1 0 1 #> 327 327 A 55 Female 0 1 3 0 4 #> 328 328 A 69 Female 0 0 2 0 18 #> 329 329 A 48 Female 0 0 1 0 -3 #> 330 330 A 48 Female 1 1 3 0 -3 #> 331 331 A 60 Male 0 0 3 1 9 #> 332 332 A 74 Female 0 1 2 0 23 #> 333 333 A 45 Female 0 0 1 0 -6 #> 334 334 A 64 Male 1 0 1 1 13 #> 335 335 A 75 Female 1 1 3 0 24 #> 336 336 A 62 Female 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489 0.5 1109.4375 -0.49 -0.35 #> 490 0.5 988.4375 -0.49 0.65 #> 491 0.5 2012.4375 -0.49 -0.35 #> 492 0.5 1877.4375 0.51 0.65 #> 493 -0.5 -586.5625 0.51 0.65 #> 494 0.5 2429.4375 -0.49 0.65 #> 495 0.5 413.4375 0.51 0.65 #> 496 0.5 2572.4375 -0.49 -0.35 #> 497 -0.5 -307.5625 -0.49 -0.35 #> 498 0.5 2012.4375 -0.49 -0.35 #> 499 -0.5 -586.5625 -0.49 -0.35 #> 500 0.5 752.4375 -0.49 0.65 #> SMOKE_CENTERED N_PR_THER_MEDIAN_CENTERED #> 1 -0.1933333 0.5 #> 2 -0.1933333 0.5 #> 3 0.8066667 -0.5 #> 4 -0.1933333 0.5 #> 5 -0.1933333 0.5 #> 6 -0.1933333 0.5 #> 7 0.8066667 0.5 #> 8 0.8066667 -0.5 #> 9 0.8066667 -0.5 #> 10 0.8066667 0.5 #> 11 -0.1933333 0.5 #> 12 -0.1933333 -0.5 #> 13 -0.1933333 0.5 #> 14 0.8066667 -0.5 #> 15 -0.1933333 0.5 #> 16 -0.1933333 -0.5 #> 17 0.8066667 0.5 #> 18 -0.1933333 0.5 #> 19 -0.1933333 0.5 #> 20 -0.1933333 0.5 #> 21 0.8066667 0.5 #> 22 -0.1933333 0.5 #> 23 0.8066667 0.5 #> 24 0.8066667 -0.5 #> 25 -0.1933333 -0.5 #> 26 0.8066667 -0.5 #> 27 -0.1933333 0.5 #> 28 -0.1933333 -0.5 #> 29 -0.1933333 -0.5 #> 30 -0.1933333 0.5 #> 31 -0.1933333 0.5 #> 32 -0.1933333 0.5 #> 33 0.8066667 0.5 #> 34 -0.1933333 -0.5 #> 35 0.8066667 -0.5 #> 36 0.8066667 0.5 #> 37 -0.1933333 0.5 #> 38 -0.1933333 0.5 #> 39 -0.1933333 -0.5 #> 40 0.8066667 -0.5 #> 41 -0.1933333 0.5 #> 42 0.8066667 -0.5 #> 43 0.8066667 -0.5 #> 44 -0.1933333 -0.5 #> 45 -0.1933333 0.5 #> 46 -0.1933333 -0.5 #> 47 -0.1933333 0.5 #> 48 0.8066667 -0.5 #> 49 -0.1933333 -0.5 #> 50 -0.1933333 -0.5 #> 51 -0.1933333 0.5 #> 52 0.8066667 0.5 #> 53 0.8066667 -0.5 #> 54 0.8066667 0.5 #> 55 -0.1933333 0.5 #> 56 -0.1933333 0.5 #> 57 -0.1933333 0.5 #> 58 0.8066667 0.5 #> 59 -0.1933333 -0.5 #> 60 0.8066667 -0.5 #> 61 -0.1933333 -0.5 #> 62 0.8066667 0.5 #> 63 -0.1933333 -0.5 #> 64 0.8066667 -0.5 #> 65 -0.1933333 -0.5 #> 66 -0.1933333 0.5 #> 67 -0.1933333 -0.5 #> 68 0.8066667 -0.5 #> 69 -0.1933333 0.5 #> 70 -0.1933333 -0.5 #> 71 -0.1933333 -0.5 #> 72 -0.1933333 -0.5 #> 73 -0.1933333 0.5 #> 74 -0.1933333 0.5 #> 75 -0.1933333 0.5 #> 76 -0.1933333 -0.5 #> 77 -0.1933333 0.5 #> 78 0.8066667 -0.5 #> 79 -0.1933333 -0.5 #> 80 -0.1933333 -0.5 #> 81 -0.1933333 0.5 #> 82 -0.1933333 -0.5 #> 83 -0.1933333 0.5 #> 84 -0.1933333 0.5 #> 85 -0.1933333 -0.5 #> 86 0.8066667 0.5 #> 87 0.8066667 -0.5 #> 88 -0.1933333 0.5 #> 89 -0.1933333 -0.5 #> 90 -0.1933333 -0.5 #> 91 0.8066667 0.5 #> 92 -0.1933333 -0.5 #> 93 -0.1933333 -0.5 #> 94 -0.1933333 -0.5 #> 95 0.8066667 0.5 #> 96 -0.1933333 -0.5 #> 97 -0.1933333 0.5 #> 98 0.8066667 0.5 #> 99 0.8066667 0.5 #> 100 0.8066667 0.5 #> 101 -0.1933333 -0.5 #> 102 0.8066667 0.5 #> 103 -0.1933333 0.5 #> 104 0.8066667 -0.5 #> 105 0.8066667 -0.5 #> 106 0.8066667 -0.5 #> 107 0.8066667 -0.5 #> 108 -0.1933333 0.5 #> 109 -0.1933333 0.5 #> 110 0.8066667 -0.5 #> 111 -0.1933333 -0.5 #> 112 -0.1933333 0.5 #> 113 -0.1933333 0.5 #> 114 -0.1933333 -0.5 #> 115 -0.1933333 -0.5 #> 116 -0.1933333 -0.5 #> 117 -0.1933333 -0.5 #> 118 -0.1933333 0.5 #> 119 -0.1933333 -0.5 #> 120 -0.1933333 -0.5 #> 121 0.8066667 0.5 #> 122 0.8066667 -0.5 #> 123 -0.1933333 0.5 #> 124 -0.1933333 0.5 #> 125 -0.1933333 0.5 #> 126 -0.1933333 -0.5 #> 127 -0.1933333 -0.5 #> 128 -0.1933333 0.5 #> 129 0.8066667 0.5 #> 130 -0.1933333 0.5 #> 131 -0.1933333 -0.5 #> 132 -0.1933333 0.5 #> 133 -0.1933333 -0.5 #> 134 0.8066667 0.5 #> 135 -0.1933333 -0.5 #> 136 0.8066667 0.5 #> 137 0.8066667 0.5 #> 138 -0.1933333 -0.5 #> 139 -0.1933333 -0.5 #> 140 -0.1933333 0.5 #> 141 -0.1933333 0.5 #> 142 -0.1933333 -0.5 #> 143 0.8066667 0.5 #> 144 0.8066667 0.5 #> 145 -0.1933333 -0.5 #> 146 0.8066667 0.5 #> 147 -0.1933333 -0.5 #> 148 -0.1933333 0.5 #> 149 -0.1933333 0.5 #> 150 0.8066667 0.5 #> 151 0.8066667 0.5 #> 152 -0.1933333 0.5 #> 153 0.8066667 0.5 #> 154 0.8066667 0.5 #> 155 -0.1933333 0.5 #> 156 -0.1933333 0.5 #> 157 -0.1933333 0.5 #> 158 -0.1933333 -0.5 #> 159 -0.1933333 -0.5 #> 160 -0.1933333 -0.5 #> 161 0.8066667 -0.5 #> 162 -0.1933333 -0.5 #> 163 -0.1933333 0.5 #> 164 -0.1933333 0.5 #> 165 -0.1933333 -0.5 #> 166 -0.1933333 0.5 #> 167 -0.1933333 -0.5 #> 168 -0.1933333 -0.5 #> 169 -0.1933333 0.5 #> 170 -0.1933333 0.5 #> 171 -0.1933333 -0.5 #> 172 0.8066667 0.5 #> 173 -0.1933333 -0.5 #> 174 0.8066667 -0.5 #> 175 0.8066667 0.5 #> 176 0.8066667 0.5 #> 177 -0.1933333 -0.5 #> 178 0.8066667 0.5 #> 179 -0.1933333 0.5 #> 180 -0.1933333 0.5 #> 181 -0.1933333 -0.5 #> 182 -0.1933333 -0.5 #> 183 -0.1933333 -0.5 #> 184 -0.1933333 0.5 #> 185 -0.1933333 -0.5 #> 186 -0.1933333 0.5 #> 187 0.8066667 0.5 #> 188 -0.1933333 0.5 #> 189 0.8066667 0.5 #> 190 0.8066667 -0.5 #> 191 -0.1933333 0.5 #> 192 0.8066667 -0.5 #> 193 -0.1933333 0.5 #> 194 -0.1933333 0.5 #> 195 -0.1933333 0.5 #> 196 0.8066667 -0.5 #> 197 -0.1933333 0.5 #> 198 -0.1933333 -0.5 #> 199 0.8066667 -0.5 #> 200 0.8066667 -0.5 #> 201 -0.1933333 -0.5 #> 202 -0.1933333 0.5 #> 203 -0.1933333 -0.5 #> 204 0.8066667 -0.5 #> 205 0.8066667 -0.5 #> 206 -0.1933333 -0.5 #> 207 0.8066667 -0.5 #> 208 0.8066667 0.5 #> 209 0.8066667 -0.5 #> 210 0.8066667 0.5 #> 211 -0.1933333 -0.5 #> 212 0.8066667 -0.5 #> 213 -0.1933333 0.5 #> 214 0.8066667 -0.5 #> 215 -0.1933333 0.5 #> 216 0.8066667 0.5 #> 217 -0.1933333 -0.5 #> 218 0.8066667 -0.5 #> 219 -0.1933333 0.5 #> 220 -0.1933333 -0.5 #> 221 0.8066667 -0.5 #> 222 -0.1933333 0.5 #> 223 -0.1933333 -0.5 #> 224 -0.1933333 -0.5 #> 225 -0.1933333 -0.5 #> 226 -0.1933333 -0.5 #> 227 0.8066667 0.5 #> 228 -0.1933333 -0.5 #> 229 -0.1933333 -0.5 #> 230 -0.1933333 -0.5 #> 231 0.8066667 -0.5 #> 232 -0.1933333 -0.5 #> 233 -0.1933333 -0.5 #> 234 -0.1933333 -0.5 #> 235 -0.1933333 -0.5 #> 236 -0.1933333 -0.5 #> 237 0.8066667 0.5 #> 238 0.8066667 -0.5 #> 239 -0.1933333 -0.5 #> 240 0.8066667 0.5 #> 241 0.8066667 0.5 #> 242 -0.1933333 0.5 #> 243 -0.1933333 -0.5 #> 244 -0.1933333 -0.5 #> 245 -0.1933333 -0.5 #> 246 0.8066667 0.5 #> 247 -0.1933333 0.5 #> 248 0.8066667 0.5 #> 249 0.8066667 -0.5 #> 250 -0.1933333 0.5 #> 251 -0.1933333 -0.5 #> 252 -0.1933333 -0.5 #> 253 0.8066667 0.5 #> 254 0.8066667 0.5 #> 255 -0.1933333 0.5 #> 256 -0.1933333 -0.5 #> 257 -0.1933333 0.5 #> 258 0.8066667 0.5 #> 259 0.8066667 -0.5 #> 260 -0.1933333 -0.5 #> 261 0.8066667 -0.5 #> 262 -0.1933333 0.5 #> 263 -0.1933333 0.5 #> 264 -0.1933333 0.5 #> 265 0.8066667 0.5 #> 266 0.8066667 -0.5 #> 267 -0.1933333 -0.5 #> 268 0.8066667 0.5 #> 269 0.8066667 -0.5 #> 270 -0.1933333 -0.5 #> 271 0.8066667 0.5 #> 272 -0.1933333 0.5 #> 273 -0.1933333 0.5 #> 274 0.8066667 -0.5 #> 275 0.8066667 -0.5 #> 276 0.8066667 -0.5 #> 277 -0.1933333 -0.5 #> 278 0.8066667 -0.5 #> 279 0.8066667 -0.5 #> 280 -0.1933333 0.5 #> 281 -0.1933333 0.5 #> 282 -0.1933333 -0.5 #> 283 -0.1933333 -0.5 #> 284 0.8066667 0.5 #> 285 0.8066667 0.5 #> 286 -0.1933333 -0.5 #> 287 0.8066667 0.5 #> 288 -0.1933333 -0.5 #> 289 -0.1933333 0.5 #> 290 -0.1933333 0.5 #> 291 0.8066667 0.5 #> 292 0.8066667 -0.5 #> 293 -0.1933333 0.5 #> 294 -0.1933333 0.5 #> 295 0.8066667 0.5 #> 296 -0.1933333 -0.5 #> 297 0.8066667 0.5 #> 298 -0.1933333 0.5 #> 299 -0.1933333 -0.5 #> 300 -0.1933333 0.5 #> 301 -0.1933333 -0.5 #> 302 -0.1933333 -0.5 #> 303 -0.1933333 0.5 #> 304 -0.1933333 0.5 #> 305 -0.1933333 -0.5 #> 306 -0.1933333 0.5 #> 307 -0.1933333 0.5 #> 308 -0.1933333 -0.5 #> 309 -0.1933333 -0.5 #> 310 -0.1933333 0.5 #> 311 -0.1933333 0.5 #> 312 -0.1933333 -0.5 #> 313 -0.1933333 0.5 #> 314 -0.1933333 -0.5 #> 315 -0.1933333 -0.5 #> 316 -0.1933333 0.5 #> 317 -0.1933333 0.5 #> 318 0.8066667 -0.5 #> 319 -0.1933333 -0.5 #> 320 -0.1933333 -0.5 #> 321 -0.1933333 -0.5 #> 322 -0.1933333 -0.5 #> 323 0.8066667 0.5 #> 324 -0.1933333 0.5 #> 325 -0.1933333 -0.5 #> 326 -0.1933333 -0.5 #> 327 -0.1933333 0.5 #> 328 -0.1933333 -0.5 #> 329 -0.1933333 -0.5 #> 330 0.8066667 0.5 #> 331 -0.1933333 0.5 #> 332 -0.1933333 -0.5 #> 333 -0.1933333 -0.5 #> 334 0.8066667 -0.5 #> 335 0.8066667 0.5 #> 336 -0.1933333 0.5 #> 337 -0.1933333 0.5 #> 338 0.8066667 0.5 #> 339 -0.1933333 -0.5 #> 340 -0.1933333 0.5 #> 341 -0.1933333 -0.5 #> 342 -0.1933333 -0.5 #> 343 -0.1933333 -0.5 #> 344 -0.1933333 -0.5 #> 345 -0.1933333 -0.5 #> 346 -0.1933333 0.5 #> 347 -0.1933333 0.5 #> 348 -0.1933333 -0.5 #> 349 0.8066667 -0.5 #> 350 -0.1933333 0.5 #> 351 -0.1933333 0.5 #> 352 -0.1933333 0.5 #> 353 -0.1933333 0.5 #> 354 -0.1933333 0.5 #> 355 -0.1933333 -0.5 #> 356 -0.1933333 -0.5 #> 357 0.8066667 -0.5 #> 358 0.8066667 -0.5 #> 359 0.8066667 -0.5 #> 360 -0.1933333 -0.5 #> 361 -0.1933333 0.5 #> 362 -0.1933333 0.5 #> 363 -0.1933333 -0.5 #> 364 -0.1933333 0.5 #> 365 -0.1933333 -0.5 #> 366 -0.1933333 0.5 #> 367 -0.1933333 -0.5 #> 368 -0.1933333 0.5 #> 369 -0.1933333 0.5 #> 370 -0.1933333 0.5 #> 371 -0.1933333 -0.5 #> 372 -0.1933333 0.5 #> 373 -0.1933333 0.5 #> 374 0.8066667 0.5 #> 375 -0.1933333 0.5 #> 376 -0.1933333 0.5 #> 377 -0.1933333 0.5 #> 378 0.8066667 0.5 #> 379 -0.1933333 -0.5 #> 380 -0.1933333 -0.5 #> 381 0.8066667 -0.5 #> 382 -0.1933333 -0.5 #> 383 -0.1933333 0.5 #> 384 0.8066667 0.5 #> 385 -0.1933333 0.5 #> 386 -0.1933333 -0.5 #> 387 0.8066667 -0.5 #> 388 0.8066667 0.5 #> 389 -0.1933333 -0.5 #> 390 -0.1933333 -0.5 #> 391 -0.1933333 -0.5 #> 392 -0.1933333 0.5 #> 393 0.8066667 0.5 #> 394 -0.1933333 0.5 #> 395 -0.1933333 -0.5 #> 396 -0.1933333 0.5 #> 397 -0.1933333 0.5 #> 398 -0.1933333 -0.5 #> 399 -0.1933333 0.5 #> 400 0.8066667 0.5 #> 401 -0.1933333 -0.5 #> 402 0.8066667 0.5 #> 403 -0.1933333 0.5 #> 404 -0.1933333 0.5 #> 405 -0.1933333 0.5 #> 406 -0.1933333 0.5 #> 407 -0.1933333 0.5 #> 408 0.8066667 -0.5 #> 409 0.8066667 -0.5 #> 410 -0.1933333 0.5 #> 411 -0.1933333 0.5 #> 412 -0.1933333 -0.5 #> 413 0.8066667 0.5 #> 414 -0.1933333 -0.5 #> 415 -0.1933333 -0.5 #> 416 -0.1933333 0.5 #> 417 0.8066667 0.5 #> 418 -0.1933333 -0.5 #> 419 0.8066667 0.5 #> 420 0.8066667 -0.5 #> 421 -0.1933333 -0.5 #> 422 -0.1933333 0.5 #> 423 0.8066667 -0.5 #> 424 0.8066667 0.5 #> 425 -0.1933333 -0.5 #> 426 -0.1933333 -0.5 #> 427 0.8066667 -0.5 #> 428 0.8066667 -0.5 #> 429 -0.1933333 0.5 #> 430 0.8066667 0.5 #> 431 -0.1933333 -0.5 #> 432 0.8066667 -0.5 #> 433 0.8066667 -0.5 #> 434 -0.1933333 -0.5 #> 435 -0.1933333 -0.5 #> 436 -0.1933333 -0.5 #> 437 -0.1933333 -0.5 #> 438 -0.1933333 -0.5 #> 439 -0.1933333 -0.5 #> 440 -0.1933333 -0.5 #> 441 -0.1933333 0.5 #> 442 -0.1933333 0.5 #> 443 -0.1933333 0.5 #> 444 0.8066667 0.5 #> 445 -0.1933333 0.5 #> 446 -0.1933333 0.5 #> 447 0.8066667 0.5 #> 448 -0.1933333 0.5 #> 449 -0.1933333 0.5 #> 450 -0.1933333 0.5 #> 451 -0.1933333 -0.5 #> 452 0.8066667 -0.5 #> 453 0.8066667 0.5 #> 454 -0.1933333 0.5 #> 455 -0.1933333 0.5 #> 456 -0.1933333 0.5 #> 457 -0.1933333 0.5 #> 458 -0.1933333 0.5 #> 459 0.8066667 0.5 #> 460 0.8066667 0.5 #> 461 -0.1933333 0.5 #> 462 -0.1933333 0.5 #> 463 -0.1933333 -0.5 #> 464 0.8066667 -0.5 #> 465 0.8066667 -0.5 #> 466 -0.1933333 0.5 #> 467 -0.1933333 0.5 #> 468 -0.1933333 0.5 #> 469 -0.1933333 0.5 #> 470 -0.1933333 0.5 #> 471 -0.1933333 -0.5 #> 472 0.8066667 0.5 #> 473 0.8066667 -0.5 #> 474 -0.1933333 0.5 #> 475 0.8066667 0.5 #> 476 -0.1933333 0.5 #> 477 -0.1933333 0.5 #> 478 0.8066667 0.5 #> 479 -0.1933333 -0.5 #> 480 -0.1933333 0.5 #> 481 -0.1933333 -0.5 #> 482 -0.1933333 0.5 #> 483 -0.1933333 -0.5 #> 484 -0.1933333 -0.5 #> 485 -0.1933333 -0.5 #> 486 -0.1933333 -0.5 #> 487 -0.1933333 0.5 #> 488 0.8066667 -0.5 #> 489 0.8066667 0.5 #> 490 -0.1933333 0.5 #> 491 0.8066667 -0.5 #> 492 0.8066667 0.5 #> 493 0.8066667 -0.5 #> 494 -0.1933333 -0.5 #> 495 -0.1933333 0.5 #> 496 -0.1933333 0.5 #> 497 0.8066667 0.5 #> 498 0.8066667 -0.5 #> 499 -0.1933333 -0.5 #> 500 0.8066667 0.5 centered_colnames <- grep(\"_CENTERED$\", colnames(centered_ipd_sat), value = TRUE) weighted_data <- estimate_weights(data = centered_ipd_sat, centered_colnames = centered_colnames) weighted_data2 <- estimate_weights( data = centered_ipd_sat, centered_colnames = centered_colnames, n_boot_iteration = 500 ) # get dummy binary IPD pseudo_adrs <- get_pseudo_ipd_binary( binary_agd = data.frame( ARM = rep(\"B\", 2), RESPONSE = c(\"YES\", \"NO\"), COUNT = c(280, 120) ), format = \"stacked\" ) # unanchored binary MAIC, with CI based on sandwich estimator maic_unanchored( weights_object = weighted_data, ipd = adrs_sat, pseudo_ipd = pseudo_adrs, trt_ipd = \"A\", trt_agd = \"B\", trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_type = \"binary\", endpoint_name = \"Binary Endpoint\", eff_measure = \"RR\", # binary specific args binary_robust_cov_type = \"HC3\" ) #> Waiting for profiling to be done... #> $descriptive #> list() #> #> $inferential #> $inferential$model_before #> #> Call: glm(formula = RESPONSE ~ ARM, family = glm_link, data = dat) #> #> Coefficients: #> (Intercept) ARMA #> -0.3567 0.1082 #> #> Degrees of Freedom: 899 Total (i.e. Null); 898 Residual #> Null Deviance:\t 395.5 #> Residual Deviance: 393.5 \tAIC: 1738 #> #> $inferential$model_after #> #> Call: glm(formula = RESPONSE ~ ARM, family = glm_link, data = dat, #> weights = weights) #> #> Coefficients: #> (Intercept) ARMA #> -0.35667 0.05611 #> #> Degrees of Freedom: 899 Total (i.e. Null); 898 Residual #> Null Deviance:\t 277.2 #> Residual Deviance: 276.9 \tAIC: 1098 #> #> $inferential$report_overall_robustCI #> Matching treatment N n.events(%) RR[95% CI] #> A Before matching/Binary Endpoint A 500 390(78.0) 1.11[0.96;1.30] #> B B 400 280(70.0) #> A1 After matching/Binary Endpoint A 500 128.3(25.7) 1.06[0.94;1.19] #> B1 B 400 280(70.0) #> p-Value #> A 0.167 #> B #> A1 0.367 #> B1 #> #> # unanchored binary MAIC, with bootstrapped CI maic_unanchored( weights_object = weighted_data2, ipd = adrs_sat, pseudo_ipd = pseudo_adrs, trt_ipd = \"A\", trt_agd = \"B\", trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_type = \"binary\", endpoint_name = \"Binary Endpoint\", eff_measure = \"RR\", # binary specific args binary_robust_cov_type = \"HC3\" ) #> Waiting for profiling to be done... #> Waiting for profiling to be done... #> $descriptive #> list() #> #> $inferential #> $inferential$model_before #> #> Call: glm(formula = RESPONSE ~ ARM, family = glm_link, data = dat) #> #> Coefficients: #> (Intercept) ARMA #> -0.3567 0.1082 #> #> Degrees of Freedom: 899 Total (i.e. Null); 898 Residual #> Null Deviance:\t 395.5 #> Residual Deviance: 393.5 \tAIC: 1738 #> #> $inferential$model_after #> #> Call: glm(formula = RESPONSE ~ ARM, family = glm_link, data = dat, #> weights = weights) #> #> Coefficients: #> (Intercept) ARMA #> -0.35667 0.05611 #> #> Degrees of Freedom: 899 Total (i.e. Null); 898 Residual #> Null Deviance:\t 277.2 #> Residual Deviance: 276.9 \tAIC: 1098 #> #> $inferential$boot_est #> #> ORDINARY NONPARAMETRIC BOOTSTRAP #> #> #> Call: #> boot(data = boot_ipd, statistic = stat_fun, R = R, w_obj = weights_object, #> pseudo_ipd = pseudo_ipd) #> #> #> Bootstrap Statistics : #> original bias std. error #> t1* 0.05611408 0.0022793426 0.0513740040 #> t2* 0.01136433 0.0002412849 0.0008190764 #> #> $inferential$report_overall_robustCI #> Matching treatment N n.events(%) RR[95% CI] #> A Before matching/Binary Endpoint A 500 390(78.0) 1.11[0.96;1.30] #> B B 400 280(70.0) #> A1 After matching/Binary Endpoint A 500 128.3(25.7) 1.06[0.94;1.19] #> B1 B 400 280(70.0) #> p-Value #> A 0.167 #> B #> A1 0.367 #> B1 #> #> $inferential$report_overall_bootCI #> Matching treatment N n.events(%) RR[95% CI] #> A Before matching/Binary Endpoint A 500 390(78.0) 1.11[0.96;1.30] #> B B 400 280(70.0) #> A1 After matching/Binary Endpoint A 500 128.3(25.7) 1.06[0.95;1.17] #> B1 B 400 280(70.0) #> p-Value #> A 0.167 #> B #> A1 NA #> B1 #> #>"},{"path":"https://hta-pharma.github.io/maicplus/reference/maicplus-package.html","id":null,"dir":"Reference","previous_headings":"","what":"maicplus: Matching Adjusted Indirect Comparison — maicplus-package","title":"maicplus: Matching Adjusted Indirect Comparison — maicplus-package","text":"maicplus package facilitates performing matching adjusted indirect comparison (MAIC) analysis endpoint interest either time--event (e.g. overall survival) binary (e.g. objective tumor response).","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/reference/maicplus-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"maicplus: Matching Adjusted Indirect Comparison — maicplus-package","text":"Maintainer: hta-pharma hta-pharma@example.com","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/medSurv_makeup.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","title":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","text":"Extract display median survival time confidence interval","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/medSurv_makeup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","text":"","code":"medSurv_makeup(km_fit, legend = \"before matching\", time_scale)"},{"path":"https://hta-pharma.github.io/maicplus/reference/medSurv_makeup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","text":"km_fit returned object survival::survfit legend character string, name used 'type' column returned data frame time_scale character string, 'years', 'months', 'weeks' 'days', time unit median survival time","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/medSurv_makeup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","text":"data frame index column 'type', median survival time confidence interval","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/medSurv_makeup.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","text":"","code":"data(adtte_sat) data(pseudo_ipd_sat) library(survival) combined_data <- rbind(adtte_sat[, c(\"TIME\", \"EVENT\", \"ARM\")], pseudo_ipd_sat) kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data, conf.type = \"log-log\") # Derive median survival time medSurv <- medSurv_makeup(kmobj, legend = \"before matching\", time_scale = \"day\") medSurv #> treatment type records n.max n.start events rmean se(rmean) #> 1 ARM=A before matching 500 500 500 190 265.1012 10.80981 #> 2 ARM=B before matching 300 300 300 178 130.9893 10.24910 #> median 0.95LCL 0.95UCL #> 1 230.94839 191.10767 313.1574 #> 2 83.58535 68.82298 101.0786"},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnosis plot of proportional hazard assumption for anchored and unanchored — ph_diagplot","title":"Diagnosis plot of proportional hazard assumption for anchored and unanchored — ph_diagplot","text":"Diagnosis plot proportional hazard assumption anchored unanchored","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnosis plot of proportional hazard assumption for anchored and unanchored — ph_diagplot","text":"","code":"ph_diagplot( weights_object, tte_ipd, tte_pseudo_ipd, trt_ipd, trt_agd, trt_common = NULL, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_name = \"Time to Event Endpoint\", time_scale, zph_transform = \"log\", zph_log_hazard = TRUE )"},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnosis plot of proportional hazard assumption for anchored and unanchored — ph_diagplot","text":"weights_object object returned estimate_weight tte_ipd data frame individual patient data (IPD) internal trial, contain least \"USUBJID\", \"EVENT\", \"TIME\" columns column indicating treatment assignment tte_pseudo_ipd data frame pseudo IPD digitized KM curves external trial (time--event endpoint), contain least \"EVENT\", \"TIME\" trt_ipd string, name interested investigation arm internal trial tte_ipd (real IPD) trt_agd string, name interested investigation arm external trial tte_pseudo_ipd (pseudo IPD) trt_common string, name common comparator internal external trial, default NULL, indicating unanchored case trt_var_ipd string, column name tte_ipd contains treatment assignment trt_var_agd string, column name tte_pseudo_ipd contains treatment assignment endpoint_name string, name time event endpoint, show last line title time_scale string, time unit median survival time, taking value 'years', 'months', 'weeks' 'days' zph_transform string, pass survival::cox.zph, default \"log\" zph_log_hazard logical, TRUE (default), y axis time dependent hazard function log-hazard, otherwise, hazard.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnosis plot of proportional hazard assumption for anchored and unanchored — ph_diagplot","text":"3 2 plot, include log-cumulative hazard plot, time dependent hazard function unscaled Schoenfeld residual plot, matching","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diagnosis plot of proportional hazard assumption for anchored and unanchored — ph_diagplot","text":"","code":"# unanchored example using ph_diagplot data(weighted_sat) data(adtte_sat) data(pseudo_ipd_sat) ph_diagplot( weights_object = weighted_sat, tte_ipd = adtte_sat, tte_pseudo_ipd = pseudo_ipd_sat, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", trt_ipd = \"A\", trt_agd = \"B\", trt_common = NULL, endpoint_name = \"Overall Survival\", time_scale = \"week\", zph_transform = \"log\", zph_log_hazard = TRUE ) # anchored example using ph_diagplot data(weighted_twt) data(adtte_twt) data(pseudo_ipd_twt) ph_diagplot( weights_object = weighted_twt, tte_ipd = adtte_twt, tte_pseudo_ipd = pseudo_ipd_twt, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", trt_ipd = \"A\", trt_agd = \"B\", trt_common = \"C\", endpoint_name = \"Overall Survival\", time_scale = \"week\", zph_transform = \"log\", zph_log_hazard = TRUE )"},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot_lch.html","id":null,"dir":"Reference","previous_headings":"","what":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"plot also known log negative log survival rate.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot_lch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"","code":"ph_diagplot_lch( km_fit, time_scale, log_time = TRUE, endpoint_name = \"\", subtitle = \"\", exclude_censor = TRUE )"},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot_lch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"km_fit returned object survival::survfit time_scale character string, 'years', 'months', 'weeks' 'days', time unit median survival time log_time logical, TRUE (default) FALSE endpoint_name character string, name endpoint subtitle character string, subtitle plot exclude_censor logical, censored data point plotted","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot_lch.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"plot log cumulative hazard rate","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot_lch.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"diagnosis plot proportional hazard assumption, versus log-time (default) time","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot_lch.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"","code":"library(survival) data(adtte_sat) data(pseudo_ipd_sat) combined_data <- rbind(adtte_sat[, c(\"TIME\", \"EVENT\", \"ARM\")], pseudo_ipd_sat) kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data, conf.type = \"log-log\") ph_diagplot_lch(kmobj, time_scale = \"month\", log_time = TRUE, endpoint_name = \"OS\", subtitle = \"(Before Matching)\" )"},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot_schoenfeld.html","id":null,"dir":"Reference","previous_headings":"","what":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","title":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","text":"PH Diagnosis Plot Schoenfeld residuals Cox model fit","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot_schoenfeld.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","text":"","code":"ph_diagplot_schoenfeld( coxobj, time_scale = \"months\", log_time = TRUE, endpoint_name = \"\", subtitle = \"\" )"},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot_schoenfeld.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","text":"coxobj object returned coxph time_scale character string, 'years', 'months', 'weeks' 'days', time unit median survival time log_time logical, TRUE (default) FALSE endpoint_name character string, name endpoint subtitle character string, subtitle plot","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot_schoenfeld.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","text":"plot Schoenfeld residuals","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot_schoenfeld.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","text":"","code":"library(survival) data(adtte_sat) data(pseudo_ipd_sat) combined_data <- rbind(adtte_sat[, c(\"TIME\", \"EVENT\", \"ARM\")], pseudo_ipd_sat) unweighted_cox <- coxph(Surv(TIME, EVENT == 1) ~ ARM, data = combined_data) ph_diagplot_schoenfeld(unweighted_cox, time_scale = \"month\", log_time = TRUE, endpoint_name = \"OS\", subtitle = \"(Before Matching)\" )"},{"path":"https://hta-pharma.github.io/maicplus/reference/plot_weights_base.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot MAIC weights in a histogram with key statistics in legend — plot_weights_base","title":"Plot MAIC weights in a histogram with key statistics in legend — plot_weights_base","text":"Generates base R histogram weights. Default plot either unscaled scaled weights .","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/plot_weights_base.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot MAIC weights in a histogram with key statistics in legend — plot_weights_base","text":"","code":"plot_weights_base( weighted_data, bin_col, vline_col, main_title, scaled_weights )"},{"path":"https://hta-pharma.github.io/maicplus/reference/plot_weights_base.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot MAIC weights in a histogram with key statistics in legend — plot_weights_base","text":"weighted_data object returned calculating weights using estimate_weights bin_col string, color bins histogram vline_col string, color vertical line histogram main_title title plot scaled_weights indicator using scaled weights instead regular weights","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/plot_weights_base.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot MAIC weights in a histogram with key statistics in legend — plot_weights_base","text":"plot unscaled scaled weights","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/plot_weights_ggplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot MAIC weights in a histogram with key statistics in legend using ggplot2 — plot_weights_ggplot","title":"Plot MAIC weights in a histogram with key statistics in legend using ggplot2 — plot_weights_ggplot","text":"Generates ggplot histogram weights. Default plot unscaled scaled weights graph.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/plot_weights_ggplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot MAIC weights in a histogram with key statistics in legend using ggplot2 — plot_weights_ggplot","text":"","code":"plot_weights_ggplot(weighted_data, bin_col, vline_col, main_title, bins)"},{"path":"https://hta-pharma.github.io/maicplus/reference/plot_weights_ggplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot MAIC weights in a histogram with key statistics in legend using ggplot2 — plot_weights_ggplot","text":"weighted_data object returned calculating weights using estimate_weights bin_col string, color bins histogram vline_col string, color vertical line histogram main_title Name scaled weights plot unscaled weights plot, respectively. bins number bin parameter use","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/plot_weights_ggplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot MAIC weights in a histogram with key statistics in legend using ggplot2 — plot_weights_ggplot","text":"plot unscaled scaled weights","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/process_agd.html","id":null,"dir":"Reference","previous_headings":"","what":"Pre-process aggregate data — process_agd","title":"Pre-process aggregate data — process_agd","text":"function checks format aggregate data. Data required three columns: STUDY, ARM, N. Column names legal suffixes (MEAN, MEDIAN, SD, COUNT, PROP) dropped. variable count variable, converted proportions dividing sample size (N). Note, count specified, proportion always calculated based count, , specified proportion ignored applicable. aggregated data comes multiple sources (.e. different analysis population) sample size differs variable, one option specify proportion directly instead count using suffix _PROP.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/process_agd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pre-process aggregate data — process_agd","text":"","code":"process_agd(raw_agd)"},{"path":"https://hta-pharma.github.io/maicplus/reference/process_agd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pre-process aggregate data — process_agd","text":"raw_agd raw aggregate data contain STUDY, ARM, N. Variable names followed legal suffixes (.e. MEAN, MEDIAN, SD, COUNT, PROP).","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/process_agd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pre-process aggregate data — process_agd","text":"pre-processed aggregate level data","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/process_agd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pre-process aggregate data — process_agd","text":"","code":"data(agd) agd <- process_agd(agd)"},{"path":"https://hta-pharma.github.io/maicplus/reference/pseudo_ipd_sat.html","id":null,"dir":"Reference","previous_headings":"","what":"Pseudo individual patient survival data from published study — pseudo_ipd_sat","title":"Pseudo individual patient survival data from published study — pseudo_ipd_sat","text":"Pseudo individual patient survival data published study","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/pseudo_ipd_sat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pseudo individual patient survival data from published study — pseudo_ipd_sat","text":"","code":"pseudo_ipd_sat"},{"path":"https://hta-pharma.github.io/maicplus/reference/pseudo_ipd_sat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pseudo individual patient survival data from published study — pseudo_ipd_sat","text":"data frame 300 rows 3 columns: TIME Survival time days. EVENT Event indicator 0/1. ARM Assigned treatment arm, \"B\".","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/reference/pseudo_ipd_twt.html","id":null,"dir":"Reference","previous_headings":"","what":"Pseudo individual patient survival data from published two arm study — pseudo_ipd_twt","title":"Pseudo individual patient survival data from published two arm study — pseudo_ipd_twt","text":"Pseudo individual patient survival data published two arm study","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/pseudo_ipd_twt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pseudo individual patient survival data from published two arm study — pseudo_ipd_twt","text":"","code":"pseudo_ipd_twt"},{"path":"https://hta-pharma.github.io/maicplus/reference/pseudo_ipd_twt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pseudo individual patient survival data from published two arm study — pseudo_ipd_twt","text":"data frame 800 rows 3 columns: TIME Survival time days. EVENT Event indicator 0/1. ARM Assigned treatment arm, \"B\", \"C\".","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/reference/reformat.html","id":null,"dir":"Reference","previous_headings":"","what":"Reformat maicplus_bucher alike object — reformat","title":"Reformat maicplus_bucher alike object — reformat","text":"Reformat maicplus_bucher alike object","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/reformat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reformat maicplus_bucher alike object — reformat","text":"","code":"reformat( x, ci_digits = 2, pval_digits = 3, show_pval = TRUE, exponentiate = FALSE )"},{"path":"https://hta-pharma.github.io/maicplus/reference/reformat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reformat maicplus_bucher alike object — reformat","text":"x list, structured like maicplus_bucher object ci_digits integer, number decimal places point estimate derived confidence limits pval_digits integer, number decimal places display Z-test p-value show_pval logical value, default TRUE. FALSE, p-value output second element character vector exponentiate whether treatment effect confidence interval exponentiated. applies relative treatment effects. Default set false.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/report_table_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"helper function: sort out a nice report table to summarize binary analysis results — report_table_binary","title":"helper function: sort out a nice report table to summarize binary analysis results — report_table_binary","text":"helper function: sort nice report table summarize binary analysis results","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/report_table_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"helper function: sort out a nice report table to summarize binary analysis results — report_table_binary","text":"","code":"report_table_binary( binobj, weighted_result = NULL, eff_measure = c(\"OR\", \"RD\", \"RR\"), tag = NULL )"},{"path":"https://hta-pharma.github.io/maicplus/reference/report_table_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"helper function: sort out a nice report table to summarize binary analysis results — report_table_binary","text":"binobj object glm() weighted_result weighted result object eff_measure string, binary effect measure, \"\", \"RR\", \"RD\" tag string, default NULL, specified, extra 1st column created output","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/report_table_binary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"helper function: sort out a nice report table to summarize binary analysis results — report_table_binary","text":"data frame sample size, incidence rate, estimate binary effect measure 95% CI Wald test hazard ratio","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/report_table_binary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"helper function: sort out a nice report table to summarize binary analysis results — report_table_binary","text":"","code":"data(adrs_sat) testdat <- data.frame(Yes = 280, No = 120) rownames(testdat) <- \"B\" pseudo_ipd_binary_sat <- get_pseudo_ipd_binary( binary_agd = testdat, format = \"unstacked\" ) combined_data <- rbind(adrs_sat[, c(\"USUBJID\", \"RESPONSE\", \"ARM\")], pseudo_ipd_binary_sat) combined_data$ARM <- as.factor(combined_data$ARM) binobj_dat <- glm(RESPONSE ~ ARM, combined_data, family = binomial(link = \"logit\")) report_table_binary(binobj_dat, eff_measure = \"OR\") #> Waiting for profiling to be done... #> treatment N n.events(%) OR[95% CI] p-Value #> B B 400 280(70.0) 0.66[0.49;0.89] 0.006 #> A A 500 390(78.0)"},{"path":"https://hta-pharma.github.io/maicplus/reference/report_table_tte.html","id":null,"dir":"Reference","previous_headings":"","what":"helper function: sort out a nice report table to summarize survival analysis results — report_table_tte","title":"helper function: sort out a nice report table to summarize survival analysis results — report_table_tte","text":"helper function: sort nice report table summarize survival analysis results","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/report_table_tte.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"helper function: sort out a nice report table to summarize survival analysis results — report_table_tte","text":"","code":"report_table_tte(coxobj, medSurvobj, tag = NULL)"},{"path":"https://hta-pharma.github.io/maicplus/reference/report_table_tte.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"helper function: sort out a nice report table to summarize survival analysis results — report_table_tte","text":"coxobj returned object coxph medSurvobj returned object medSurv_makeup tag string, default NULL, specified, extra 1st column created output","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/report_table_tte.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"helper function: sort out a nice report table to summarize survival analysis results — report_table_tte","text":"data frame sample size, incidence rate, median survival time 95% CI, hazard ratio estimate 95% CI Wald test hazard ratio","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/report_table_tte.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"helper function: sort out a nice report table to summarize survival analysis results — report_table_tte","text":"","code":"library(survival) data(adtte_sat) data(pseudo_ipd_sat) combined_data <- rbind(adtte_sat[, c(\"TIME\", \"EVENT\", \"ARM\")], pseudo_ipd_sat) unweighted_cox <- coxph(Surv(TIME, EVENT == 1) ~ ARM, data = combined_data) # Derive median survival time kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data, conf.type = \"log-log\") medSurv <- medSurv_makeup(kmobj, legend = \"before matching\", time_scale = \"day\") report_table_tte(unweighted_cox, medSurv) #> treatment N n.events(%) median[95% CI] HR[95% CI] p-Value #> 2 ARM=B 300 178(59.3) 83.6[ 68.8;101.1] 2.67[2.16;3.29] <0.001 #> 1 ARM=A 500 190(38.0) 230.9[191.1;313.2]"},{"path":"https://hta-pharma.github.io/maicplus/reference/survfit_makeup.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","title":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","text":"Helper function select set variables used Kaplan-Meier plot","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/survfit_makeup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","text":"","code":"survfit_makeup(km_fit, single_trt_name = \"treatment\")"},{"path":"https://hta-pharma.github.io/maicplus/reference/survfit_makeup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","text":"km_fit returned object survival::survfit single_trt_name name treatment strata specified km_fit","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/survfit_makeup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","text":"list data frames variables survival::survfit(). Data frame divided treatment.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/survfit_makeup.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","text":"","code":"library(survival) data(adtte_sat) data(pseudo_ipd_sat) combined_data <- rbind(adtte_sat[, c(\"TIME\", \"EVENT\", \"ARM\")], pseudo_ipd_sat) kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data, conf.type = \"log-log\") survfit_makeup(kmobj) #> $A #> time treatment n.risk n.event censor surv lower upper #> 1 0.2046028 A 500 0 1 1.0000000 NA NA #> 2 0.3321626 A 499 1 0 0.9979960 0.9858600 0.9997175 #> 3 0.4303082 A 498 1 0 0.9959920 0.9840702 0.9989961 #> 4 1.2688996 A 497 1 0 0.9939880 0.9814767 0.9980570 #> 5 1.2808132 A 496 0 1 0.9939880 0.9814767 0.9980570 #> 6 1.9459751 A 495 1 0 0.9919799 0.9787735 0.9969824 #> 7 2.0300958 A 494 0 1 0.9919799 0.9787735 0.9969824 #> 8 2.4121713 A 493 0 1 0.9919799 0.9787735 0.9969824 #> 9 2.6752029 A 492 1 0 0.9899637 0.9760567 0.9958104 #> 10 2.9969434 A 491 0 1 0.9899637 0.9760567 0.9958104 #> 11 3.2894058 A 490 0 1 0.9899637 0.9760567 0.9958104 #> 12 3.3942755 A 489 1 0 0.9879392 0.9733519 0.9945636 #> 13 3.8751813 A 488 0 1 0.9879392 0.9733519 0.9945636 #> 14 4.0793628 A 487 0 1 0.9879392 0.9733519 0.9945636 #> 15 4.2866692 A 486 0 1 0.9879392 0.9733519 0.9945636 #> 16 4.6245131 A 485 0 1 0.9879392 0.9733519 0.9945636 #> 17 5.1546502 A 484 1 0 0.9858980 0.9706473 0.9932524 #> 18 5.4278376 A 483 0 1 0.9858980 0.9706473 0.9932524 #> 19 5.5735189 A 482 0 1 0.9858980 0.9706473 0.9932524 #> 20 5.8283381 A 481 1 0 0.9838484 0.9679619 0.9918902 #> 21 6.5182421 A 480 0 1 0.9838484 0.9679619 0.9918902 #> 22 6.8416124 A 479 1 0 0.9817944 0.9653018 0.9904863 #> 23 6.8633253 A 478 0 1 0.9817944 0.9653018 0.9904863 #> 24 7.2337488 A 477 1 0 0.9797361 0.9626636 0.9890463 #> 25 7.4378994 A 476 1 0 0.9776779 0.9600523 0.9875768 #> 26 8.3675931 A 475 1 0 0.9756196 0.9574650 0.9860816 #> 27 8.3998372 A 474 0 1 0.9756196 0.9574650 0.9860816 #> 28 8.6078129 A 473 1 0 0.9735570 0.9548917 0.9845610 #> 29 8.7321192 A 472 0 1 0.9735570 0.9548917 0.9845610 #> 30 8.7683064 A 471 0 1 0.9735570 0.9548917 0.9845610 #> 31 9.1691822 A 470 0 1 0.9735570 0.9548917 0.9845610 #> 32 9.3282041 A 469 0 1 0.9735570 0.9548917 0.9845610 #> 33 10.2292442 A 468 0 1 0.9735570 0.9548917 0.9845610 #> 34 10.2380585 A 467 1 0 0.9714723 0.9523009 0.9830067 #> 35 10.2701408 A 466 0 1 0.9714723 0.9523009 0.9830067 #> 36 10.3618326 A 465 0 1 0.9714723 0.9523009 0.9830067 #> 37 10.3717562 A 464 1 0 0.9693786 0.9497145 0.9814279 #> 38 10.9352310 A 463 0 1 0.9693786 0.9497145 0.9814279 #> 39 11.1663130 A 462 1 0 0.9672803 0.9471387 0.9798289 #> 40 11.3527364 A 461 1 0 0.9651821 0.9445796 0.9782137 #> 41 12.0051551 A 460 1 0 0.9630839 0.9420357 0.9765837 #> 42 12.1850070 A 459 0 1 0.9630839 0.9420357 0.9765837 #> 43 12.3885075 A 458 0 1 0.9630839 0.9420357 0.9765837 #> 44 12.6075825 A 457 0 1 0.9630839 0.9420357 0.9765837 #> 45 12.6413833 A 456 1 0 0.9609719 0.9394843 0.9749312 #> 46 12.7575511 A 455 1 0 0.9588598 0.9369464 0.9732659 #> 47 12.8976681 A 454 1 0 0.9567478 0.9344210 0.9715885 #> 48 12.9398765 A 453 0 1 0.9567478 0.9344210 0.9715885 #> 49 13.0073322 A 452 0 1 0.9567478 0.9344210 0.9715885 #> 50 13.1747627 A 451 0 1 0.9567478 0.9344210 0.9715885 #> 51 13.2112969 A 450 0 1 0.9567478 0.9344210 0.9715885 #> 52 13.4000154 A 449 1 0 0.9546170 0.9318787 0.9698877 #> 53 13.7869996 A 448 0 1 0.9546170 0.9318787 0.9698877 #> 54 13.8523925 A 447 0 1 0.9546170 0.9318787 0.9698877 #> 55 13.8586577 A 446 0 1 0.9546170 0.9318787 0.9698877 #> 56 14.1516358 A 445 1 0 0.9524718 0.9293264 0.9681666 #> 57 14.6109023 A 444 1 0 0.9503266 0.9267852 0.9664352 #> 58 15.2886098 A 443 0 1 0.9503266 0.9267852 0.9664352 #> 59 15.7821636 A 442 1 0 0.9481765 0.9242474 0.9646908 #> 60 15.8151577 A 441 0 1 0.9481765 0.9242474 0.9646908 #> 61 15.8302898 A 440 1 0 0.9460216 0.9217123 0.9629339 #> 62 15.8860250 A 439 0 1 0.9460216 0.9217123 0.9629339 #> 63 15.9789179 A 438 0 1 0.9460216 0.9217123 0.9629339 #> 64 16.0028888 A 437 0 1 0.9460216 0.9217123 0.9629339 #> 65 16.1304526 A 436 0 1 0.9460216 0.9217123 0.9629339 #> 66 16.6777043 A 435 1 0 0.9438468 0.9191578 0.9611548 #> 67 17.0752816 A 434 1 0 0.9416720 0.9166127 0.9593672 #> 68 17.1922456 A 433 0 1 0.9416720 0.9166127 0.9593672 #> 69 18.0631542 A 432 0 1 0.9416720 0.9166127 0.9593672 #> 70 18.4767328 A 431 0 1 0.9416720 0.9166127 0.9593672 #> 71 18.6336849 A 430 0 1 0.9416720 0.9166127 0.9593672 #> 72 19.5843537 A 429 0 1 0.9416720 0.9166127 0.9593672 #> 73 19.6747055 A 428 1 0 0.9394719 0.9140400 0.9575539 #> 74 19.9823259 A 427 0 1 0.9394719 0.9140400 0.9575539 #> 75 20.0770245 A 426 1 0 0.9372665 0.9114688 0.9557291 #> 76 20.4779808 A 425 0 1 0.9372665 0.9114688 0.9557291 #> 77 20.9126041 A 424 1 0 0.9350560 0.9088988 0.9538931 #> 78 20.9492488 A 423 0 1 0.9350560 0.9088988 0.9538931 #> 79 20.9775017 A 422 1 0 0.9328402 0.9063296 0.9520460 #> 80 20.9988640 A 421 0 1 0.9328402 0.9063296 0.9520460 #> 81 21.5799492 A 420 1 0 0.9306192 0.9037610 0.9501881 #> 82 21.8873529 A 419 0 1 0.9306192 0.9037610 0.9501881 #> 83 22.6567004 A 418 1 0 0.9283928 0.9011926 0.9483197 #> 84 22.8324762 A 417 1 0 0.9261665 0.8986315 0.9464447 #> 85 23.2247571 A 416 0 1 0.9261665 0.8986315 0.9464447 #> 86 23.2462453 A 415 1 0 0.9239347 0.8960701 0.9445595 #> 87 23.7400288 A 414 0 1 0.9239347 0.8960701 0.9445595 #> 88 25.3781343 A 413 0 1 0.9239347 0.8960701 0.9445595 #> 89 25.5091635 A 412 1 0 0.9216922 0.8935005 0.9426605 #> 90 25.5794699 A 411 1 0 0.9194496 0.8909376 0.9407555 #> 91 25.6490953 A 410 0 1 0.9194496 0.8909376 0.9407555 #> 92 26.3512119 A 409 0 1 0.9194496 0.8909376 0.9407555 #> 93 27.2088851 A 408 0 1 0.9194496 0.8909376 0.9407555 #> 94 28.0451023 A 407 1 0 0.9171905 0.8883585 0.9388328 #> 95 28.2886213 A 406 0 1 0.9171905 0.8883585 0.9388328 #> 96 28.5374060 A 405 0 1 0.9171905 0.8883585 0.9388328 #> 97 29.5678669 A 404 0 1 0.9171905 0.8883585 0.9388328 #> 98 29.5736758 A 403 0 1 0.9171905 0.8883585 0.9388328 #> 99 30.4157094 A 402 1 0 0.9149090 0.8857551 0.9368882 #> 100 30.4968240 A 401 1 0 0.9126274 0.8831581 0.9349379 #> 101 30.7223985 A 400 1 0 0.9103458 0.8805673 0.9329822 #> 102 30.8273271 A 399 1 0 0.9080642 0.8779825 0.9310213 #> 103 30.8717178 A 398 1 0 0.9057827 0.8754033 0.9290553 #> 104 31.4891944 A 397 0 1 0.9057827 0.8754033 0.9290553 #> 105 31.7437313 A 396 0 1 0.9057827 0.8754033 0.9290553 #> 106 32.3195134 A 395 0 1 0.9057827 0.8754033 0.9290553 #> 107 32.8199139 A 394 0 1 0.9057827 0.8754033 0.9290553 #> 108 33.3162513 A 393 0 1 0.9057827 0.8754033 0.9290553 #> 109 33.8249243 A 392 0 1 0.9057827 0.8754033 0.9290553 #> 110 33.8799882 A 391 0 1 0.9057827 0.8754033 0.9290553 #> 111 34.0150132 A 390 1 0 0.9034602 0.8727748 0.9270542 #> 112 34.0826279 A 389 0 1 0.9034602 0.8727748 0.9270542 #> 113 34.0987472 A 388 0 1 0.9034602 0.8727748 0.9270542 #> 114 34.5806822 A 387 1 0 0.9011256 0.8701359 0.9250392 #> 115 34.5820833 A 386 0 1 0.9011256 0.8701359 0.9250392 #> 116 35.6976157 A 385 1 0 0.8987851 0.8674947 0.9230147 #> 117 35.7879285 A 384 0 1 0.8987851 0.8674947 0.9230147 #> 118 35.8691523 A 383 1 0 0.8964384 0.8648510 0.9209809 #> 119 36.0369583 A 382 1 0 0.8940917 0.8622127 0.9189424 #> 120 36.3408479 A 381 1 0 0.8917450 0.8595797 0.9168992 #> 121 37.0950110 A 380 0 1 0.8917450 0.8595797 0.9168992 #> 122 37.1862175 A 379 1 0 0.8893921 0.8569437 0.9148470 #> 123 37.7626359 A 378 1 0 0.8870392 0.8543127 0.9127904 #> 124 37.8814246 A 377 0 1 0.8870392 0.8543127 0.9127904 #> 125 38.5668907 A 376 0 1 0.8870392 0.8543127 0.9127904 #> 126 39.2364700 A 375 1 0 0.8846737 0.8516701 0.9107201 #> 127 39.5099966 A 374 0 1 0.8846737 0.8516701 0.9107201 #> 128 39.6943191 A 373 0 1 0.8846737 0.8516701 0.9107201 #> 129 39.8458329 A 372 0 1 0.8846737 0.8516701 0.9107201 #> 130 39.9270162 A 371 1 0 0.8822892 0.8490072 0.9086311 #> 131 40.1599769 A 370 0 1 0.8822892 0.8490072 0.9086311 #> 132 41.5788383 A 369 0 1 0.8822892 0.8490072 0.9086311 #> 133 41.6267738 A 368 1 0 0.8798917 0.8463323 0.9065281 #> 134 42.1096005 A 367 1 0 0.8774941 0.8436623 0.9044209 #> 135 42.4329399 A 366 0 1 0.8774941 0.8436623 0.9044209 #> 136 42.5864914 A 365 0 1 0.8774941 0.8436623 0.9044209 #> 137 43.0372554 A 364 0 1 0.8774941 0.8436623 0.9044209 #> 138 43.3221132 A 363 0 1 0.8774941 0.8436623 0.9044209 #> 139 43.6389358 A 362 0 1 0.8774941 0.8436623 0.9044209 #> 140 44.1549084 A 361 1 0 0.8750634 0.8409537 0.9022845 #> 141 44.5140716 A 360 0 1 0.8750634 0.8409537 0.9022845 #> 142 45.4720260 A 359 0 1 0.8750634 0.8409537 0.9022845 #> 143 46.1566080 A 358 0 1 0.8750634 0.8409537 0.9022845 #> 144 47.0546355 A 357 0 1 0.8750634 0.8409537 0.9022845 #> 145 48.0823337 A 356 0 1 0.8750634 0.8409537 0.9022845 #> 146 48.3479226 A 355 0 1 0.8750634 0.8409537 0.9022845 #> 147 48.4040249 A 354 0 1 0.8750634 0.8409537 0.9022845 #> 148 48.7539111 A 353 1 0 0.8725845 0.8381871 0.9001073 #> 149 49.2037680 A 352 1 0 0.8701055 0.8354258 0.8979258 #> 150 49.3435304 A 351 1 0 0.8676266 0.8326694 0.8957402 #> 151 49.6663522 A 350 0 1 0.8676266 0.8326694 0.8957402 #> 152 49.8173522 A 349 0 1 0.8676266 0.8326694 0.8957402 #> 153 50.1248807 A 348 0 1 0.8676266 0.8326694 0.8957402 #> 154 50.3150662 A 347 1 0 0.8651262 0.8298901 0.8935341 #> 155 50.6666237 A 346 1 0 0.8626259 0.8271158 0.8913239 #> 156 50.8566442 A 345 1 0 0.8601255 0.8243462 0.8891097 #> 157 51.6937890 A 344 0 1 0.8601255 0.8243462 0.8891097 #> 158 52.2469200 A 343 1 0 0.8576178 0.8215719 0.8868860 #> 159 52.4467296 A 342 1 0 0.8551102 0.8188021 0.8846584 #> 160 52.6613128 A 341 1 0 0.8526025 0.8160368 0.8824271 #> 161 52.7997845 A 340 1 0 0.8500949 0.8132759 0.8801920 #> 162 53.4114635 A 339 1 0 0.8475872 0.8105191 0.8779534 #> 163 53.9061795 A 338 0 1 0.8475872 0.8105191 0.8779534 #> 164 53.9968261 A 337 0 1 0.8475872 0.8105191 0.8779534 #> 165 54.6123914 A 336 0 1 0.8475872 0.8105191 0.8779534 #> 166 54.6861744 A 335 0 1 0.8475872 0.8105191 0.8779534 #> 167 55.0706664 A 334 0 1 0.8475872 0.8105191 0.8779534 #> 168 55.6752080 A 333 1 0 0.8450419 0.8077186 0.8756820 #> 169 56.4060157 A 332 0 1 0.8450419 0.8077186 0.8756820 #> 170 56.6565761 A 331 1 0 0.8424889 0.8049126 0.8734009 #> 171 56.6804001 A 330 0 1 0.8424889 0.8049126 0.8734009 #> 172 56.7837124 A 329 0 1 0.8424889 0.8049126 0.8734009 #> 173 57.1719156 A 328 0 1 0.8424889 0.8049126 0.8734009 #> 174 58.1487885 A 327 1 0 0.8399125 0.8020811 0.8710981 #> 175 58.9000652 A 326 0 1 0.8399125 0.8020811 0.8710981 #> 176 59.3667629 A 325 0 1 0.8399125 0.8020811 0.8710981 #> 177 59.6044136 A 324 0 1 0.8399125 0.8020811 0.8710981 #> 178 60.0808303 A 323 0 1 0.8399125 0.8020811 0.8710981 #> 179 60.2611923 A 322 0 1 0.8399125 0.8020811 0.8710981 #> 180 60.2893293 A 321 0 1 0.8399125 0.8020811 0.8710981 #> 181 60.6304562 A 320 0 1 0.8399125 0.8020811 0.8710981 #> 182 61.1170188 A 319 1 0 0.8372796 0.7991821 0.8687477 #> 183 61.6891086 A 318 0 1 0.8372796 0.7991821 0.8687477 #> 184 61.8689337 A 317 1 0 0.8346383 0.7962771 0.8663871 #> 185 62.6283981 A 316 0 1 0.8346383 0.7962771 0.8663871 #> 186 62.6601429 A 315 0 1 0.8346383 0.7962771 0.8663871 #> 187 63.5048707 A 314 0 1 0.8346383 0.7962771 0.8663871 #> 188 63.9452407 A 313 0 1 0.8346383 0.7962771 0.8663871 #> 189 64.6872731 A 312 0 1 0.8346383 0.7962771 0.8663871 #> 190 66.0390115 A 311 0 1 0.8346383 0.7962771 0.8663871 #> 191 66.0441764 A 310 0 1 0.8346383 0.7962771 0.8663871 #> 192 66.0733700 A 309 0 1 0.8346383 0.7962771 0.8663871 #> 193 67.3416893 A 308 1 0 0.8319284 0.7932893 0.8639695 #> 194 67.4707314 A 307 1 0 0.8292186 0.7903065 0.8615479 #> 195 67.6855902 A 306 0 1 0.8292186 0.7903065 0.8615479 #> 196 68.7223284 A 305 0 1 0.8292186 0.7903065 0.8615479 #> 197 70.2602449 A 304 0 1 0.8292186 0.7903065 0.8615479 #> 198 70.5995378 A 303 0 1 0.8292186 0.7903065 0.8615479 #> 199 71.2587985 A 302 1 0 0.8264728 0.7872829 0.8590945 #> 200 71.2672138 A 301 0 1 0.8264728 0.7872829 0.8590945 #> 201 71.8214219 A 300 0 1 0.8264728 0.7872829 0.8590945 #> 202 72.1670941 A 299 1 0 0.8237087 0.7842410 0.8566229 #> 203 72.1695415 A 298 1 0 0.8209446 0.7812042 0.8541472 #> 204 72.3471050 A 297 1 0 0.8181804 0.7781723 0.8516674 #> 205 73.0774123 A 296 0 1 0.8181804 0.7781723 0.8516674 #> 206 74.4971040 A 295 0 1 0.8181804 0.7781723 0.8516674 #> 207 75.4758943 A 294 1 0 0.8153975 0.7751215 0.8491691 #> 208 76.2621398 A 293 1 0 0.8126146 0.7720755 0.8466669 #> 209 76.6013852 A 292 1 0 0.8098317 0.7690343 0.8441608 #> 210 76.6524844 A 291 1 0 0.8070487 0.7659977 0.8416510 #> 211 76.9672003 A 290 1 0 0.8042658 0.7629656 0.8391374 #> 212 77.9263829 A 289 0 1 0.8042658 0.7629656 0.8391374 #> 213 79.8316941 A 288 0 1 0.8042658 0.7629656 0.8391374 #> 214 79.9076509 A 287 0 1 0.8042658 0.7629656 0.8391374 #> 215 80.5380440 A 286 1 0 0.8014537 0.7599013 0.8365973 #> 216 80.7172811 A 285 0 1 0.8014537 0.7599013 0.8365973 #> 217 81.0661957 A 284 0 1 0.8014537 0.7599013 0.8365973 #> 218 81.6143745 A 283 1 0 0.7986217 0.7568167 0.8340378 #> 219 83.7537322 A 282 0 1 0.7986217 0.7568167 0.8340378 #> 220 84.3199651 A 281 0 1 0.7986217 0.7568167 0.8340378 #> 221 84.7166775 A 280 0 1 0.7986217 0.7568167 0.8340378 #> 222 85.1705400 A 279 0 1 0.7986217 0.7568167 0.8340378 #> 223 85.1873869 A 278 0 1 0.7986217 0.7568167 0.8340378 #> 224 85.2629108 A 277 1 0 0.7957386 0.7536725 0.8314344 #> 225 86.8743175 A 276 1 0 0.7928555 0.7505332 0.8288271 #> 226 86.9340820 A 275 0 1 0.7928555 0.7505332 0.8288271 #> 227 87.0820950 A 274 1 0 0.7899618 0.7473854 0.8262078 #> 228 87.2997415 A 273 1 0 0.7870682 0.7442423 0.8235846 #> 229 87.4381573 A 272 1 0 0.7841746 0.7411038 0.8209576 #> 230 88.0871919 A 271 0 1 0.7841746 0.7411038 0.8209576 #> 231 88.3243550 A 270 1 0 0.7812702 0.7379564 0.8183186 #> 232 88.3276166 A 269 0 1 0.7812702 0.7379564 0.8183186 #> 233 88.6145506 A 268 0 1 0.7812702 0.7379564 0.8183186 #> 234 89.1750066 A 267 0 1 0.7812702 0.7379564 0.8183186 #> 235 89.4307290 A 266 1 0 0.7783331 0.7347728 0.8156499 #> 236 89.6167692 A 265 1 0 0.7753960 0.7315938 0.8129774 #> 237 89.7755695 A 264 1 0 0.7724589 0.7284193 0.8103013 #> 238 91.8647559 A 263 0 1 0.7724589 0.7284193 0.8103013 #> 239 91.9824456 A 262 1 0 0.7695106 0.7252353 0.8076127 #> 240 93.2408592 A 261 0 1 0.7695106 0.7252353 0.8076127 #> 241 93.3136053 A 260 0 1 0.7695106 0.7252353 0.8076127 #> 242 93.4905863 A 259 0 1 0.7695106 0.7252353 0.8076127 #> 243 93.7104093 A 258 0 1 0.7695106 0.7252353 0.8076127 #> 244 94.0876845 A 257 1 0 0.7665164 0.7219989 0.8048840 #> 245 94.7052270 A 256 0 1 0.7665164 0.7219989 0.8048840 #> 246 96.7897325 A 255 0 1 0.7665164 0.7219989 0.8048840 #> 247 97.4835043 A 254 1 0 0.7634986 0.7187379 0.8021327 #> 248 97.5095606 A 253 1 0 0.7604808 0.7154816 0.7993777 #> 249 98.2021703 A 252 1 0 0.7574630 0.7122299 0.7966190 #> 250 98.5382896 A 251 1 0 0.7544453 0.7089826 0.7938565 #> 251 99.3645367 A 250 0 1 0.7544453 0.7089826 0.7938565 #> 252 99.6052279 A 249 0 1 0.7544453 0.7089826 0.7938565 #> 253 99.6448581 A 248 0 1 0.7544453 0.7089826 0.7938565 #> 254 99.9203054 A 247 0 1 0.7544453 0.7089826 0.7938565 #> 255 100.0319960 A 246 0 1 0.7544453 0.7089826 0.7938565 #> 256 101.1286638 A 245 0 1 0.7544453 0.7089826 0.7938565 #> 257 101.2849073 A 244 0 1 0.7544453 0.7089826 0.7938565 #> 258 101.3714715 A 243 0 1 0.7544453 0.7089826 0.7938565 #> 259 101.4314849 A 242 0 1 0.7544453 0.7089826 0.7938565 #> 260 101.6631927 A 241 0 1 0.7544453 0.7089826 0.7938565 #> 261 101.6701105 A 240 0 1 0.7544453 0.7089826 0.7938565 #> 262 101.7250316 A 239 0 1 0.7544453 0.7089826 0.7938565 #> 263 102.0646836 A 238 0 1 0.7544453 0.7089826 0.7938565 #> 264 102.4731386 A 237 0 1 0.7544453 0.7089826 0.7938565 #> 265 103.3599337 A 236 0 1 0.7544453 0.7089826 0.7938565 #> 266 103.3858997 A 235 1 0 0.7512349 0.7055012 0.7909378 #> 267 104.1193509 A 234 0 1 0.7512349 0.7055012 0.7909378 #> 268 104.5408470 A 233 0 1 0.7512349 0.7055012 0.7909378 #> 269 104.8671198 A 232 0 1 0.7512349 0.7055012 0.7909378 #> 270 105.0762188 A 231 1 0 0.7479828 0.7019734 0.7879817 #> 271 105.9591180 A 230 0 1 0.7479828 0.7019734 0.7879817 #> 272 106.6420898 A 229 1 0 0.7447165 0.6984336 0.7850100 #> 273 106.7080111 A 228 0 1 0.7447165 0.6984336 0.7850100 #> 274 108.2199961 A 227 0 1 0.7447165 0.6984336 0.7850100 #> 275 108.6286686 A 226 0 1 0.7447165 0.6984336 0.7850100 #> 276 108.9082910 A 225 0 1 0.7447165 0.6984336 0.7850100 #> 277 108.9450103 A 224 0 1 0.7447165 0.6984336 0.7850100 #> 278 109.4524597 A 223 0 1 0.7447165 0.6984336 0.7850100 #> 279 110.6752035 A 222 0 1 0.7447165 0.6984336 0.7850100 #> 280 111.1416159 A 221 1 0 0.7413467 0.6947704 0.7819525 #> 281 111.9904588 A 220 1 0 0.7379769 0.6911137 0.7788900 #> 282 113.1377720 A 219 0 1 0.7379769 0.6911137 0.7788900 #> 283 114.1935322 A 218 1 0 0.7345917 0.6874440 0.7758105 #> 284 115.1863723 A 217 0 1 0.7345917 0.6874440 0.7758105 #> 285 116.0163469 A 216 0 1 0.7345917 0.6874440 0.7758105 #> 286 117.0809444 A 215 1 0 0.7311750 0.6837414 0.7727012 #> 287 117.1680451 A 214 1 0 0.7277583 0.6800452 0.7695870 #> 288 119.6814391 A 213 0 1 0.7277583 0.6800452 0.7695870 #> 289 120.7169260 A 212 0 1 0.7277583 0.6800452 0.7695870 #> 290 122.1766793 A 211 0 1 0.7277583 0.6800452 0.7695870 #> 291 124.8525835 A 210 1 0 0.7242928 0.6762944 0.7664294 #> 292 124.8827926 A 209 1 0 0.7208273 0.6725502 0.7632668 #> 293 125.7206051 A 208 0 1 0.7208273 0.6725502 0.7632668 #> 294 126.0500878 A 207 0 1 0.7208273 0.6725502 0.7632668 #> 295 126.7874323 A 206 0 1 0.7208273 0.6725502 0.7632668 #> 296 126.8931381 A 205 0 1 0.7208273 0.6725502 0.7632668 #> 297 127.9743928 A 204 0 1 0.7208273 0.6725502 0.7632668 #> 298 128.1287128 A 203 0 1 0.7208273 0.6725502 0.7632668 #> 299 128.8321170 A 202 1 0 0.7172588 0.6686841 0.7600184 #> 300 129.3533244 A 201 0 1 0.7172588 0.6686841 0.7600184 #> 301 129.6704036 A 200 1 0 0.7136725 0.6648029 0.7567505 #> 302 130.1803275 A 199 0 1 0.7136725 0.6648029 0.7567505 #> 303 130.7031601 A 198 0 1 0.7136725 0.6648029 0.7567505 #> 304 130.9639163 A 197 0 1 0.7136725 0.6648029 0.7567505 #> 305 130.9970045 A 196 1 0 0.7100313 0.6608601 0.7534342 #> 306 131.4908055 A 195 1 0 0.7063902 0.6569248 0.7501121 #> 307 132.0808389 A 194 0 1 0.7063902 0.6569248 0.7501121 #> 308 132.7882079 A 193 0 1 0.7063902 0.6569248 0.7501121 #> 309 132.8690671 A 192 0 1 0.7063902 0.6569248 0.7501121 #> 310 133.8500312 A 191 0 1 0.7063902 0.6569248 0.7501121 #> 311 133.9676829 A 190 0 1 0.7063902 0.6569248 0.7501121 #> 312 134.5847123 A 189 0 1 0.7063902 0.6569248 0.7501121 #> 313 134.9211996 A 188 0 1 0.7063902 0.6569248 0.7501121 #> 314 135.1019149 A 187 0 1 0.7063902 0.6569248 0.7501121 #> 315 135.1588000 A 186 1 0 0.7025924 0.6528006 0.7466625 #> 316 135.2069709 A 185 1 0 0.6987946 0.6486849 0.7432064 #> 317 135.8094446 A 184 1 0 0.6949968 0.6445774 0.7397439 #> 318 136.1992022 A 183 1 0 0.6911990 0.6404780 0.7362752 #> 319 136.3800132 A 182 1 0 0.6874012 0.6363864 0.7328003 #> 320 137.1939184 A 181 1 0 0.6836034 0.6323025 0.7293194 #> 321 139.0211403 A 180 0 1 0.6836034 0.6323025 0.7293194 #> 322 141.3017821 A 179 0 1 0.6836034 0.6323025 0.7293194 #> 323 141.6016337 A 178 0 1 0.6836034 0.6323025 0.7293194 #> 324 142.4839370 A 177 0 1 0.6836034 0.6323025 0.7293194 #> 325 143.3116393 A 176 1 0 0.6797193 0.6281190 0.7257650 #> 326 144.5084460 A 175 1 0 0.6758352 0.6239437 0.7222041 #> 327 145.2570428 A 174 1 0 0.6719511 0.6197762 0.7186371 #> 328 146.5126322 A 173 0 1 0.6719511 0.6197762 0.7186371 #> 329 147.5909236 A 172 1 0 0.6680444 0.6155886 0.7150461 #> 330 149.0394051 A 171 0 1 0.6680444 0.6155886 0.7150461 #> 331 151.8747627 A 170 1 0 0.6641147 0.6113805 0.7114309 #> 332 152.3036359 A 169 1 0 0.6601850 0.6071802 0.7078095 #> 333 152.6837825 A 168 0 1 0.6601850 0.6071802 0.7078095 #> 334 153.0189691 A 167 0 1 0.6601850 0.6071802 0.7078095 #> 335 153.7818843 A 166 0 1 0.6601850 0.6071802 0.7078095 #> 336 154.3107802 A 165 0 1 0.6601850 0.6071802 0.7078095 #> 337 156.3530945 A 164 0 1 0.6601850 0.6071802 0.7078095 #> 338 157.0907872 A 163 0 1 0.6601850 0.6071802 0.7078095 #> 339 158.0674152 A 162 0 1 0.6601850 0.6071802 0.7078095 #> 340 158.4800477 A 161 0 1 0.6601850 0.6071802 0.7078095 #> 341 158.5194710 A 160 1 0 0.6560589 0.6027445 0.7040278 #> 342 158.7392013 A 159 0 1 0.6560589 0.6027445 0.7040278 #> 343 161.5846889 A 158 1 0 0.6519066 0.5982855 0.7002185 #> 344 164.7372721 A 157 1 0 0.6477543 0.5938359 0.6964019 #> 345 166.2426128 A 156 1 0 0.6436020 0.5893954 0.6925782 #> 346 168.1161018 A 155 1 0 0.6394498 0.5849638 0.6887474 #> 347 168.2281259 A 154 1 0 0.6352975 0.5805409 0.6849098 #> 348 168.4294215 A 153 0 1 0.6352975 0.5805409 0.6849098 #> 349 168.8770094 A 152 1 0 0.6311179 0.5760931 0.6810437 #> 350 169.4244480 A 151 1 0 0.6269383 0.5716540 0.6771708 #> 351 169.4422522 A 150 1 0 0.6227587 0.5672233 0.6732911 #> 352 170.4704706 A 149 0 1 0.6227587 0.5672233 0.6732911 #> 353 170.5776237 A 148 1 0 0.6185509 0.5627666 0.6693824 #> 354 172.9990508 A 147 0 1 0.6185509 0.5627666 0.6693824 #> 355 173.7507167 A 146 0 1 0.6185509 0.5627666 0.6693824 #> 356 173.8815563 A 145 0 1 0.6185509 0.5627666 0.6693824 #> 357 173.9223130 A 144 1 0 0.6142554 0.5582111 0.6653973 #> 358 174.2766212 A 143 1 0 0.6099599 0.5536648 0.6614050 #> 359 174.5461325 A 142 1 0 0.6056644 0.5491273 0.6574057 #> 360 174.7162274 A 141 0 1 0.6056644 0.5491273 0.6574057 #> 361 176.6590842 A 140 1 0 0.6013383 0.5445612 0.6533749 #> 362 176.9610656 A 139 1 0 0.5970121 0.5400039 0.6493370 #> 363 178.5031223 A 138 0 1 0.5970121 0.5400039 0.6493370 #> 364 178.9681939 A 137 0 1 0.5970121 0.5400039 0.6493370 #> 365 179.5550296 A 136 0 1 0.5970121 0.5400039 0.6493370 #> 366 180.0471671 A 135 1 0 0.5925898 0.5353386 0.6452156 #> 367 180.2837518 A 134 0 1 0.5925898 0.5353386 0.6452156 #> 368 180.6996302 A 133 1 0 0.5881342 0.5306421 0.6410602 #> 369 180.9009377 A 132 1 0 0.5836786 0.5259552 0.6368973 #> 370 181.3283312 A 131 0 1 0.5836786 0.5259552 0.6368973 #> 371 184.0466008 A 130 1 0 0.5791888 0.5212360 0.6326994 #> 372 185.7438835 A 129 0 1 0.5791888 0.5212360 0.6326994 #> 373 187.2930109 A 128 1 0 0.5746639 0.5164839 0.6284660 #> 374 187.3284384 A 127 0 1 0.5746639 0.5164839 0.6284660 #> 375 187.8449785 A 126 0 1 0.5746639 0.5164839 0.6284660 #> 376 189.3613424 A 125 0 1 0.5746639 0.5164839 0.6284660 #> 377 189.4981770 A 124 1 0 0.5700295 0.5116085 0.6241378 #> 378 189.7859562 A 123 1 0 0.5653951 0.5067437 0.6198012 #> 379 190.6912802 A 122 1 0 0.5607607 0.5018892 0.6154563 #> 380 191.1076682 A 121 1 0 0.5561264 0.4970449 0.6111031 #> 381 198.6208194 A 120 1 0 0.5514920 0.4922108 0.6067419 #> 382 201.8084701 A 119 1 0 0.5468576 0.4873865 0.6023726 #> 383 203.2206009 A 118 0 1 0.5468576 0.4873865 0.6023726 #> 384 203.4020672 A 117 1 0 0.5421836 0.4825244 0.5979635 #> 385 204.6085827 A 116 0 1 0.5421836 0.4825244 0.5979635 #> 386 204.7131962 A 115 0 1 0.5421836 0.4825244 0.5979635 #> 387 204.8322250 A 114 1 0 0.5374276 0.4775738 0.5934806 #> 388 205.0363326 A 113 1 0 0.5326716 0.4726338 0.5889891 #> 389 205.1596244 A 112 0 1 0.5326716 0.4726338 0.5889891 #> 390 207.4070734 A 111 0 1 0.5326716 0.4726338 0.5889891 #> 391 208.5095081 A 110 1 0 0.5278291 0.4676004 0.5844199 #> 392 208.5440529 A 109 0 1 0.5278291 0.4676004 0.5844199 #> 393 209.6796639 A 108 1 0 0.5229418 0.4625242 0.5798058 #> 394 210.5303223 A 107 0 1 0.5229418 0.4625242 0.5798058 #> 395 211.4006422 A 106 0 1 0.5229418 0.4625242 0.5798058 #> 396 213.0554394 A 105 1 0 0.5179614 0.4573474 0.5751083 #> 397 214.0049455 A 104 0 1 0.5179614 0.4573474 0.5751083 #> 398 215.4027340 A 103 1 0 0.5129327 0.4521245 0.5703625 #> 399 217.0832170 A 102 1 0 0.5079039 0.4469140 0.5656067 #> 400 219.5385758 A 101 0 1 0.5079039 0.4469140 0.5656067 #> 401 221.0842591 A 100 1 0 0.5028249 0.4416555 0.5608007 #> 402 222.4007682 A 99 0 1 0.5028249 0.4416555 0.5608007 #> 403 222.7575312 A 98 0 1 0.5028249 0.4416555 0.5608007 #> 404 222.9677203 A 97 0 1 0.5028249 0.4416555 0.5608007 #> 405 227.9757161 A 96 0 1 0.5028249 0.4416555 0.5608007 #> 406 230.9483853 A 95 1 0 0.4975320 0.4361517 0.5558152 #> 407 233.3291179 A 94 0 1 0.4975320 0.4361517 0.5558152 #> 408 234.7546470 A 93 0 1 0.4975320 0.4361517 0.5558152 #> 409 236.1039880 A 92 0 1 0.4975320 0.4361517 0.5558152 #> 410 237.0593233 A 91 1 0 0.4920646 0.4304507 0.5506811 #> 411 237.8774307 A 90 1 0 0.4865972 0.4247664 0.5455337 #> 412 238.4480442 A 89 0 1 0.4865972 0.4247664 0.5455337 #> 413 241.1774392 A 88 0 1 0.4865972 0.4247664 0.5455337 #> 414 241.5604544 A 87 1 0 0.4810041 0.4189457 0.5402751 #> 415 242.1178978 A 86 0 1 0.4810041 0.4189457 0.5402751 #> 416 242.3122311 A 85 0 1 0.4810041 0.4189457 0.5402751 #> 417 245.3458498 A 84 1 0 0.4752779 0.4129803 0.5348990 #> 418 245.5501089 A 83 0 1 0.4752779 0.4129803 0.5348990 #> 419 245.8857517 A 82 0 1 0.4752779 0.4129803 0.5348990 #> 420 246.1299544 A 81 0 1 0.4752779 0.4129803 0.5348990 #> 421 247.7760192 A 80 0 1 0.4752779 0.4129803 0.5348990 #> 422 249.9233069 A 79 0 1 0.4752779 0.4129803 0.5348990 #> 423 252.7425757 A 78 0 1 0.4752779 0.4129803 0.5348990 #> 424 254.5446271 A 77 1 0 0.4691055 0.4064842 0.5291673 #> 425 256.4404196 A 76 0 1 0.4691055 0.4064842 0.5291673 #> 426 259.3829166 A 75 0 1 0.4691055 0.4064842 0.5291673 #> 427 271.3474961 A 74 0 1 0.4691055 0.4064842 0.5291673 #> 428 273.1257002 A 73 0 1 0.4691055 0.4064842 0.5291673 #> 429 280.3909651 A 72 0 1 0.4691055 0.4064842 0.5291673 #> 430 281.5194863 A 71 0 1 0.4691055 0.4064842 0.5291673 #> 431 282.6318510 A 70 0 1 0.4691055 0.4064842 0.5291673 #> 432 284.9961410 A 69 0 1 0.4691055 0.4064842 0.5291673 #> 433 287.8308610 A 68 0 1 0.4691055 0.4064842 0.5291673 #> 434 293.6815759 A 67 1 0 0.4621039 0.3989645 0.5228094 #> 435 295.1617767 A 66 1 0 0.4551023 0.3914862 0.5164191 #> 436 295.6642687 A 65 0 1 0.4551023 0.3914862 0.5164191 #> 437 299.0591141 A 64 0 1 0.4551023 0.3914862 0.5164191 #> 438 301.7347889 A 63 0 1 0.4551023 0.3914862 0.5164191 #> 439 303.3503769 A 62 0 1 0.4551023 0.3914862 0.5164191 #> 440 304.6405555 A 61 0 1 0.4551023 0.3914862 0.5164191 #> 441 310.0655345 A 60 0 1 0.4551023 0.3914862 0.5164191 #> 442 311.0184998 A 59 0 1 0.4551023 0.3914862 0.5164191 #> 443 311.8106687 A 58 1 0 0.4472557 0.3829516 0.5094087 #> 444 312.7128096 A 57 1 0 0.4394091 0.3744784 0.5023504 #> 445 313.1573766 A 56 1 0 0.4315625 0.3660646 0.4952459 #> 446 313.3722027 A 55 0 1 0.4315625 0.3660646 0.4952459 #> 447 316.3884586 A 54 0 1 0.4315625 0.3660646 0.4952459 #> 448 317.5266710 A 53 0 1 0.4315625 0.3660646 0.4952459 #> 449 318.7833017 A 52 1 0 0.4232633 0.3571188 0.4877865 #> 450 320.7036093 A 51 1 0 0.4149640 0.3482425 0.4802730 #> 451 326.3333469 A 50 1 0 0.4066647 0.3394335 0.4727070 #> 452 326.7511055 A 49 0 1 0.4066647 0.3394335 0.4727070 #> 453 329.3894821 A 48 0 1 0.4066647 0.3394335 0.4727070 #> 454 329.8200403 A 47 0 1 0.4066647 0.3394335 0.4727070 #> 455 330.7110893 A 46 0 1 0.4066647 0.3394335 0.4727070 #> 456 332.3420058 A 45 0 1 0.4066647 0.3394335 0.4727070 #> 457 336.8420868 A 44 1 0 0.3974223 0.3294592 0.4644560 #> 458 337.3663345 A 43 0 1 0.3974223 0.3294592 0.4644560 #> 459 341.3657643 A 42 0 1 0.3974223 0.3294592 0.4644560 #> 460 343.7001138 A 41 1 0 0.3877291 0.3189841 0.4558378 #> 461 344.6852715 A 40 0 1 0.3877291 0.3189841 0.4558378 #> 462 350.2207221 A 39 1 0 0.3777873 0.3082914 0.4469729 #> 463 350.2654152 A 38 0 1 0.3777873 0.3082914 0.4469729 #> 464 350.3644114 A 37 0 1 0.3777873 0.3082914 0.4469729 #> 465 352.4383084 A 36 0 1 0.3777873 0.3082914 0.4469729 #> 466 357.7553493 A 35 0 1 0.3777873 0.3082914 0.4469729 #> 467 359.1658176 A 34 0 1 0.3777873 0.3082914 0.4469729 #> 468 362.2067038 A 33 1 0 0.3663392 0.2956825 0.4370957 #> 469 363.8263817 A 32 1 0 0.3548911 0.2832713 0.4270651 #> 470 366.6874542 A 31 1 0 0.3434430 0.2710479 0.4168890 #> 471 370.0496553 A 30 0 1 0.3434430 0.2710479 0.4168890 #> 472 370.3157123 A 29 1 0 0.3316002 0.2584805 0.4063326 #> 473 373.8745493 A 28 0 1 0.3316002 0.2584805 0.4063326 #> 474 379.0575377 A 27 0 1 0.3316002 0.2584805 0.4063326 #> 475 382.6979071 A 26 1 0 0.3188463 0.2448977 0.3950803 #> 476 389.5516777 A 25 1 0 0.3060925 0.2315671 0.3836334 #> 477 391.6005114 A 24 0 1 0.3060925 0.2315671 0.3836334 #> 478 402.2578995 A 23 0 1 0.3060925 0.2315671 0.3836334 #> 479 404.9665031 A 22 1 0 0.2921792 0.2169492 0.3713229 #> 480 405.1024439 A 21 0 1 0.2921792 0.2169492 0.3713229 #> 481 406.0771660 A 20 0 1 0.2921792 0.2169492 0.3713229 #> 482 409.5208072 A 19 0 1 0.2921792 0.2169492 0.3713229 #> 483 414.9602508 A 18 1 0 0.2759470 0.1995282 0.3575339 #> 484 418.5750209 A 17 1 0 0.2597148 0.1826716 0.3433122 #> 485 425.2838139 A 16 0 1 0.2597148 0.1826716 0.3433122 #> 486 452.7208595 A 15 1 0 0.2424005 0.1649256 0.3281277 #> 487 453.8755271 A 14 0 1 0.2424005 0.1649256 0.3281277 #> 488 464.9161960 A 13 1 0 0.2237543 0.1461040 0.3117891 #> 489 500.0000000 A 12 0 12 0.2237543 0.1461040 0.3117891 #> cumhaz #> 1 0.000000000 #> 2 0.002004008 #> 3 0.004012040 #> 4 0.006024113 #> 5 0.006024113 #> 6 0.008044315 #> 7 0.008044315 #> 8 0.008044315 #> 9 0.010076835 #> 10 0.010076835 #> 11 0.010076835 #> 12 0.012121825 #> 13 0.012121825 #> 14 0.012121825 #> 15 0.012121825 #> 16 0.012121825 #> 17 0.014187940 #> 18 0.014187940 #> 19 0.014187940 #> 20 0.016266942 #> 21 0.016266942 #> 22 0.018354625 #> 23 0.018354625 #> 24 0.020451061 #> 25 0.022551902 #> 26 0.024657165 #> 27 0.024657165 #> 28 0.026771330 #> 29 0.026771330 #> 30 0.026771330 #> 31 0.026771330 #> 32 0.026771330 #> 33 0.026771330 #> 34 0.028912657 #> 35 0.028912657 #> 36 0.028912657 #> 37 0.031067830 #> 38 0.031067830 #> 39 0.033232332 #> 40 0.035401529 #> 41 0.037575442 #> 42 0.037575442 #> 43 0.037575442 #> 44 0.037575442 #> 45 0.039768425 #> 46 0.041966227 #> 47 0.044168870 #> 48 0.044168870 #> 49 0.044168870 #> 50 0.044168870 #> 51 0.044168870 #> 52 0.046396042 #> 53 0.046396042 #> 54 0.046396042 #> 55 0.046396042 #> 56 0.048643233 #> 57 0.050895485 #> 58 0.050895485 #> 59 0.053157928 #> 60 0.053157928 #> 61 0.055430656 #> 62 0.055430656 #> 63 0.055430656 #> 64 0.055430656 #> 65 0.055430656 #> 66 0.057729506 #> 67 0.060033654 #> 68 0.060033654 #> 69 0.060033654 #> 70 0.060033654 #> 71 0.060033654 #> 72 0.060033654 #> 73 0.062370102 #> 74 0.062370102 #> 75 0.064717520 #> 76 0.064717520 #> 77 0.067076011 #> 78 0.067076011 #> 79 0.069445679 #> 80 0.069445679 #> 81 0.071826631 #> 82 0.071826631 #> 83 0.074218976 #> 84 0.076617057 #> 85 0.076617057 #> 86 0.079026696 #> 87 0.079026696 #> 88 0.079026696 #> 89 0.081453880 #> 90 0.083886970 #> 91 0.083886970 #> 92 0.083886970 #> 93 0.083886970 #> 94 0.086343973 #> 95 0.086343973 #> 96 0.086343973 #> 97 0.086343973 #> 98 0.086343973 #> 99 0.088831535 #> 100 0.091325301 #> 101 0.093825301 #> 102 0.096331566 #> 103 0.098844129 #> 104 0.098844129 #> 105 0.098844129 #> 106 0.098844129 #> 107 0.098844129 #> 108 0.098844129 #> 109 0.098844129 #> 110 0.098844129 #> 111 0.101408232 #> 112 0.101408232 #> 113 0.101408232 #> 114 0.103992211 #> 115 0.103992211 #> 116 0.106589613 #> 117 0.106589613 #> 118 0.109200580 #> 119 0.111818381 #> 120 0.114443053 #> 121 0.114443053 #> 122 0.117081575 #> 123 0.119727078 #> 124 0.119727078 #> 125 0.119727078 #> 126 0.122393744 #> 127 0.122393744 #> 128 0.122393744 #> 129 0.122393744 #> 130 0.125089162 #> 131 0.125089162 #> 132 0.125089162 #> 133 0.127806553 #> 134 0.130531349 #> 135 0.130531349 #> 136 0.130531349 #> 137 0.130531349 #> 138 0.130531349 #> 139 0.130531349 #> 140 0.133301432 #> 141 0.133301432 #> 142 0.133301432 #> 143 0.133301432 #> 144 0.133301432 #> 145 0.133301432 #> 146 0.133301432 #> 147 0.133301432 #> 148 0.136134293 #> 149 0.138975202 #> 150 0.141824205 #> 151 0.141824205 #> 152 0.141824205 #> 153 0.141824205 #> 154 0.144706050 #> 155 0.147596223 #> 156 0.150494774 #> 157 0.150494774 #> 158 0.153410226 #> 159 0.156334202 #> 160 0.159266754 #> 161 0.162207930 #> 162 0.165157783 #> 163 0.165157783 #> 164 0.165157783 #> 165 0.165157783 #> 166 0.165157783 #> 167 0.165157783 #> 168 0.168160786 #> 169 0.168160786 #> 170 0.171181934 #> 171 0.171181934 #> 172 0.171181934 #> 173 0.171181934 #> 174 0.174240038 #> 175 0.174240038 #> 176 0.174240038 #> 177 0.174240038 #> 178 0.174240038 #> 179 0.174240038 #> 180 0.174240038 #> 181 0.174240038 #> 182 0.177374834 #> 183 0.177374834 #> 184 0.180529408 #> 185 0.180529408 #> 186 0.180529408 #> 187 0.180529408 #> 188 0.180529408 #> 189 0.180529408 #> 190 0.180529408 #> 191 0.180529408 #> 192 0.180529408 #> 193 0.183776161 #> 194 0.187033490 #> 195 0.187033490 #> 196 0.187033490 #> 197 0.187033490 #> 198 0.187033490 #> 199 0.190344748 #> 200 0.190344748 #> 201 0.190344748 #> 202 0.193689230 #> 203 0.197044935 #> 204 0.200411938 #> 205 0.200411938 #> 206 0.200411938 #> 207 0.203813299 #> 208 0.207226268 #> 209 0.210650925 #> 210 0.214087352 #> 211 0.217535627 #> 212 0.217535627 #> 213 0.217535627 #> 214 0.217535627 #> 215 0.221032131 #> 216 0.221032131 #> 217 0.221032131 #> 218 0.224565700 #> 219 0.224565700 #> 220 0.224565700 #> 221 0.224565700 #> 222 0.224565700 #> 223 0.224565700 #> 224 0.228175808 #> 225 0.231798997 #> 226 0.231798997 #> 227 0.235448632 #> 228 0.239111635 #> 229 0.242788106 #> 230 0.242788106 #> 231 0.246491810 #> 232 0.246491810 #> 233 0.246491810 #> 234 0.246491810 #> 235 0.250251208 #> 236 0.254024793 #> 237 0.257812672 #> 238 0.257812672 #> 239 0.261629466 #> 240 0.261629466 #> 241 0.261629466 #> 242 0.261629466 #> 243 0.261629466 #> 244 0.265520516 #> 245 0.265520516 #> 246 0.265520516 #> 247 0.269457524 #> 248 0.273410093 #> 249 0.277378347 #> 250 0.281362411 #> 251 0.281362411 #> 252 0.281362411 #> 253 0.281362411 #> 254 0.281362411 #> 255 0.281362411 #> 256 0.281362411 #> 257 0.281362411 #> 258 0.281362411 #> 259 0.281362411 #> 260 0.281362411 #> 261 0.281362411 #> 262 0.281362411 #> 263 0.281362411 #> 264 0.281362411 #> 265 0.281362411 #> 266 0.285617730 #> 267 0.285617730 #> 268 0.285617730 #> 269 0.285617730 #> 270 0.289946734 #> 271 0.289946734 #> 272 0.294313547 #> 273 0.294313547 #> 274 0.294313547 #> 275 0.294313547 #> 276 0.294313547 #> 277 0.294313547 #> 278 0.294313547 #> 279 0.294313547 #> 280 0.298838434 #> 281 0.303383888 #> 282 0.303383888 #> 283 0.307971044 #> 284 0.307971044 #> 285 0.307971044 #> 286 0.312622207 #> 287 0.317295104 #> 288 0.317295104 #> 289 0.317295104 #> 290 0.317295104 #> 291 0.322057009 #> 292 0.326841698 #> 293 0.326841698 #> 294 0.326841698 #> 295 0.326841698 #> 296 0.326841698 #> 297 0.326841698 #> 298 0.326841698 #> 299 0.331792193 #> 300 0.331792193 #> 301 0.336792193 #> 302 0.336792193 #> 303 0.336792193 #> 304 0.336792193 #> 305 0.341894234 #> 306 0.347022439 #> 307 0.347022439 #> 308 0.347022439 #> 309 0.347022439 #> 310 0.347022439 #> 311 0.347022439 #> 312 0.347022439 #> 313 0.347022439 #> 314 0.347022439 #> 315 0.352398783 #> 316 0.357804188 #> 317 0.363238971 #> 318 0.368703452 #> 319 0.374197957 #> 320 0.379722819 #> 321 0.379722819 #> 322 0.379722819 #> 323 0.379722819 #> 324 0.379722819 #> 325 0.385404637 #> 326 0.391118923 #> 327 0.396866049 #> 328 0.396866049 #> 329 0.402680003 #> 330 0.402680003 #> 331 0.408562356 #> 332 0.414479516 #> 333 0.414479516 #> 334 0.414479516 #> 335 0.414479516 #> 336 0.414479516 #> 337 0.414479516 #> 338 0.414479516 #> 339 0.414479516 #> 340 0.414479516 #> 341 0.420729516 #> 342 0.420729516 #> 343 0.427058630 #> 344 0.433428056 #> 345 0.439838313 #> 346 0.446289926 #> 347 0.452783432 #> 348 0.452783432 #> 349 0.459362380 #> 350 0.465984896 #> 351 0.472651563 #> 352 0.472651563 #> 353 0.479408320 #> 354 0.479408320 #> 355 0.479408320 #> 356 0.479408320 #> 357 0.486352764 #> 358 0.493345771 #> 359 0.500388024 #> 360 0.500388024 #> 361 0.507530882 #> 362 0.514725126 #> 363 0.514725126 #> 364 0.514725126 #> 365 0.514725126 #> 366 0.522132534 #> 367 0.522132534 #> 368 0.529651331 #> 369 0.537227088 #> 370 0.537227088 #> 371 0.544919396 #> 372 0.544919396 #> 373 0.552731896 #> 374 0.552731896 #> 375 0.552731896 #> 376 0.552731896 #> 377 0.560796412 #> 378 0.568926493 #> 379 0.577123215 #> 380 0.585387677 #> 381 0.593721011 #> 382 0.602124372 #> 383 0.602124372 #> 384 0.610671381 #> 385 0.610671381 #> 386 0.610671381 #> 387 0.619443310 #> 388 0.628292868 #> 389 0.628292868 #> 390 0.628292868 #> 391 0.637383777 #> 392 0.637383777 #> 393 0.646643036 #> 394 0.646643036 #> 395 0.646643036 #> 396 0.656166846 #> 397 0.656166846 #> 398 0.665875584 #> 399 0.675679505 #> 400 0.675679505 #> 401 0.685679505 #> 402 0.685679505 #> 403 0.685679505 #> 404 0.685679505 #> 405 0.685679505 #> 406 0.696205821 #> 407 0.696205821 #> 408 0.696205821 #> 409 0.696205821 #> 410 0.707194832 #> 411 0.718305943 #> 412 0.718305943 #> 413 0.718305943 #> 414 0.729800196 #> 415 0.729800196 #> 416 0.729800196 #> 417 0.741704958 #> 418 0.741704958 #> 419 0.741704958 #> 420 0.741704958 #> 421 0.741704958 #> 422 0.741704958 #> 423 0.741704958 #> 424 0.754691971 #> 425 0.754691971 #> 426 0.754691971 #> 427 0.754691971 #> 428 0.754691971 #> 429 0.754691971 #> 430 0.754691971 #> 431 0.754691971 #> 432 0.754691971 #> 433 0.754691971 #> 434 0.769617344 #> 435 0.784768859 #> 436 0.784768859 #> 437 0.784768859 #> 438 0.784768859 #> 439 0.784768859 #> 440 0.784768859 #> 441 0.784768859 #> 442 0.784768859 #> 443 0.802010239 #> 444 0.819554098 #> 445 0.837411241 #> 446 0.837411241 #> 447 0.837411241 #> 448 0.837411241 #> 449 0.856642010 #> 450 0.876249853 #> 451 0.896249853 #> 452 0.896249853 #> 453 0.896249853 #> 454 0.896249853 #> 455 0.896249853 #> 456 0.896249853 #> 457 0.918977126 #> 458 0.918977126 #> 459 0.918977126 #> 460 0.943367370 #> 461 0.943367370 #> 462 0.969008396 #> 463 0.969008396 #> 464 0.969008396 #> 465 0.969008396 #> 466 0.969008396 #> 467 0.969008396 #> 468 0.999311426 #> 469 1.030561426 #> 470 1.062819491 #> 471 1.062819491 #> 472 1.097302249 #> 473 1.097302249 #> 474 1.097302249 #> 475 1.135763788 #> 476 1.175763788 #> 477 1.175763788 #> 478 1.175763788 #> 479 1.221218333 #> 480 1.221218333 #> 481 1.221218333 #> 482 1.221218333 #> 483 1.276773889 #> 484 1.335597418 #> 485 1.335597418 #> 486 1.402264085 #> 487 1.402264085 #> 488 1.479187162 #> 489 1.479187162 #> #> $B #> time treatment n.risk n.event censor surv lower upper #> 490 0.8492261 B 300 1 0 0.99666667 0.97657559 0.9995298 #> 491 1.4030134 B 299 1 0 0.99333333 0.97360892 0.9983285 #> 492 1.7198339 B 298 1 0 0.99000000 0.96931862 0.9967638 #> 493 2.0758651 B 297 0 1 0.99000000 0.96931862 0.9967638 #> 494 2.3393342 B 296 1 0 0.98665541 0.96483781 0.9949706 #> 495 2.7979480 B 295 0 1 0.98665541 0.96483781 0.9949706 #> 496 2.9805455 B 294 0 1 0.98665541 0.96483781 0.9949706 #> 497 3.0113133 B 293 1 0 0.98328798 0.96031714 0.9930100 #> 498 3.0685160 B 292 1 0 0.97992056 0.95585305 0.9909291 #> 499 3.1712902 B 291 1 0 0.97655313 0.95144783 0.9887532 #> 500 3.2412819 B 290 1 0 0.97318571 0.94709776 0.9864998 #> 501 3.4211613 B 289 0 1 0.97318571 0.94709776 0.9864998 #> 502 3.4450075 B 288 1 0 0.96980659 0.94277571 0.9841754 #> 503 3.4515079 B 287 1 0 0.96642747 0.93850041 0.9817950 #> 504 3.6143976 B 286 1 0 0.96304836 0.93426704 0.9793660 #> 505 3.7608198 B 285 0 1 0.96304836 0.93426704 0.9793660 #> 506 4.6203917 B 284 1 0 0.95965734 0.93005084 0.9768874 #> 507 5.5211413 B 283 0 1 0.95965734 0.93005084 0.9768874 #> 508 5.5773040 B 282 1 0 0.95625430 0.92584945 0.9743634 #> 509 6.1533491 B 281 0 1 0.95625430 0.92584945 0.9743634 #> 510 6.4806603 B 280 1 0 0.95283911 0.92166069 0.9717974 #> 511 6.7140516 B 279 1 0 0.94942391 0.91750264 0.9691997 #> 512 7.3859528 B 278 1 0 0.94600872 0.91337265 0.9665731 #> 513 7.4210626 B 277 0 1 0.94600872 0.91337265 0.9665731 #> 514 7.5020884 B 276 1 0 0.94258115 0.90924888 0.9639122 #> 515 7.7622930 B 275 1 0 0.93915358 0.90514953 0.9612267 #> 516 8.2221920 B 274 1 0 0.93572602 0.90107282 0.9585184 #> 517 8.6680839 B 273 1 0 0.93229845 0.89701715 0.9557889 #> 518 8.7919587 B 272 0 1 0.93229845 0.89701715 0.9557889 #> 519 9.0396962 B 271 1 0 0.92885823 0.89296211 0.9530312 #> 520 9.0521882 B 270 0 1 0.92885823 0.89296211 0.9530312 #> 521 10.3249249 B 269 0 1 0.92885823 0.89296211 0.9530312 #> 522 10.5896188 B 268 1 0 0.92539234 0.88888760 0.9502379 #> 523 11.9051673 B 267 1 0 0.92192645 0.88483162 0.9474266 #> 524 12.1075238 B 266 0 1 0.92192645 0.88483162 0.9474266 #> 525 12.2191633 B 265 1 0 0.91844749 0.88077388 0.9445896 #> 526 12.2323488 B 264 1 0 0.91496852 0.87673294 0.9417364 #> 527 12.4915818 B 263 0 1 0.91496852 0.87673294 0.9417364 #> 528 12.7049720 B 262 0 1 0.91496852 0.87673294 0.9417364 #> 529 13.0554772 B 261 1 0 0.91146289 0.87266934 0.9388497 #> 530 13.2074189 B 260 1 0 0.90795727 0.86862143 0.9359482 #> 531 13.4869944 B 259 0 1 0.90795727 0.86862143 0.9359482 #> 532 13.5637730 B 258 0 1 0.90795727 0.86862143 0.9359482 #> 533 13.6943879 B 257 1 0 0.90442436 0.86454949 0.9330138 #> 534 13.9126408 B 256 1 0 0.90089145 0.86049230 0.9300655 #> 535 13.9279249 B 255 1 0 0.89735854 0.85644913 0.9271042 #> 536 14.2204744 B 254 1 0 0.89382563 0.85241930 0.9241303 #> 537 14.2519825 B 253 0 1 0.89382563 0.85241930 0.9241303 #> 538 14.4693075 B 252 1 0 0.89027871 0.84838272 0.9211345 #> 539 15.1259399 B 251 0 1 0.89027871 0.84838272 0.9211345 #> 540 15.2915139 B 250 0 1 0.89027871 0.84838272 0.9211345 #> 541 16.0775600 B 249 0 1 0.89027871 0.84838272 0.9211345 #> 542 16.3430735 B 248 0 1 0.89027871 0.84838272 0.9211345 #> 543 16.5377021 B 247 1 0 0.88667434 0.84427896 0.9180862 #> 544 16.7345687 B 246 0 1 0.88667434 0.84427896 0.9180862 #> 545 17.0540489 B 245 1 0 0.88305526 0.84016783 0.9150158 #> 546 17.7346078 B 244 1 0 0.87943618 0.83606940 0.9119339 #> 547 18.3219669 B 243 0 1 0.87943618 0.83606940 0.9119339 #> 548 18.5735263 B 242 1 0 0.87580215 0.83196263 0.9088302 #> 549 18.6707015 B 241 0 1 0.87580215 0.83196263 0.9088302 #> 550 18.8265045 B 240 1 0 0.87215297 0.82784712 0.9057050 #> 551 18.9679808 B 239 0 1 0.87215297 0.82784712 0.9057050 #> 552 18.9849356 B 238 1 0 0.86848846 0.82372246 0.9025581 #> 553 19.2886739 B 237 1 0 0.86482396 0.81960925 0.8994010 #> 554 19.3144386 B 236 0 1 0.86482396 0.81960925 0.8994010 #> 555 19.5837178 B 235 0 1 0.86482396 0.81960925 0.8994010 #> 556 19.7480647 B 234 1 0 0.86112813 0.81546487 0.8962111 #> 557 20.2311676 B 233 1 0 0.85743230 0.81133155 0.8930115 #> 558 20.3595135 B 232 1 0 0.85373647 0.80720889 0.8898023 #> 559 20.8240812 B 231 0 1 0.85373647 0.80720889 0.8898023 #> 560 20.8436242 B 230 1 0 0.85002457 0.80307513 0.8865720 #> 561 20.9476736 B 229 0 1 0.85002457 0.80307513 0.8865720 #> 562 21.4476172 B 228 0 1 0.85002457 0.80307513 0.8865720 #> 563 21.6935437 B 227 0 1 0.85002457 0.80307513 0.8865720 #> 564 22.2428646 B 226 1 0 0.84626340 0.79888609 0.8832962 #> 565 22.4233793 B 225 1 0 0.84250223 0.79470751 0.8800113 #> 566 22.5998941 B 224 1 0 0.83874106 0.79053903 0.8767175 #> 567 23.8855711 B 223 0 1 0.83874106 0.79053903 0.8767175 #> 568 24.2416986 B 222 0 1 0.83874106 0.79053903 0.8767175 #> 569 24.9889490 B 221 0 1 0.83874106 0.79053903 0.8767175 #> 570 25.3284722 B 220 0 1 0.83874106 0.79053903 0.8767175 #> 571 26.2260987 B 219 1 0 0.83491119 0.78628985 0.8733637 #> 572 26.2748075 B 218 1 0 0.83108132 0.78205100 0.8700011 #> 573 26.6127303 B 217 1 0 0.82725146 0.77782215 0.8666298 #> 574 27.0121949 B 216 1 0 0.82342159 0.77360296 0.8632501 #> 575 27.1385849 B 215 1 0 0.81959172 0.76939316 0.8598623 #> 576 27.1427844 B 214 1 0 0.81576185 0.76519245 0.8564664 #> 577 27.3660973 B 213 0 1 0.81576185 0.76519245 0.8564664 #> 578 27.4099098 B 212 1 0 0.81191392 0.76097725 0.8530490 #> 579 27.8727602 B 211 0 1 0.81191392 0.76097725 0.8530490 #> 580 28.7679537 B 210 1 0 0.80804766 0.75674724 0.8496099 #> 581 28.7863941 B 209 0 1 0.80804766 0.75674724 0.8496099 #> 582 28.8516511 B 208 0 1 0.80804766 0.75674724 0.8496099 #> 583 29.7580207 B 207 0 1 0.80804766 0.75674724 0.8496099 #> 584 29.8875161 B 206 1 0 0.80412510 0.75245351 0.8461202 #> 585 31.0753223 B 205 1 0 0.80020254 0.74816888 0.8426226 #> 586 31.3239259 B 204 1 0 0.79627998 0.74389311 0.8391174 #> 587 31.3737925 B 203 1 0 0.79235742 0.73962596 0.8356047 #> 588 31.4008382 B 202 1 0 0.78843486 0.73536720 0.8320847 #> 589 31.8360820 B 201 1 0 0.78451229 0.73111663 0.8285575 #> 590 32.1776180 B 200 1 0 0.78058973 0.72687405 0.8250232 #> 591 32.2845542 B 199 1 0 0.77666717 0.72263926 0.8214821 #> 592 32.3201275 B 198 0 1 0.77666717 0.72263926 0.8214821 #> 593 32.6268562 B 197 1 0 0.77272470 0.71838704 0.8179187 #> 594 32.8579123 B 196 1 0 0.76878223 0.71414244 0.8143486 #> 595 33.5083467 B 195 1 0 0.76483975 0.70990530 0.8107719 #> 596 33.7408843 B 194 1 0 0.76089728 0.70567545 0.8071887 #> 597 34.0285658 B 193 1 0 0.75695481 0.70145275 0.8035991 #> 598 34.3307012 B 192 0 1 0.75695481 0.70145275 0.8035991 #> 599 34.7955872 B 191 1 0 0.75299169 0.69721141 0.7999870 #> 600 35.3155408 B 190 1 0 0.74902858 0.69297709 0.7963686 #> 601 35.3873079 B 189 0 1 0.74902858 0.69297709 0.7963686 #> 602 35.3912534 B 188 0 1 0.74902858 0.69297709 0.7963686 #> 603 36.6028967 B 187 0 1 0.74902858 0.69297709 0.7963686 #> 604 37.1140694 B 186 1 0 0.74500154 0.68867044 0.7926935 #> 605 37.3202892 B 185 1 0 0.74097451 0.68437108 0.7890120 #> 606 37.3557166 B 184 0 1 0.74097451 0.68437108 0.7890120 #> 607 37.6872463 B 183 1 0 0.73692547 0.68005164 0.7853068 #> 608 37.8779261 B 182 1 0 0.73287643 0.67573939 0.7815953 #> 609 37.9046463 B 181 1 0 0.72882738 0.67143419 0.7778776 #> 610 38.2870764 B 180 1 0 0.72477834 0.66713590 0.7741537 #> 611 38.4278985 B 179 0 1 0.72477834 0.66713590 0.7741537 #> 612 38.4697339 B 178 1 0 0.72070655 0.66281652 0.7704057 #> 613 38.4803842 B 177 1 0 0.71663477 0.65850400 0.7666517 #> 614 38.8236166 B 176 1 0 0.71256298 0.65419821 0.7628917 #> 615 38.9656553 B 175 0 1 0.71256298 0.65419821 0.7628917 #> 616 39.0678457 B 174 1 0 0.70846779 0.64987050 0.7591070 #> 617 39.7824824 B 173 0 1 0.70846779 0.64987050 0.7591070 #> 618 40.1446851 B 172 1 0 0.70434879 0.64552047 0.7552973 #> 619 40.2919970 B 171 0 1 0.70434879 0.64552047 0.7552973 #> 620 40.8238759 B 170 1 0 0.70020556 0.64114771 0.7514621 #> 621 41.0376828 B 169 0 1 0.70020556 0.64114771 0.7514621 #> 622 41.0662129 B 168 0 1 0.70020556 0.64114771 0.7514621 #> 623 41.2949253 B 167 1 0 0.69601271 0.63672135 0.7475812 #> 624 42.5311133 B 166 0 1 0.69601271 0.63672135 0.7475812 #> 625 44.6248777 B 165 1 0 0.69179445 0.63227115 0.7436736 #> 626 44.8460398 B 164 1 0 0.68757620 0.62782812 0.7397599 #> 627 44.9419892 B 163 0 1 0.68757620 0.62782812 0.7397599 #> 628 46.6101451 B 162 0 1 0.68757620 0.62782812 0.7397599 #> 629 47.1405277 B 161 0 1 0.68757620 0.62782812 0.7397599 #> 630 47.8510476 B 160 0 1 0.68757620 0.62782812 0.7397599 #> 631 47.8891543 B 159 1 0 0.68325182 0.62326300 0.7357548 #> 632 48.0896160 B 158 1 0 0.67892744 0.61870565 0.7317431 #> 633 48.1972482 B 157 1 0 0.67460306 0.61415594 0.7277249 #> 634 48.2754867 B 156 1 0 0.67027868 0.60961373 0.7237003 #> 635 49.0816396 B 155 1 0 0.66595430 0.60507893 0.7196693 #> 636 49.7949898 B 154 0 1 0.66595430 0.60507893 0.7196693 #> 637 49.9062966 B 153 1 0 0.66160166 0.60051727 0.7156092 #> 638 51.2082623 B 152 1 0 0.65724902 0.59596301 0.7115427 #> 639 52.2920989 B 151 0 1 0.65724902 0.59596301 0.7115427 #> 640 53.3559798 B 150 1 0 0.65286736 0.59138107 0.7074464 #> 641 53.6840931 B 149 0 1 0.65286736 0.59138107 0.7074464 #> 642 54.9236065 B 148 1 0 0.64845609 0.58677089 0.7033198 #> 643 55.0154610 B 147 1 0 0.64404483 0.58216823 0.6991868 #> 644 55.3458512 B 146 0 1 0.64404483 0.58216823 0.6991868 #> 645 56.7171853 B 145 0 1 0.64404483 0.58216823 0.6991868 #> 646 56.8481500 B 144 1 0 0.63957229 0.57749934 0.6949977 #> 647 56.9334980 B 143 1 0 0.63509976 0.57283823 0.6908020 #> 648 57.1030595 B 142 1 0 0.63062723 0.56818477 0.6865997 #> 649 58.4270646 B 141 1 0 0.62615469 0.56353888 0.6823909 #> 650 58.6084245 B 140 1 0 0.62168216 0.55890044 0.6781757 #> 651 58.7730149 B 139 1 0 0.61720962 0.55426938 0.6739542 #> 652 59.6546380 B 138 1 0 0.61273709 0.54964561 0.6697263 #> 653 60.2527239 B 137 0 1 0.61273709 0.54964561 0.6697263 #> 654 61.1832674 B 136 0 1 0.61273709 0.54964561 0.6697263 #> 655 61.2068279 B 135 1 0 0.60819830 0.54495030 0.6654379 #> 656 61.3301293 B 134 1 0 0.60365950 0.54026258 0.6611430 #> 657 62.4040822 B 133 1 0 0.59912071 0.53558237 0.6568416 #> 658 62.4816013 B 132 1 0 0.59458192 0.53090958 0.6525338 #> 659 63.0700888 B 131 1 0 0.59004313 0.52624415 0.6482196 #> 660 63.3996256 B 130 0 1 0.59004313 0.52624415 0.6482196 #> 661 64.0457781 B 129 1 0 0.58546915 0.52154448 0.6438701 #> 662 64.5608166 B 128 0 1 0.58546915 0.52154448 0.6438701 #> 663 64.6149696 B 127 1 0 0.58085915 0.51680977 0.6394845 #> 664 64.9965467 B 126 1 0 0.57624916 0.51208270 0.6350923 #> 665 65.7530859 B 125 0 1 0.57624916 0.51208270 0.6350923 #> 666 67.4200893 B 124 1 0 0.57160199 0.50731942 0.6306629 #> 667 67.5251343 B 123 1 0 0.56695482 0.50256389 0.6262268 #> 668 68.5812580 B 122 0 1 0.56695482 0.50256389 0.6262268 #> 669 68.8229804 B 121 1 0 0.56226924 0.49777091 0.6217523 #> 670 69.0486551 B 120 1 0 0.55758367 0.49298583 0.6172710 #> 671 69.4935935 B 119 1 0 0.55289809 0.48820856 0.6127830 #> 672 69.5240787 B 118 0 1 0.55289809 0.48820856 0.6127830 #> 673 69.7451992 B 117 1 0 0.54817246 0.48339217 0.6082551 #> 674 70.1227875 B 116 0 1 0.54817246 0.48339217 0.6082551 #> 675 70.2628625 B 115 0 1 0.54817246 0.48339217 0.6082551 #> 676 72.9266499 B 114 1 0 0.54336393 0.47848660 0.6036517 #> 677 73.2620925 B 113 1 0 0.53855540 0.47358955 0.5990411 #> 678 73.5593273 B 112 1 0 0.53374687 0.46870096 0.5944232 #> 679 73.6926698 B 111 1 0 0.52893834 0.46382076 0.5897981 #> 680 74.0347874 B 110 1 0 0.52412981 0.45894889 0.5851659 #> 681 75.1656036 B 109 1 0 0.51932128 0.45408527 0.5805264 #> 682 75.4571413 B 108 0 1 0.51932128 0.45408527 0.5805264 #> 683 75.5205919 B 107 0 1 0.51932128 0.45408527 0.5805264 #> 684 75.5904943 B 106 0 1 0.51932128 0.45408527 0.5805264 #> 685 76.1546928 B 105 1 0 0.51437536 0.44906987 0.5757659 #> 686 76.4192775 B 104 1 0 0.50942945 0.44406367 0.5709975 #> 687 77.1943697 B 103 0 1 0.50942945 0.44406367 0.5709975 #> 688 77.4276997 B 102 0 1 0.50942945 0.44406367 0.5709975 #> 689 78.4347848 B 101 1 0 0.50438559 0.43895232 0.5661404 #> 690 78.8443008 B 100 0 1 0.50438559 0.43895232 0.5661404 #> 691 80.4921978 B 99 0 1 0.50438559 0.43895232 0.5661404 #> 692 82.5469181 B 98 0 1 0.50438559 0.43895232 0.5661404 #> 693 83.5830935 B 97 0 1 0.50438559 0.43895232 0.5661404 #> 694 83.5853505 B 96 1 0 0.49913158 0.43360420 0.5611027 #> 695 84.2445319 B 95 0 1 0.49913158 0.43360420 0.5611027 #> 696 84.6593786 B 94 0 1 0.49913158 0.43360420 0.5611027 #> 697 85.2637815 B 93 1 0 0.49376457 0.42813436 0.5559635 #> 698 88.7049039 B 92 0 1 0.49376457 0.42813436 0.5559635 #> 699 90.5420118 B 91 1 0 0.48833858 0.42260739 0.5507660 #> 700 90.6121802 B 90 1 0 0.48291260 0.41709349 0.5455577 #> 701 91.3637782 B 89 1 0 0.47748662 0.41159256 0.5403387 #> 702 91.8865159 B 88 1 0 0.47206063 0.40610446 0.5351090 #> 703 93.2119835 B 87 1 0 0.46663465 0.40062912 0.5298687 #> 704 93.6487147 B 86 1 0 0.46120866 0.39516642 0.5246177 #> 705 93.7520019 B 85 1 0 0.45578268 0.38971628 0.5193562 #> 706 93.8619589 B 84 0 1 0.45578268 0.38971628 0.5193562 #> 707 94.4459064 B 83 1 0 0.45029132 0.38420250 0.5140306 #> 708 95.9224128 B 82 1 0 0.44479996 0.37870177 0.5086939 #> 709 95.9720009 B 81 0 1 0.44479996 0.37870177 0.5086939 #> 710 96.2560394 B 80 1 0 0.43923996 0.37313424 0.5032901 #> 711 101.0785894 B 79 1 0 0.43367996 0.36758030 0.4978747 #> 712 101.8512269 B 78 1 0 0.42811997 0.36203990 0.4924480 #> 713 102.0480708 B 77 0 1 0.42811997 0.36203990 0.4924480 #> 714 102.1683344 B 76 1 0 0.42248681 0.35642831 0.4869497 #> 715 103.4746398 B 75 0 1 0.42248681 0.35642831 0.4869497 #> 716 105.5323827 B 74 0 1 0.42248681 0.35642831 0.4869497 #> 717 107.3694875 B 73 1 0 0.41669932 0.35065176 0.4813133 #> 718 109.4392228 B 72 1 0 0.41091183 0.34489101 0.4756636 #> 719 111.0420553 B 71 0 1 0.41091183 0.34489101 0.4756636 #> 720 113.1300571 B 70 0 1 0.41091183 0.34489101 0.4756636 #> 721 114.2058151 B 69 1 0 0.40495658 0.33895090 0.4698642 #> 722 114.4152727 B 68 0 1 0.40495658 0.33895090 0.4698642 #> 723 114.7067767 B 67 1 0 0.39891245 0.33292475 0.4639783 #> 724 114.8396735 B 66 0 1 0.39891245 0.33292475 0.4639783 #> 725 115.3801046 B 65 0 1 0.39891245 0.33292475 0.4639783 #> 726 115.6529216 B 64 0 1 0.39891245 0.33292475 0.4639783 #> 727 116.2388631 B 63 0 1 0.39891245 0.33292475 0.4639783 #> 728 116.5770227 B 62 1 0 0.39247838 0.32645875 0.4577657 #> 729 117.1516860 B 61 1 0 0.38604431 0.32001678 0.4515334 #> 730 119.8663712 B 60 1 0 0.37961024 0.31359868 0.4452816 #> 731 121.0960158 B 59 0 1 0.37961024 0.31359868 0.4452816 #> 732 123.7513692 B 58 1 0 0.37306524 0.30707385 0.4389223 #> 733 124.2150196 B 57 1 0 0.36652023 0.30057433 0.4325423 #> 734 127.4336784 B 56 0 1 0.36652023 0.30057433 0.4325423 #> 735 128.6077718 B 55 0 1 0.36652023 0.30057433 0.4325423 #> 736 128.9828791 B 54 0 1 0.36652023 0.30057433 0.4325423 #> 737 129.1025209 B 53 1 0 0.35960475 0.29366314 0.4258504 #> 738 129.7187580 B 52 1 0 0.35268928 0.28678334 0.4191330 #> 739 129.7250459 B 51 1 0 0.34577380 0.27993472 0.4123901 #> 740 133.1594500 B 50 0 1 0.34577380 0.27993472 0.4123901 #> 741 135.4086771 B 49 1 0 0.33871719 0.27295114 0.4055113 #> 742 139.8042160 B 48 1 0 0.33166059 0.26600114 0.3986051 #> 743 139.8137748 B 47 1 0 0.32460398 0.25908458 0.3916715 #> 744 140.2541753 B 46 1 0 0.31754737 0.25220139 0.3847103 #> 745 143.5751793 B 45 0 1 0.31754737 0.25220139 0.3847103 #> 746 144.2521772 B 44 0 1 0.31754737 0.25220139 0.3847103 #> 747 150.1996343 B 43 0 1 0.31754737 0.25220139 0.3847103 #> 748 150.6582394 B 42 1 0 0.30998672 0.24476184 0.3773298 #> 749 155.7676131 B 41 1 0 0.30242607 0.23736642 0.3699134 #> 750 157.8603589 B 40 1 0 0.29486541 0.23001505 0.3624608 #> 751 159.8315468 B 39 0 1 0.29486541 0.23001505 0.3624608 #> 752 160.7520774 B 38 0 1 0.29486541 0.23001505 0.3624608 #> 753 163.3827108 B 37 1 0 0.28689608 0.22222871 0.3546600 #> 754 163.6438166 B 36 1 0 0.27892674 0.21449687 0.3468147 #> 755 168.2133266 B 35 1 0 0.27095741 0.20681951 0.3389248 #> 756 170.6566002 B 34 0 1 0.27095741 0.20681951 0.3389248 #> 757 172.4208727 B 33 0 1 0.27095741 0.20681951 0.3389248 #> 758 175.4929594 B 32 0 1 0.27095741 0.20681951 0.3389248 #> 759 177.5091814 B 31 0 1 0.27095741 0.20681951 0.3389248 #> 760 184.5103590 B 30 0 1 0.27095741 0.20681951 0.3389248 #> 761 188.0853029 B 29 1 0 0.26161405 0.19756100 0.3299907 #> 762 191.6321564 B 28 1 0 0.25227069 0.18840587 0.3209734 #> 763 195.5002523 B 27 1 0 0.24292733 0.17935342 0.3118732 #> 764 196.4246807 B 26 0 1 0.24292733 0.17935342 0.3118732 #> 765 199.8426419 B 25 1 0 0.23321024 0.16995843 0.3024280 #> 766 201.1106058 B 24 0 1 0.23321024 0.16995843 0.3024280 #> 767 203.2845550 B 23 0 1 0.23321024 0.16995843 0.3024280 #> 768 204.6022754 B 22 0 1 0.23321024 0.16995843 0.3024280 #> 769 210.0548161 B 21 0 1 0.23321024 0.16995843 0.3024280 #> 770 212.7464896 B 20 1 0 0.22154973 0.15830211 0.2916309 #> 771 215.0981459 B 19 1 0 0.20988921 0.14689054 0.2806380 #> 772 215.5597419 B 18 1 0 0.19822870 0.13572014 0.2694522 #> 773 220.6392416 B 17 1 0 0.18656819 0.12478957 0.2580743 #> 774 230.5868701 B 16 1 0 0.17490768 0.11409964 0.2465032 #> 775 230.9729773 B 15 1 0 0.16324717 0.10365340 0.2347358 #> 776 232.5020784 B 14 0 1 0.16324717 0.10365340 0.2347358 #> 777 236.3826316 B 13 0 1 0.16324717 0.10365340 0.2347358 #> 778 253.9413828 B 12 0 1 0.16324717 0.10365340 0.2347358 #> 779 258.5498424 B 11 1 0 0.14840652 0.08975672 0.2209452 #> 780 294.1386975 B 10 1 0 0.13356586 0.07651535 0.2066334 #> 781 297.0789867 B 9 1 0 0.11872521 0.06393665 0.1917953 #> 782 320.4212314 B 8 1 0 0.10388456 0.05204555 0.1764110 #> 783 324.6217046 B 7 0 1 0.10388456 0.05204555 0.1764110 #> 784 333.8879947 B 6 1 0 0.08657047 0.03842706 0.1592925 #> 785 341.8226476 B 5 0 1 0.08657047 0.03842706 0.1592925 #> 786 364.3259341 B 4 1 0 0.06492785 0.02233017 0.1399053 #> 787 401.8506175 B 3 1 0 0.04328523 0.00996229 0.1177545 #> 788 417.7060892 B 2 0 1 0.04328523 0.00996229 0.1177545 #> 789 500.0000000 B 1 0 1 0.04328523 0.00996229 0.1177545 #> cumhaz #> 490 0.003333333 #> 491 0.006677815 #> 492 0.010033520 #> 493 0.010033520 #> 494 0.013411898 #> 495 0.013411898 #> 496 0.013411898 #> 497 0.016824867 #> 498 0.020249525 #> 499 0.023685951 #> 500 0.027134227 #> 501 0.027134227 #> 502 0.030606449 #> 503 0.034090770 #> 504 0.037587273 #> 505 0.037587273 #> 506 0.041108400 #> 507 0.041108400 #> 508 0.044654499 #> 509 0.044654499 #> 510 0.048225928 #> 511 0.051810157 #> 512 0.055407279 #> 513 0.055407279 #> 514 0.059030468 #> 515 0.062666831 #> 516 0.066316466 #> 517 0.069979470 #> 518 0.069979470 #> 519 0.073669507 #> 520 0.073669507 #> 521 0.073669507 #> 522 0.077400850 #> 523 0.081146169 #> 524 0.081146169 #> 525 0.084919754 #> 526 0.088707632 #> 527 0.088707632 #> 528 0.088707632 #> 529 0.092539050 #> 530 0.096385204 #> 531 0.096385204 #> 532 0.096385204 #> 533 0.100276254 #> 534 0.104182504 #> 535 0.108104073 #> 536 0.112041081 #> 537 0.112041081 #> 538 0.116009335 #> 539 0.116009335 #> 540 0.116009335 #> 541 0.116009335 #> 542 0.116009335 #> 543 0.120057918 #> 544 0.120057918 #> 545 0.124139551 #> 546 0.128237911 #> 547 0.128237911 #> 548 0.132370143 #> 549 0.132370143 #> 550 0.136536809 #> 551 0.136536809 #> 552 0.140738490 #> 553 0.144957899 #> 554 0.144957899 #> 555 0.144957899 #> 556 0.149231404 #> 557 0.153523249 #> 558 0.157833594 #> 559 0.157833594 #> 560 0.162181420 #> 561 0.162181420 #> 562 0.162181420 #> 563 0.162181420 #> 564 0.166606199 #> 565 0.171050643 #> 566 0.175514929 #> 567 0.175514929 #> 568 0.175514929 #> 569 0.175514929 #> 570 0.175514929 #> 571 0.180081139 #> 572 0.184668295 #> 573 0.189276590 #> 574 0.193906219 #> 575 0.198557382 #> 576 0.203230279 #> 577 0.203230279 #> 578 0.207947261 #> 579 0.207947261 #> 580 0.212709165 #> 581 0.212709165 #> 582 0.212709165 #> 583 0.212709165 #> 584 0.217563534 #> 585 0.222441583 #> 586 0.227343544 #> 587 0.232269652 #> 588 0.237220147 #> 589 0.242195272 #> 590 0.247195272 #> 591 0.252220397 #> 592 0.252220397 #> 593 0.257296539 #> 594 0.262398580 #> 595 0.267526785 #> 596 0.272681424 #> 597 0.277862772 #> 598 0.277862772 #> 599 0.283098374 #> 600 0.288361532 #> 601 0.288361532 #> 602 0.288361532 #> 603 0.288361532 #> 604 0.293737876 #> 605 0.299143281 #> 606 0.299143281 #> 607 0.304607762 #> 608 0.310102267 #> 609 0.315627129 #> 610 0.321182685 #> 611 0.321182685 #> 612 0.326800662 #> 613 0.332450380 #> 614 0.338132198 #> 615 0.338132198 #> 616 0.343879325 #> 617 0.343879325 #> 618 0.349693278 #> 619 0.349693278 #> 620 0.355575631 #> 621 0.355575631 #> 622 0.355575631 #> 623 0.361563655 #> 624 0.361563655 #> 625 0.367624261 #> 626 0.373721822 #> 627 0.373721822 #> 628 0.373721822 #> 629 0.373721822 #> 630 0.373721822 #> 631 0.380011130 #> 632 0.386340244 #> 633 0.392709671 #> 634 0.399119927 #> 635 0.405571540 #> 636 0.405571540 #> 637 0.412107488 #> 638 0.418686435 #> 639 0.418686435 #> 640 0.425353102 #> 641 0.425353102 #> 642 0.432109859 #> 643 0.438912580 #> 644 0.438912580 #> 645 0.438912580 #> 646 0.445857024 #> 647 0.452850031 #> 648 0.459892285 #> 649 0.466984483 #> 650 0.474127340 #> 651 0.481321585 #> 652 0.488567962 #> 653 0.488567962 #> 654 0.488567962 #> 655 0.495975369 #> 656 0.503438056 #> 657 0.510956853 #> 658 0.518532610 #> 659 0.526166198 #> 660 0.526166198 #> 661 0.533918136 #> 662 0.533918136 #> 663 0.541792152 #> 664 0.549728660 #> 665 0.549728660 #> 666 0.557793176 #> 667 0.565923257 #> 668 0.565923257 #> 669 0.574187720 #> 670 0.582521053 #> 671 0.590924415 #> 672 0.590924415 #> 673 0.599471423 #> 674 0.599471423 #> 675 0.599471423 #> 676 0.608243353 #> 677 0.617092911 #> 678 0.626021482 #> 679 0.635030491 #> 680 0.644121400 #> 681 0.653295712 #> 682 0.653295712 #> 683 0.653295712 #> 684 0.653295712 #> 685 0.662819522 #> 686 0.672434906 #> 687 0.672434906 #> 688 0.672434906 #> 689 0.682335896 #> 690 0.682335896 #> 691 0.682335896 #> 692 0.682335896 #> 693 0.682335896 #> 694 0.692752563 #> 695 0.692752563 #> 696 0.692752563 #> 697 0.703505251 #> 698 0.703505251 #> 699 0.714494262 #> 700 0.725605373 #> 701 0.736841328 #> 702 0.748204965 #> 703 0.759699218 #> 704 0.771327125 #> 705 0.783091830 #> 706 0.783091830 #> 707 0.795140023 #> 708 0.807335145 #> 709 0.807335145 #> 710 0.819835145 #> 711 0.832493373 #> 712 0.845313886 #> 713 0.845313886 #> 714 0.858471781 #> 715 0.858471781 #> 716 0.858471781 #> 717 0.872170411 #> 718 0.886059300 #> 719 0.886059300 #> 720 0.886059300 #> 721 0.900552053 #> 722 0.900552053 #> 723 0.915477426 #> 724 0.915477426 #> 725 0.915477426 #> 726 0.915477426 #> 727 0.915477426 #> 728 0.931606459 #> 729 0.947999901 #> 730 0.964666568 #> 731 0.964666568 #> 732 0.981907947 #> 733 0.999451807 #> 734 0.999451807 #> 735 0.999451807 #> 736 0.999451807 #> 737 1.018319731 #> 738 1.037550501 #> 739 1.057158344 #> 740 1.057158344 #> 741 1.077566507 #> 742 1.098399840 #> 743 1.119676436 #> 744 1.141415567 #> 745 1.141415567 #> 746 1.141415567 #> 747 1.141415567 #> 748 1.165225090 #> 749 1.189615334 #> 750 1.214615334 #> 751 1.214615334 #> 752 1.214615334 #> 753 1.241642361 #> 754 1.269420139 #> 755 1.297991568 #> 756 1.297991568 #> 757 1.297991568 #> 758 1.297991568 #> 759 1.297991568 #> 760 1.297991568 #> 761 1.332474326 #> 762 1.368188612 #> 763 1.405225649 #> 764 1.405225649 #> 765 1.445225649 #> 766 1.445225649 #> 767 1.445225649 #> 768 1.445225649 #> 769 1.445225649 #> 770 1.495225649 #> 771 1.547857228 #> 772 1.603412783 #> 773 1.662236313 #> 774 1.724736313 #> 775 1.791402980 #> 776 1.791402980 #> 777 1.791402980 #> 778 1.791402980 #> 779 1.882312070 #> 780 1.982312070 #> 781 2.093423182 #> 782 2.218423182 #> 783 2.218423182 #> 784 2.385089848 #> 785 2.385089848 #> 786 2.635089848 #> 787 2.968423182 #> 788 2.968423182 #> 789 2.968423182 #>"},{"path":"https://hta-pharma.github.io/maicplus/reference/time_conversion.html","id":null,"dir":"Reference","previous_headings":"","what":"Get and Set Time Conversion Factors — set_time_conversion","title":"Get and Set Time Conversion Factors — set_time_conversion","text":"Get Set Time Conversion Factors","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/time_conversion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get and Set Time Conversion Factors — set_time_conversion","text":"","code":"set_time_conversion( default = \"days\", days = 1, weeks = 7, months = 365.25/12, years = 365.25 ) get_time_conversion(factor = c(\"days\", \"weeks\", \"months\", \"years\"))"},{"path":"https://hta-pharma.github.io/maicplus/reference/time_conversion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get and Set Time Conversion Factors — set_time_conversion","text":"default default time scale, commonly whichever factor = 1 days Factor divide data time units get time days weeks Factor divide data time units get time weeks months Factor divide data time units get time months years Factor divide data time units get time years factor Time factor get.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/time_conversion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get and Set Time Conversion Factors — set_time_conversion","text":"value returned. Conversion factors stored internally used within functions.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/time_conversion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get and Set Time Conversion Factors — set_time_conversion","text":"","code":"# The default time scale is days: set_time_conversion(default = \"days\", days = 1, weeks = 7, months = 365.25 / 12, years = 365.25) # Set the default time scale to years set_time_conversion( default = \"years\", days = 1 / 365.25, weeks = 1 / 52.17857, months = 1 / 12, years = 1 ) # Get time scale factors: get_time_conversion(\"years\") #> years #> 1 get_time_conversion(\"weeks\") #> weeks #> 0.01916496"},{"path":"https://hta-pharma.github.io/maicplus/reference/weighted_sat.html","id":null,"dir":"Reference","previous_headings":"","what":"Weighted object for single arm trial data — weighted_sat","title":"Weighted object for single arm trial data — weighted_sat","text":"Weighted object single arm trial data","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/weighted_sat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Weighted object for single arm trial data — weighted_sat","text":"","code":"weighted_sat"},{"path":"https://hta-pharma.github.io/maicplus/reference/weighted_sat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Weighted object for single arm trial data — weighted_sat","text":"maicplus_estimate_weights object created estimate_weights() containing data patient level data weights centered_colnames Columns used MAIC nr_missing Number observations missing data ess Expected sample size opt Information optim weight calculation boot Parameters bootstrap sample weights, NULL object","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/reference/weighted_twt.html","id":null,"dir":"Reference","previous_headings":"","what":"Weighted object for two arm trial data — weighted_twt","title":"Weighted object for two arm trial data — weighted_twt","text":"weighted patient data two arm trial generated centered patient data (centered_ipd_twt). weights calculated 100 bootstrap samples. object generated using following code:","code":"estimate_weights( data = centered_ipd_twt, centered_colnames = c( \"AGE_CENTERED\", \"AGE_MEDIAN_CENTERED\", \"AGE_SQUARED_CENTERED\", \"SEX_MALE_CENTERED\", \"ECOG0_CENTERED\", \"SMOKE_CENTERED\" ), n_boot_iteration = 100 )"},{"path":"https://hta-pharma.github.io/maicplus/reference/weighted_twt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Weighted object for two arm trial data — weighted_twt","text":"","code":"weighted_twt"},{"path":"https://hta-pharma.github.io/maicplus/reference/weighted_twt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Weighted object for two arm trial data — weighted_twt","text":"maicplus_estimate_weights object created estimate_weights() containing data patient level data weights centered_colnames Columns used MAIC nr_missing Number observations missing data ess Expected sample size opt Information optim weight calculation boot Parameters bootstrap sample weights 100 samples","code":""},{"path":[]},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/news/index.html","id":"new-features-0-1-0","dir":"Changelog","previous_headings":"","what":"New features","title":"maicplus 0.1.0","text":"Add initializer script.","code":""},{"path":"https://hta-pharma.github.io/maicplus/news/index.html","id":"enhancements-0-1-0","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"maicplus 0.1.0","text":"Documentation use initialize package.","code":""},{"path":"https://hta-pharma.github.io/maicplus/news/index.html","id":"bug-fixes-0-1-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"maicplus 0.1.0","text":"None.","code":""}] +[{"path":"https://hta-pharma.github.io/maicplus/main/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"hta-pharma. Author, maintainer.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"hta-pharma (2024). maicplus: Matching Adjusted Indirect Comparison. R package version 0.1.0, https://github.com/hta-pharma/maicplus/.","code":"@Manual{, title = {maicplus: Matching Adjusted Indirect Comparison}, author = {{hta-pharma}}, year = {2024}, note = {R package version 0.1.0}, url = {https://github.com/hta-pharma/maicplus/}, }"},{"path":"https://hta-pharma.github.io/maicplus/main/index.html","id":"maicplus","dir":"","previous_headings":"","what":"Matching Adjusted Indirect Comparison","title":"Matching Adjusted Indirect Comparison","text":"open source R package MAIC","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adrs_sat.html","id":null,"dir":"Reference","previous_headings":"","what":"Binary outcome data from single arm trial — adrs_sat","title":"Binary outcome data from single arm trial — adrs_sat","text":"Binary outcome data single arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adrs_sat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Binary outcome data from single arm trial — adrs_sat","text":"","code":"adrs_sat"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adrs_sat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Binary outcome data from single arm trial — adrs_sat","text":"data frame 500 rows 5 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm. AVAL Analysis value, dataset indicator response. PARAM Parameter type AVAL. RESPONSE Indicator response.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adrs_twt.html","id":null,"dir":"Reference","previous_headings":"","what":"Binary outcome data from two arm trial — adrs_twt","title":"Binary outcome data from two arm trial — adrs_twt","text":"Binary outcome data two arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adrs_twt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Binary outcome data from two arm trial — adrs_twt","text":"","code":"adrs_twt"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adrs_twt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Binary outcome data from two arm trial — adrs_twt","text":"data frame 1000 rows 5 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm, \"\", \"C\". AVAL Analysis value, dataset indicator response. PARAM Parameter type AVAL. RESPONSE Indicator response.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adsl_sat.html","id":null,"dir":"Reference","previous_headings":"","what":"Patient data from single arm study — adsl_sat","title":"Patient data from single arm study — adsl_sat","text":"Patient data single arm study","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adsl_sat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Patient data from single arm study — adsl_sat","text":"","code":"adsl_sat"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adsl_sat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Patient data from single arm study — adsl_sat","text":"data frame 500 rows 8 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm. AGE Age years baseline. SEX Sex patient recorded character \"Male\"/\"Female\". SMOKE Smoking status baseline integer 1/0. ECOG0 Indicator ECOG score = 0 baseline integer 1/0. N_PR_THER Number prior therapies received integer 1, 2, 3, 4. SEX_MALE Indicator SEX == \"Male\" numeric 1/0.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adsl_twt.html","id":null,"dir":"Reference","previous_headings":"","what":"Patient data from two arm trial — adsl_twt","title":"Patient data from two arm trial — adsl_twt","text":"Patient data two arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adsl_twt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Patient data from two arm trial — adsl_twt","text":"","code":"adsl_twt"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adsl_twt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Patient data from two arm trial — adsl_twt","text":"data frame 1000 rows 8 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm. AGE Age years baseline. SEX Sex patient recorded character \"Male\"/\"Female\" SMOKE Smoking status baseline integer 1/0. ECOG0 Indicator ECOG score = 0 baseline integer 1/0. N_PR_THER Number prior therapies received integer 1, 2, 3, 4. SEX_MALE Indicator SEX == \"Male\" numeric 1/0","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adtte_sat.html","id":null,"dir":"Reference","previous_headings":"","what":"Survival data from single arm trial — adtte_sat","title":"Survival data from single arm trial — adtte_sat","text":"Survival data single arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adtte_sat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Survival data from single arm trial — adtte_sat","text":"","code":"adtte_sat"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adtte_sat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Survival data from single arm trial — adtte_sat","text":"data frame 500 rows 10 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm, \"\". AVAL Analysis value dataset overall survival time days. AVALU Unit AVAL. PARAMCD Paramater code AVAL, \"OS\". PARAM Parameter name AVAL, \"Overall Survival. CNSR Censoring indicator 0/1. TIME Survival time days. EVENT Event indicator 0/1.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adtte_twt.html","id":null,"dir":"Reference","previous_headings":"","what":"Survival data from two arm trial — adtte_twt","title":"Survival data from two arm trial — adtte_twt","text":"Survival data two arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adtte_twt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Survival data from two arm trial — adtte_twt","text":"","code":"adtte_twt"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adtte_twt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Survival data from two arm trial — adtte_twt","text":"data frame 1000 rows 10 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm, \"\", \"C\". AVAL Analysis value dataset overall survival time days. AVALU Unit AVAL. PARAMCD Parameter code AVAL, \"OS\". PARAM Parameter name AVAL, \"Overall Survival. CNSR Censoring indicator 0/1. TIME Survival time days. EVENT Event indicator 0/1.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/agd.html","id":null,"dir":"Reference","previous_headings":"","what":"Aggregate effect modifier data from published study — agd","title":"Aggregate effect modifier data from published study — agd","text":"data formatted used center_ipd().","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/agd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aggregate effect modifier data from published study — agd","text":"","code":"agd"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/agd.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Aggregate effect modifier data from published study — agd","text":"data frame 3 rows 9 columns: STUDY study name, Study_XXXX ARM Study arm name total N Number observations study arm AGE_MEAN Mean age study arm AGE_MEDIAN Median age study arm AGE_SD Standard deviation age study arm SEX_MALE_COUNT Number male patients ECOG0_COUNT Number patients ECOG score = 0 SMOKE_COUNT Number smokers N_PR_THER_MEDIAN Median number prior therapies","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/basic_kmplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Basic Kaplan Meier (KM) plot function — basic_kmplot","title":"Basic Kaplan Meier (KM) plot function — basic_kmplot","text":"function can generate basic KM plot without risk set table appended bottom. single plot, can include 4 KM curves. depends number levels 'treatment' column input data.frame kmdat","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/basic_kmplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Basic Kaplan Meier (KM) plot function — basic_kmplot","text":"","code":"basic_kmplot( kmdat, endpoint_name = \"Time to Event Endpoint\", time_scale = NULL, time_grid = NULL, show_risk_set = TRUE, main_title = \"Kaplan-Meier Curves\", subplot_heights = NULL, suppress_plot_layout = FALSE, use_colors = NULL, use_line_types = NULL, use_pch_cex = 0.65, use_pch_alpha = 100 )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/basic_kmplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Basic Kaplan Meier (KM) plot function — basic_kmplot","text":"kmdat data.frame, must consist treatment, time (unit days), n.risk, censor, surv, similar output maicplus:::survfit_makeup endpoint_name string, name time event endpoint, show last line title time_scale string, time unit median survival time, taking value 'years', 'months', 'weeks' 'days' time_grid numeric vector unit time_scale, risk set table x axis km plot defined based time grid show_risk_set logical, show risk set table , TRUE default main_title string, main title KM plot subplot_heights numeric vector, heights argument graphic::layout(),NULL default means user use default setting suppress_plot_layout logical, suppress layout setting function user can specify layout outside function, FALSE default use_colors character vector length 4, colors KM curves, passed col lines() use_line_types numeric vector length 4, line type KM curves, passed lty lines() use_pch_cex scalar 0 1, point size indicate censored individuals KM curves, passed cex points() use_pch_alpha scalar 0 255, degree color transparency points indicate censored individuals KM curves, passed cex points()","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/basic_kmplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Basic Kaplan Meier (KM) plot function — basic_kmplot","text":"KM plot without risk set table appended bottom, 4 KM curves","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/basic_kmplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Basic Kaplan Meier (KM) plot function — basic_kmplot","text":"","code":"library(survival) data(adtte_sat) data(pseudo_ipd_sat) combined_data <- rbind(adtte_sat[, c(\"TIME\", \"EVENT\", \"ARM\")], pseudo_ipd_sat) kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data, conf.type = \"log-log\") kmdat <- do.call(rbind, survfit_makeup(kmobj)) kmdat$treatment <- factor(kmdat$treatment) # without risk set table basic_kmplot(kmdat, time_scale = \"month\", time_grid = seq(0, 20, by = 2), show_risk_set = FALSE, main_title = \"Kaplan-Meier Curves\", subplot_heights = NULL, suppress_plot_layout = FALSE, use_colors = NULL, use_line_types = NULL ) # with risk set table basic_kmplot(kmdat, time_scale = \"month\", time_grid = seq(0, 20, by = 2), show_risk_set = TRUE, main_title = \"Kaplan-Meier Curves\", subplot_heights = NULL, suppress_plot_layout = FALSE, use_colors = NULL, use_line_types = NULL )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/basic_kmplot2.html","id":null,"dir":"Reference","previous_headings":"","what":"Basic Kaplan Meier (KM) plot function using ggplot — basic_kmplot2","title":"Basic Kaplan Meier (KM) plot function using ggplot — basic_kmplot2","text":"function generates basic KM plot using ggplot.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/basic_kmplot2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Basic Kaplan Meier (KM) plot function using ggplot — basic_kmplot2","text":"","code":"basic_kmplot2( kmlist, kmlist_name, endpoint_name = \"Time to Event Endpoint\", show_risk_set = TRUE, main_title = \"Kaplan-Meier Curves\", break_x_by = NULL, censor = TRUE, xlab = \"Time\", xlim = NULL, use_colors = NULL, use_line_types = NULL )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/basic_kmplot2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Basic Kaplan Meier (KM) plot function using ggplot — basic_kmplot2","text":"kmlist list survfit object kmlist_name vector indicating treatment names survfit object endpoint_name string, name time event endpoint, show last line title show_risk_set logical, show risk set table , TRUE default main_title string, main title KM plot break_x_by bin parameter survminer censor indicator include censor information xlab label name x-axis plot xlim x limit x-axis plot use_colors character vector length 4, colors KM curves, passed 'col' lines() use_line_types numeric vector length 4, line type KM curves, passed lty lines()","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/basic_kmplot2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Basic Kaplan Meier (KM) plot function using ggplot — basic_kmplot2","text":"","code":"library(survival) data(adtte_sat) data(pseudo_ipd_sat) kmobj_A <- survfit(Surv(TIME, EVENT) ~ ARM, data = adtte_sat, conf.type = \"log-log\" ) kmobj_B <- survfit(Surv(TIME, EVENT) ~ ARM, data = pseudo_ipd_sat, conf.type = \"log-log\" ) kmlist <- list(kmobj_A = kmobj_A, kmobj_B = kmobj_B) kmlist_name <- c(\"A\", \"B\") basic_kmplot2(kmlist, kmlist_name) #> Warning: There was 1 warning in `mutate()`. #> ℹ In argument: `survtable = purrr::map2(...)`. #> Caused by warning: #> ! `select_()` was deprecated in dplyr 0.7.0. #> ℹ Please use `select()` instead. #> ℹ The deprecated feature was likely used in the survminer package. #> Please report the issue at ."},{"path":"https://hta-pharma.github.io/maicplus/main/reference/bucher.html","id":null,"dir":"Reference","previous_headings":"","what":"Bucher method for combining treatment effects — bucher","title":"Bucher method for combining treatment effects — bucher","text":"Given two treatment effects vs. C B vs. C derive treatment effects vs. B using Bucher method. Two-sided confidence interval Z-test p-value also calculated. Treatment effects standard errors log scale hazard ratio, odds ratio, risk ratio. Treatment effects standard errors natural scale risk difference mean difference.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/bucher.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bucher method for combining treatment effects — bucher","text":"","code":"bucher(trt, com, conf_lv = 0.95) # S3 method for class 'maicplus_bucher' print(x, ci_digits = 2, pval_digits = 3, exponentiate = FALSE, ...)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/bucher.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bucher method for combining treatment effects — bucher","text":"trt list two scalars study experimental arm. 'est' point estimate 'se' standard error treatment effect. time--event data, 'est' 'se' point estimate standard error log hazard ratio. binary data, 'est' 'se' point estimate standard error log odds ratio, log risk ratio, risk difference. continuous data, 'est' 'se' point estimate standard error mean difference. com trt, study control arm conf_lv numerical scalar, prescribe confidence level derive two-sided confidence interval treatment effect x maicplus_bucher object ci_digits integer, number decimal places point estimate derived confidence limits pval_digits integer, number decimal places display Z-test p-value exponentiate whether treatment effect confidence interval exponentiated. applies relative treatment effects. Default set false. ... used","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/bucher.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bucher method for combining treatment effects — bucher","text":"list 5 elements, est scalar, point estimate treatment effect se scalar, standard error treatment effect ci_l scalar, lower confidence limit two-sided CI prescribed nominal level conf_lv ci_u scalar, upper confidence limit two-sided CI prescribed nominal level conf_lv pval p-value Z-test, null hypothesis est zero","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/bucher.html","id":"methods-by-generic-","dir":"Reference","previous_headings":"","what":"Methods (by generic)","title":"Bucher method for combining treatment effects — bucher","text":"print(maicplus_bucher): Print method maicplus_bucher objects","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/bucher.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bucher method for combining treatment effects — bucher","text":"","code":"trt <- list(est = log(1.1), se = 0.2) com <- list(est = log(1.3), se = 0.18) result <- bucher(trt, com, conf_lv = 0.9) print(result, ci_digits = 3, pval_digits = 3) #> result pvalue #> \"-0.167 [-0.610; 0.276]\" \"0.535\""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/calculate_weights_legend.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","title":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","text":"Calculates ESS reduction median weights used create legend weights plot","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/calculate_weights_legend.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","text":"","code":"calculate_weights_legend(weighted_data)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/calculate_weights_legend.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","text":"weighted_data object returned calculating weights using estimate_weights","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/calculate_weights_legend.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","text":"list ESS, ESS reduction, median value scaled unscaled weights, missing count","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/calculate_weights_legend.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","text":"","code":"data(\"weighted_sat\") calculate_weights_legend(weighted_sat) #> $ess #> [1] 121.99 #> #> $ess_reduction #> [1] 75.6 #> #> $wt_median #> [1] 0.0567 #> #> $wt_scaled_median #> [1] 0.1635 #> #> $nr_na #> [1] 0 #>"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/center_ipd.html","id":null,"dir":"Reference","previous_headings":"","what":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","title":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","text":"function subtracts IPD variables (prognostic variables /effect modifiers) aggregate data averages. centering needed order calculate weights. IPD aggregate data variable names match.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/center_ipd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","text":"","code":"center_ipd(ipd, agd)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/center_ipd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","text":"ipd IPD variable names match aggregate data names without suffix. involve either changing aggregate data name ipd name. instance, binarize SEX variable MALE reference using dummize_ipd, function names new variable SEX_MALE. case, SEX_MALE also available aggregate data. agd pre-processed aggregate data contain STUDY, ARM, N. Variable names followed legal suffixes (.e. MEAN, MEDIAN, SD, PROP). Note COUNT suffix longer accepted.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/center_ipd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","text":"centered ipd using aggregate level data averages","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/center_ipd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","text":"","code":"data(adsl_sat) data(agd) agd <- process_agd(agd) ipd_centered <- center_ipd(ipd = adsl_sat, agd = agd)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/centered_ipd_sat.html","id":null,"dir":"Reference","previous_headings":"","what":"Centered patient data from single arm trial — centered_ipd_sat","title":"Centered patient data from single arm trial — centered_ipd_sat","text":"Centered patient data single arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/centered_ipd_sat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Centered patient data from single arm trial — centered_ipd_sat","text":"","code":"centered_ipd_sat"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/centered_ipd_sat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Centered patient data from single arm trial — centered_ipd_sat","text":"data frame 500 rows 14 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm. AGE Age years baseline. SEX Sex patient recorded character \"Male\"/\"Female\". SMOKE Smoking status baseline integer 1/0. ECOG0 Indicator ECOG score = 0 baseline integer 1/0. N_PR_THER Number prior therapies received integer 1, 2, 3, 4. SEX_MALE Indicator SEX == \"Male\" numeric 1/0. AGE_CENTERED Age years baseline relative average aggregate data agd. AGE_MEDIAN_CENTERED AGE greater/less MEDIAN_AGE agd coded 1/0 centered 0.5. AGE_SQUARED_CENTERED AGE squared centered respect AGE agd. squared age aggregate data derived \\(E(X^2)\\) term variance formula. SEX_MALE_CENTERED SEX_MALE centered proportion male patients agd ECOG0_CENTERED ECOG0 centered proportion ECOG0 agd SMOKE_CENTERED SMOKE centered proportion SMOKE agd N_PR_THER_MEDIAN_CENTERED N_PR_THER centered median agd.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/centered_ipd_twt.html","id":null,"dir":"Reference","previous_headings":"","what":"Centered patient data from two arm trial — centered_ipd_twt","title":"Centered patient data from two arm trial — centered_ipd_twt","text":"Centered patient data two arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/centered_ipd_twt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Centered patient data from two arm trial — centered_ipd_twt","text":"","code":"centered_ipd_twt"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/centered_ipd_twt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Centered patient data from two arm trial — centered_ipd_twt","text":"data frame 1000 rows 14 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm. AGE Age years baseline. SEX Sex patient recorded character \"Male\"/\"Female\". SMOKE Smoking status baseline integer 1/0. ECOG0 Indicator ECOG score = 0 baseline integer 1/0. N_PR_THER Number prior therapies received integer 1, 2, 3, 4. SEX_MALE Indicator SEX == \"Male\" numeric 1/0. AGE_CENTERED Age years baseline relative average aggregate data agd. AGE_MEDIAN_CENTERED AGE greater/less MEDIAN_AGE agd coded 1/0 centered 0.5. AGE_SQUARED_CENTERED AGE squared centered respect AGE agd. squared age aggregate data derived \\(E(X^2)\\) term variance formula. SEX_MALE_CENTERED SEX_MALE centered proportion male patients agd ECOG0_CENTERED ECOG0 centered proportion ECOG0 agd SMOKE_CENTERED SMOKE centered proportion SMOKE agd N_PR_THER_MEDIAN_CENTERED N_PR_THER centered median agd.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/check_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Check to see if weights are optimized correctly — check_weights","title":"Check to see if weights are optimized correctly — check_weights","text":"function checks see optimization done properly checking covariate averages adjustment.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/check_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check to see if weights are optimized correctly — check_weights","text":"","code":"check_weights(weighted_data, processed_agd) # S3 method for class 'maicplus_check_weights' print( x, mean_digits = 2, prop_digits = 2, sd_digits = 3, digits = getOption(\"digits\"), ... )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/check_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check to see if weights are optimized correctly — check_weights","text":"weighted_data object returned calculating weights using estimate_weights processed_agd data frame, object returned using process_agd aggregated data following naming convention x object check_weights mean_digits number digits rounding mean columns output prop_digits number digits rounding proportion columns output sd_digits number digits rounding mean columns output digits minimal number significant digits, see print.default. ... arguments print.data.frame","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/check_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check to see if weights are optimized correctly — check_weights","text":"data.frame weighted unweighted covariate averages IPD, average aggregate data, sum inner products covariate \\(x_i\\) weights (\\(exp(x_i\\beta)\\))","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/check_weights.html","id":"methods-by-generic-","dir":"Reference","previous_headings":"","what":"Methods (by generic)","title":"Check to see if weights are optimized correctly — check_weights","text":"print(maicplus_check_weights): Print method check_weights objects","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/check_weights.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check to see if weights are optimized correctly — check_weights","text":"","code":"data(weighted_sat) data(agd) check_weights(weighted_sat, process_agd(agd)) #> covariate match_stat internal_trial internal_trial_after_weighted #> 1 AGE Mean 59.850 51.00 #> 2 AGE Median 59.000 49.00 #> 3 AGE SD 9.011 3.25 #> 4 SEX_MALE Prop 0.380 0.49 #> 5 ECOG0 Prop 0.410 0.35 #> 6 SMOKE Prop 0.320 0.19 #> external_trial sum_centered_IPD_with_weights #> 1 51.00 0.0000 #> 2 49.00 0.0000 #> 3 3.25 -0.0045 #> 4 0.49 0.0000 #> 5 0.35 0.0000 #> 6 0.19 0.0000"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/complete_agd.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate pooled arm statistics in Aggregated Data (AgD) based on arm-specific statistics — complete_agd","title":"Calculate pooled arm statistics in Aggregated Data (AgD) based on arm-specific statistics — complete_agd","text":"convenient function pool arm statistics. function called within process_agd ARM equal \"Total\". Note pooled median calculated approximation.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/complete_agd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate pooled arm statistics in Aggregated Data (AgD) based on arm-specific statistics — complete_agd","text":"","code":"complete_agd(use_agd)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/complete_agd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate pooled arm statistics in Aggregated Data (AgD) based on arm-specific statistics — complete_agd","text":"use_agd aggregated data processed within process_agd","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/complete_agd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate pooled arm statistics in Aggregated Data (AgD) based on arm-specific statistics — complete_agd","text":"Complete N, count, mean, sd, median pooled arm","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/dummize_ipd.html","id":null,"dir":"Reference","previous_headings":"","what":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","title":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","text":"convenient function convert categorical variables dummy binary variables. especially useful variable two factors. Note original variable kept variable dummized.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/dummize_ipd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","text":"","code":"dummize_ipd(raw_ipd, dummize_cols, dummize_ref_level)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/dummize_ipd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","text":"raw_ipd ipd data contains variable dummize dummize_cols vector column names binarize dummize_ref_level vector reference level variables binarize","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/dummize_ipd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","text":"ipd dummized columns","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/dummize_ipd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","text":"","code":"data(adsl_twt) dummize_ipd(adsl_twt, dummize_cols = c(\"SEX\"), dummize_ref_level = c(\"Male\")) #> USUBJID ARM AGE SEX SMOKE ECOG0 N_PR_THER SEX_MALE SEX_FEMALE #> 1 xx1 A 45 Male 0 0 4 1 0 #> 2 xx2 A 71 Male 0 0 3 1 0 #> 3 xx3 A 58 Male 1 1 2 1 0 #> 4 xx4 A 48 Female 0 1 4 0 1 #> 5 xx5 A 69 Male 0 1 4 1 0 #> 6 xx6 A 48 Female 0 1 4 0 1 #> 7 xx7 A 47 Male 1 0 3 1 0 #> 8 xx8 A 61 Male 1 0 1 1 0 #> 9 xx9 A 54 Female 1 1 1 0 1 #> 10 xx10 A 56 Female 1 0 3 0 1 #> 11 xx11 A 63 Female 0 0 4 0 1 #> 12 xx12 A 50 Female 0 0 1 0 1 #> 13 xx13 A 57 Male 0 1 3 1 0 #> 14 xx14 A 62 Female 1 1 1 0 1 #> 15 xx15 A 57 Female 0 1 3 0 1 #> 16 xx16 A 66 Male 0 0 2 1 0 #> 17 xx17 A 75 Male 1 1 3 1 0 #> 18 xx18 A 47 Female 0 0 4 0 1 #> 19 xx19 A 57 Male 0 0 3 1 0 #> 20 xx20 A 54 Male 0 0 3 1 0 #> 21 xx21 A 55 Male 1 0 3 1 0 #> 22 xx22 A 64 Male 0 1 3 1 0 #> 23 xx23 A 53 Female 1 0 3 0 1 #> 24 xx24 A 58 Male 1 1 2 1 0 #> 25 xx25 A 47 Male 0 0 1 1 0 #> 26 xx26 A 60 Female 1 0 1 0 1 #> 27 xx27 A 49 Female 0 1 3 0 1 #> 28 xx28 A 55 Female 0 0 1 0 1 #> 29 xx29 A 66 Female 0 1 2 0 1 #> 30 xx30 A 58 Male 0 1 4 1 0 #> 31 xx31 A 49 Male 0 1 4 1 0 #> 32 xx32 A 61 Male 0 0 4 1 0 #> 33 xx33 A 66 Male 1 0 3 1 0 #> 34 xx34 A 45 Male 0 0 1 1 0 #> 35 xx35 A 59 Female 1 1 2 0 1 #> 36 xx36 A 74 Female 1 0 4 0 1 #> 37 xx37 A 73 Female 0 0 3 0 1 #> 38 xx38 A 74 Male 0 1 4 1 0 #> 39 xx39 A 54 Male 0 0 1 1 0 #> 40 xx40 A 58 Female 1 1 1 0 1 #> 41 xx41 A 61 Female 0 1 3 0 1 #> 42 xx42 A 47 Female 1 1 2 0 1 #> 43 xx43 A 73 Female 1 1 2 0 1 #> 44 xx44 A 68 Male 0 0 1 1 0 #> 45 xx45 A 49 Female 0 0 3 0 1 #> 46 xx46 A 71 Female 0 0 2 0 1 #> 47 xx47 A 70 Male 0 1 4 1 0 #> 48 xx48 A 62 Female 1 0 1 0 1 #> 49 xx49 A 49 Male 0 0 1 1 0 #> 50 xx50 A 74 Female 0 0 1 0 1 #> 51 xx51 A 46 Female 0 1 3 0 1 #> 52 xx52 A 68 Female 1 0 3 0 1 #> 53 xx53 A 46 Male 1 0 2 1 0 #> 54 xx54 A 75 Female 1 1 3 0 1 #> 55 xx55 A 47 Female 0 0 3 0 1 #> 56 xx56 A 56 Male 0 1 3 1 0 #> 57 xx57 A 72 Female 0 0 3 0 1 #> 58 xx58 A 57 Male 1 1 4 1 0 #> 59 xx59 A 46 Male 0 0 1 1 0 #> 60 xx60 A 56 Female 1 1 1 0 1 #> 61 xx61 A 73 Male 0 1 2 1 0 #> 62 xx62 A 60 Female 1 1 3 0 1 #> 63 xx63 A 75 Male 0 0 2 1 0 #> 64 xx64 A 69 Female 1 1 2 0 1 #> 65 xx65 A 47 Female 0 1 1 0 1 #> 66 xx66 A 74 Male 0 0 4 1 0 #> 67 xx67 A 71 Female 0 1 1 0 1 #> 68 xx68 A 49 Female 1 1 1 0 1 #> 69 xx69 A 68 Male 0 0 3 1 0 #> 70 xx70 A 49 Male 0 1 1 1 0 #> 71 xx71 A 70 Male 0 1 1 1 0 #> 72 xx72 A 45 Female 0 0 2 0 1 #> 73 xx73 A 47 Female 0 1 3 0 1 #> 74 xx74 A 58 Male 0 1 3 1 0 #> 75 xx75 A 49 Female 0 1 4 0 1 #> 76 xx76 A 68 Female 0 0 1 0 1 #> 77 xx77 A 60 Male 0 0 4 1 0 #> 78 xx78 A 45 Female 1 0 1 0 1 #> 79 xx79 A 57 Female 0 0 1 0 1 #> 80 xx80 A 50 Female 0 1 1 0 1 #> 81 xx81 A 63 Male 0 1 3 1 0 #> 82 xx82 A 47 Female 0 0 2 0 1 #> 83 xx83 A 68 Female 0 1 4 0 1 #> 84 xx84 A 51 Male 0 0 4 1 0 #> 85 xx85 A 60 Male 0 0 1 1 0 #> 86 xx86 A 52 Female 1 0 4 0 1 #> 87 xx87 A 69 Male 1 1 1 1 0 #> 88 xx88 A 70 Female 0 1 4 0 1 #> 89 xx89 A 72 Male 0 0 2 1 0 #> 90 xx90 A 46 Female 0 1 1 0 1 #> 91 xx91 A 51 Male 1 0 4 1 0 #> 92 xx92 A 69 Female 0 1 1 0 1 #> 93 xx93 A 66 Male 0 0 1 1 0 #> 94 xx94 A 73 Male 0 1 1 1 0 #> 95 xx95 A 73 Female 1 0 3 0 1 #> 96 xx96 A 62 Female 0 1 2 0 1 #> 97 xx97 A 55 Female 0 0 4 0 1 #> 98 xx98 A 67 Male 1 0 3 1 0 #> 99 xx99 A 54 Female 1 0 3 0 1 #> 100 xx100 A 52 Female 1 1 4 0 1 #> 101 xx101 A 57 Male 0 0 2 1 0 #> 102 xx102 A 57 Female 1 1 3 0 1 #> 103 xx103 A 57 Male 0 0 3 1 0 #> 104 xx104 A 67 Female 1 1 2 0 1 #> 105 xx105 A 67 Female 1 1 2 0 1 #> 106 xx106 A 74 Female 1 1 2 0 1 #> 107 xx107 A 72 Female 1 0 2 0 1 #> 108 xx108 A 73 Female 0 0 3 0 1 #> 109 xx109 A 57 Female 0 0 4 0 1 #> 110 xx110 A 69 Female 1 0 1 0 1 #> 111 xx111 A 55 Male 0 0 1 1 0 #> 112 xx112 A 74 Female 0 0 4 0 1 #> 113 xx113 A 68 Female 0 0 4 0 1 #> 114 xx114 A 53 Male 0 0 2 1 0 #> 115 xx115 A 69 Male 0 0 2 1 0 #> 116 xx116 A 68 Male 0 1 2 1 0 #> 117 xx117 A 58 Male 0 0 1 1 0 #> 118 xx118 A 64 Female 0 0 3 0 1 #> 119 xx119 A 71 Male 0 0 1 1 0 #> 120 xx120 A 69 Female 0 1 2 0 1 #> 121 xx121 A 64 Female 1 0 4 0 1 #> 122 xx122 A 64 Male 1 0 1 1 0 #> 123 xx123 A 55 Male 0 1 3 1 0 #> 124 xx124 A 74 Male 0 0 3 1 0 #> 125 xx125 A 50 Male 0 1 3 1 0 #> 126 xx126 A 68 Male 0 1 1 1 0 #> 127 xx127 A 60 Male 0 0 2 1 0 #> 128 xx128 A 59 Female 0 0 3 0 1 #> 129 xx129 A 71 Female 1 0 3 0 1 #> 130 xx130 A 69 Male 0 1 4 1 0 #> 131 xx131 A 56 Female 0 1 1 0 1 #> 132 xx132 A 51 Male 0 1 3 1 0 #> 133 xx133 A 65 Male 0 1 2 1 0 #> 134 xx134 A 45 Male 1 1 3 1 0 #> 135 xx135 A 49 Female 0 0 2 0 1 #> 136 xx136 A 74 Female 1 0 4 0 1 #> 137 xx137 A 71 Female 1 1 3 0 1 #> 138 xx138 A 65 Male 0 0 1 1 0 #> 139 xx139 A 67 Female 0 0 2 0 1 #> 140 xx140 A 48 Female 0 0 3 0 1 #> 141 xx141 A 70 Female 0 1 4 0 1 #> 142 xx142 A 72 Female 0 0 1 0 1 #> 143 xx143 A 53 Male 1 1 4 1 0 #> 144 xx144 A 68 Female 1 0 3 0 1 #> 145 xx145 A 65 Male 0 1 1 1 0 #> 146 xx146 A 70 Female 1 1 4 0 1 #> 147 xx147 A 58 Male 0 0 2 1 0 #> 148 xx148 A 68 Female 0 1 4 0 1 #> 149 xx149 A 54 Female 0 0 4 0 1 #> 150 xx150 A 75 Female 1 1 4 0 1 #> 151 xx151 A 49 Female 1 1 3 0 1 #> 152 xx152 A 60 Male 0 0 4 1 0 #> 153 xx153 A 45 Female 1 0 4 0 1 #> 154 xx154 A 73 Female 1 0 3 0 1 #> 155 xx155 A 71 Female 0 1 3 0 1 #> 156 xx156 A 73 Female 0 0 3 0 1 #> 157 xx157 A 58 Female 0 0 4 0 1 #> 158 xx158 A 59 Female 0 1 2 0 1 #> 159 xx159 A 75 Female 0 1 2 0 1 #> 160 xx160 A 65 Male 0 0 2 1 0 #> 161 xx161 A 48 Male 1 0 2 1 0 #> 162 xx162 A 63 Female 0 1 2 0 1 #> 163 xx163 A 74 Female 0 0 3 0 1 #> 164 xx164 A 64 Female 0 0 3 0 1 #> 165 xx165 A 49 Female 0 0 1 0 1 #> 166 xx166 A 56 Female 0 1 4 0 1 #> 167 xx167 A 56 Female 0 1 2 0 1 #> 168 xx168 A 48 Female 0 0 1 0 1 #> 169 xx169 A 65 Male 0 1 3 1 0 #> 170 xx170 A 53 Female 0 0 3 0 1 #> 171 xx171 A 72 Male 0 0 2 1 0 #> 172 xx172 A 75 Female 1 0 4 0 1 #> 173 xx173 A 70 Female 0 1 2 0 1 #> 174 xx174 A 53 Female 1 0 2 0 1 #> 175 xx175 A 45 Female 1 1 3 0 1 #> 176 xx176 A 53 Female 1 1 3 0 1 #> 177 xx177 A 52 Female 0 1 2 0 1 #> 178 xx178 A 61 Female 1 0 4 0 1 #> 179 xx179 A 70 Male 0 0 3 1 0 #> 180 xx180 A 58 Female 0 0 3 0 1 #> 181 xx181 A 54 Male 0 0 1 1 0 #> 182 xx182 A 53 Male 0 0 2 1 0 #> 183 xx183 A 74 Male 0 0 2 1 0 #> 184 xx184 A 64 Male 0 1 3 1 0 #> 185 xx185 A 52 Male 0 0 1 1 0 #> 186 xx186 A 73 Female 0 0 3 0 1 #> 187 xx187 A 55 Female 1 0 4 0 1 #> 188 xx188 A 71 Female 0 0 3 0 1 #> 189 xx189 A 57 Female 1 0 3 0 1 #> 190 xx190 A 49 Female 1 0 2 0 1 #> 191 xx191 A 69 Male 0 0 4 1 0 #> 192 xx192 A 74 Female 1 0 2 0 1 #> 193 xx193 A 59 Female 0 0 3 0 1 #> 194 xx194 A 53 Male 0 0 4 1 0 #> 195 xx195 A 52 Female 0 1 3 0 1 #> 196 xx196 A 47 Female 1 1 1 0 1 #> 197 xx197 A 61 Female 0 0 4 0 1 #> 198 xx198 A 51 Female 0 0 2 0 1 #> 199 xx199 A 62 Female 1 0 1 0 1 #> 200 xx200 A 59 Female 1 0 2 0 1 #> 201 xx201 A 58 Male 0 0 2 1 0 #> 202 xx202 A 61 Female 0 1 4 0 1 #> 203 xx203 A 45 Female 0 0 1 0 1 #> 204 xx204 A 59 Male 1 1 2 1 0 #> 205 xx205 A 58 Female 1 0 2 0 1 #> 206 xx206 A 67 Male 0 0 1 1 0 #> 207 xx207 A 51 Female 1 0 2 0 1 #> 208 xx208 A 68 Male 1 1 3 1 0 #> 209 xx209 A 53 Female 1 0 2 0 1 #> 210 xx210 A 64 Male 1 0 3 1 0 #> 211 xx211 A 61 Male 0 1 1 1 0 #> 212 xx212 A 52 Male 1 0 2 1 0 #> 213 xx213 A 57 Female 0 1 4 0 1 #> 214 xx214 A 57 Female 1 0 2 0 1 #> 215 xx215 A 49 Female 0 0 4 0 1 #> 216 xx216 A 51 Male 1 1 3 1 0 #> 217 xx217 A 60 Female 0 0 2 0 1 #> 218 xx218 A 60 Female 1 0 1 0 1 #> 219 xx219 A 71 Female 0 0 3 0 1 #> 220 xx220 A 54 Female 0 0 1 0 1 #> 221 xx221 A 51 Male 1 0 1 1 0 #> 222 xx222 A 71 Male 0 1 3 1 0 #> 223 xx223 A 47 Male 0 0 2 1 0 #> 224 xx224 A 65 Male 0 0 1 1 0 #> 225 xx225 A 53 Female 0 0 1 0 1 #> 226 xx226 A 56 Female 0 0 1 0 1 #> 227 xx227 A 51 Male 1 0 4 1 0 #> 228 xx228 A 68 Female 0 0 2 0 1 #> 229 xx229 A 75 Female 0 1 1 0 1 #> 230 xx230 A 49 Female 0 0 2 0 1 #> 231 xx231 A 74 Male 1 0 1 1 0 #> 232 xx232 A 66 Female 0 0 1 0 1 #> 233 xx233 A 74 Female 0 0 1 0 1 #> 234 xx234 A 62 Female 0 1 2 0 1 #> 235 xx235 A 53 Female 0 0 1 0 1 #> 236 xx236 A 62 Female 0 1 1 0 1 #> 237 xx237 A 70 Female 1 0 3 0 1 #> 238 xx238 A 60 Female 1 0 2 0 1 #> 239 xx239 A 72 Male 0 0 1 1 0 #> 240 xx240 A 74 Female 1 0 3 0 1 #> 241 xx241 A 47 Female 1 0 3 0 1 #> 242 xx242 A 54 Female 0 1 3 0 1 #> 243 xx243 A 65 Female 0 1 2 0 1 #> 244 xx244 A 74 Male 0 1 1 1 0 #> 245 xx245 A 61 Male 0 1 2 1 0 #> 246 xx246 A 54 Female 1 0 3 0 1 #> 247 xx247 A 65 Male 0 0 3 1 0 #> 248 xx248 A 72 Female 1 0 3 0 1 #> 249 xx249 A 71 Female 1 1 2 0 1 #> 250 xx250 A 65 Male 0 0 3 1 0 #> 251 xx251 A 58 Male 0 1 1 1 0 #> 252 xx252 A 46 Male 0 0 1 1 0 #> 253 xx253 A 53 Female 1 0 4 0 1 #> 254 xx254 A 71 Female 1 0 4 0 1 #> 255 xx255 A 47 Male 0 1 3 1 0 #> 256 xx256 A 45 Male 0 0 1 1 0 #> 257 xx257 A 50 Male 0 0 3 1 0 #> 258 xx258 A 67 Female 1 0 3 0 1 #> 259 xx259 A 72 Male 1 0 2 1 0 #> 260 xx260 A 45 Female 0 1 2 0 1 #> 261 xx261 A 75 Female 1 0 1 0 1 #> 262 xx262 A 65 Male 0 0 3 1 0 #> 263 xx263 A 60 Female 0 1 3 0 1 #> 264 xx264 A 75 Female 0 1 4 0 1 #> 265 xx265 A 60 Female 1 0 4 0 1 #> 266 xx266 A 49 Female 1 0 2 0 1 #> 267 xx267 A 58 Female 0 1 1 0 1 #> 268 xx268 A 57 Male 1 1 3 1 0 #> 269 xx269 A 69 Male 1 0 2 1 0 #> 270 xx270 A 51 Male 0 1 1 1 0 #> 271 xx271 A 54 Female 1 0 4 0 1 #> 272 xx272 A 55 Male 0 1 3 1 0 #> 273 xx273 A 49 Female 0 0 4 0 1 #> 274 xx274 A 74 Female 1 0 1 0 1 #> 275 xx275 A 55 Male 1 0 1 1 0 #> 276 xx276 A 52 Female 1 0 1 0 1 #> 277 xx277 A 65 Male 0 0 2 1 0 #> 278 xx278 A 70 Female 1 0 1 0 1 #> 279 xx279 A 66 Female 1 1 2 0 1 #> 280 xx280 A 63 Female 0 1 4 0 1 #> 281 xx281 A 61 Female 0 1 3 0 1 #> 282 xx282 A 65 Male 0 1 2 1 0 #> 283 xx283 A 73 Male 0 0 2 1 0 #> 284 xx284 A 55 Female 1 1 4 0 1 #> 285 xx285 A 56 Female 1 1 4 0 1 #> 286 xx286 A 68 Female 0 1 1 0 1 #> 287 xx287 A 74 Female 1 0 4 0 1 #> 288 xx288 A 67 Female 0 0 2 0 1 #> 289 xx289 A 66 Male 0 1 3 1 0 #> 290 xx290 A 48 Female 0 0 3 0 1 #> 291 xx291 A 49 Female 1 1 3 0 1 #> 292 xx292 A 60 Female 1 1 1 0 1 #> 293 xx293 A 69 Female 0 1 4 0 1 #> 294 xx294 A 58 Female 0 1 3 0 1 #> 295 xx295 A 45 Female 1 1 4 0 1 #> 296 xx296 A 49 Female 0 1 1 0 1 #> 297 xx297 A 67 Female 1 0 4 0 1 #> 298 xx298 A 63 Male 0 0 4 1 0 #> 299 xx299 A 50 Female 0 0 2 0 1 #> 300 xx300 A 68 Female 0 1 3 0 1 #> 301 xx301 A 53 Male 0 1 2 1 0 #> 302 xx302 A 63 Male 0 1 2 1 0 #> 303 xx303 A 58 Male 0 0 4 1 0 #> 304 xx304 A 70 Female 0 1 4 0 1 #> 305 xx305 A 56 Female 0 0 1 0 1 #> 306 xx306 A 56 Male 0 1 3 1 0 #> 307 xx307 A 61 Female 0 0 3 0 1 #> 308 xx308 A 72 Male 0 0 2 1 0 #> 309 xx309 A 51 Male 0 1 1 1 0 #> 310 xx310 A 72 Male 0 0 4 1 0 #> 311 xx311 A 64 Female 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1"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ess_footnote_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Note on Expected Sample Size Reduction — ess_footnote_text","title":"Note on Expected Sample Size Reduction — ess_footnote_text","text":"Note Expected Sample Size Reduction","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ess_footnote_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Note on Expected Sample Size Reduction — ess_footnote_text","text":"","code":"ess_footnote_text(width = 0.9 * getOption(\"width\"))"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ess_footnote_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Note on Expected Sample Size Reduction — ess_footnote_text","text":"width Number characters break string new lines (\\n).","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ess_footnote_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Note on Expected Sample Size Reduction — ess_footnote_text","text":"character string","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/estimate_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive individual weights in the matching step of MAIC — estimate_weights","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"Assuming data properly processed, function takes individual patient data (IPD) centered covariates (effect modifiers /prognostic variables) input, generates weights individual IPD trial match covariates aggregate data. plot function displays individuals weights key summary top right legend includes median weight, effective sample size (ESS), reduction percentage (percent ESS reduced original sample size). two options plotting: base R plot ggplot. default base R plot plot unscaled scaled separately. default ggplot plot unscaled scaled weights plot.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/estimate_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"","code":"estimate_weights( data, centered_colnames = NULL, start_val = 0, method = \"BFGS\", n_boot_iteration = NULL, set_seed_boot = 1234, boot_strata = \"ARM\", ... ) # S3 method for class 'maicplus_estimate_weights' plot( x, ggplot = FALSE, bin_col = \"#6ECEB2\", vline_col = \"#688CE8\", main_title = NULL, scaled_weights = TRUE, bins = 50, ... )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/estimate_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"data numeric matrix, centered covariates IPD, missing value cell allowed centered_colnames character numeric vector (column indicators) centered covariates start_val scalar, starting value coefficients propensity score regression method string, name optimization algorithm (see 'method' argument base::optim()) default \"BFGS\", options \"Nelder-Mead\", \"CG\", \"L-BFGS-B\", \"SANN\", \"Brent\" n_boot_iteration integer, number bootstrap iterations. default NULL means bootstrapping procedure triggered, hence element \"boot\" output list object NULL. set_seed_boot scalar, random seed conducting bootstrapping, relevant n_boot_iteration NULL. default, use seed 1234 boot_strata character vector column names data defines strata bootstrapping. ensures samples drawn proportionally defined stratum. NULL, stratification bootstrapping process. default, \"ARM\" ... Additional control parameters passed stats::optim. x object estimate_weights ggplot indicator print base weights plot ggplot weights plot bin_col string, color bins histogram vline_col string, color vertical line histogram main_title title plot. ggplot, name scaled weights plot unscaled weights plot, respectively. scaled_weights (base plot ) indicator using scaled weights instead regular weights bins (ggplot ) number bin parameter use","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/estimate_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"list following 4 elements, data data.frame, includes input data appended column 'weights' 'scaled_weights'. Scaled weights summation number rows data missing value effect modifiers centered_colnames column names centered effect modifiers data nr_missing number rows data least 1 missing value specified centered effect modifiers ess effective sample size, square sum divided sum squares opt R object returned base::optim(), assess convergence details boot_strata 'strata' boot::boot object boot_seed column names data stratification factors boot n 2 k array NA, n equals number rows data, k equals n_boot_iteration. 2 columns second dimension include column numeric indexes rows data selected bootstrapping iteration column weights. boot NA argument n_boot_iteration set NULL","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/estimate_weights.html","id":"methods-by-generic-","dir":"Reference","previous_headings":"","what":"Methods (by generic)","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"plot(maicplus_estimate_weights): Plot method estimate_weights objects","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/estimate_weights.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"","code":"data(centered_ipd_sat) centered_colnames <- grep(\"_CENTERED\", colnames(centered_ipd_sat), value = TRUE) weighted_data <- estimate_weights(data = centered_ipd_sat, centered_colnames = centered_colnames) # \\donttest{ # To later estimate bootstrap confidence intervals, we calculate the weights # for the bootstrap samples: weighted_data_boot <- estimate_weights( data = centered_ipd_sat, centered_colnames = centered_colnames, n_boot_iteration = 100 ) # } plot(weighted_sat) if (requireNamespace(\"ggplot2\")) { plot(weighted_sat, ggplot = TRUE) }"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ext_tte_transfer.html","id":null,"dir":"Reference","previous_headings":"","what":"helper function: transform TTE ADaM data to suitable input for survival R package — ext_tte_transfer","title":"helper function: transform TTE ADaM data to suitable input for survival R package — ext_tte_transfer","text":"helper function: transform TTE ADaM data suitable input survival R package","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ext_tte_transfer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"helper function: transform TTE ADaM data to suitable input for survival R package — ext_tte_transfer","text":"","code":"ext_tte_transfer(dd, time_scale = \"months\", trt = NULL)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ext_tte_transfer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"helper function: transform TTE ADaM data to suitable input for survival R package — ext_tte_transfer","text":"dd data frame, ADTTE read via haven::read_sas time_scale character string, 'years', 'months', 'weeks' 'days', time unit median survival time trt values include treatment column","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ext_tte_transfer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"helper function: transform TTE ADaM data to suitable input for survival R package — ext_tte_transfer","text":"data frame can used input survival::Surv","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/find_SE_from_CI.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","title":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","text":"Comparator studies often report confidence interval treatment effects. function calculates standard error treatment effect given reported confidence interval. relative treatment effect (.e. hazard ratio, odds ratio, risk ratio), function log confidence interval. risk difference mean difference, log confidence interval. option log confidence interval controlled 'logged' parameter.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/find_SE_from_CI.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","text":"","code":"find_SE_from_CI( CI_lower = NULL, CI_upper = NULL, CI_perc = 0.95, logged = TRUE )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/find_SE_from_CI.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","text":"CI_lower Reported lower percentile value treatment effect CI_upper Reported upper percentile value treatment effect CI_perc Percentage confidence interval reported logged Whether confidence interval logged. relative treatment effect, log applied estimated log treatment effect approximately normally distributed.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/find_SE_from_CI.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","text":"Standard error log relative treatment effect 'logged' true standard error treatment effect 'logged' false","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/find_SE_from_CI.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","text":"","code":"find_SE_from_CI(CI_lower = 0.55, CI_upper = 0.90, CI_perc = 0.95) #> [1] 0.1256341"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/get_pseudo_ipd_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","title":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","text":"Create pseudo IPD given aggregated binary data","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/get_pseudo_ipd_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","text":"","code":"get_pseudo_ipd_binary(binary_agd, format = c(\"stacked\", \"unstacked\"))"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/get_pseudo_ipd_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","text":"binary_agd data.frame take different formats depending format format string, \"stacked\" \"unstacked\"","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/get_pseudo_ipd_binary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","text":"data.frame pseudo binary IPD, columns USUBJID, ARM, RESPONSE","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/get_pseudo_ipd_binary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","text":"","code":"# example of unstacked testdat <- data.frame(Yes = 280, No = 120) rownames(testdat) <- \"B\" get_pseudo_ipd_binary( binary_agd = testdat, format = \"unstacked\" ) #> USUBJID ARM RESPONSE #> 1 pseudo_binary_subj_1 B TRUE #> 2 pseudo_binary_subj_2 B TRUE #> 3 pseudo_binary_subj_3 B TRUE #> 4 pseudo_binary_subj_4 B TRUE #> 5 pseudo_binary_subj_5 B TRUE #> 6 pseudo_binary_subj_6 B TRUE #> 7 pseudo_binary_subj_7 B TRUE #> 8 pseudo_binary_subj_8 B TRUE #> 9 pseudo_binary_subj_9 B TRUE #> 10 pseudo_binary_subj_10 B TRUE #> 11 pseudo_binary_subj_11 B TRUE #> 12 pseudo_binary_subj_12 B TRUE #> 13 pseudo_binary_subj_13 B TRUE #> 14 pseudo_binary_subj_14 B TRUE #> 15 pseudo_binary_subj_15 B TRUE #> 16 pseudo_binary_subj_16 B TRUE #> 17 pseudo_binary_subj_17 B TRUE #> 18 pseudo_binary_subj_18 B TRUE #> 19 pseudo_binary_subj_19 B TRUE #> 20 pseudo_binary_subj_20 B TRUE #> 21 pseudo_binary_subj_21 B TRUE #> 22 pseudo_binary_subj_22 B TRUE #> 23 pseudo_binary_subj_23 B TRUE #> 24 pseudo_binary_subj_24 B TRUE #> 25 pseudo_binary_subj_25 B TRUE #> 26 pseudo_binary_subj_26 B TRUE #> 27 pseudo_binary_subj_27 B TRUE #> 28 pseudo_binary_subj_28 B TRUE #> 29 pseudo_binary_subj_29 B TRUE #> 30 pseudo_binary_subj_30 B TRUE #> 31 pseudo_binary_subj_31 B TRUE #> 32 pseudo_binary_subj_32 B TRUE #> 33 pseudo_binary_subj_33 B TRUE #> 34 pseudo_binary_subj_34 B TRUE #> 35 pseudo_binary_subj_35 B TRUE #> 36 pseudo_binary_subj_36 B TRUE #> 37 pseudo_binary_subj_37 B TRUE #> 38 pseudo_binary_subj_38 B TRUE #> 39 pseudo_binary_subj_39 B TRUE #> 40 pseudo_binary_subj_40 B TRUE #> 41 pseudo_binary_subj_41 B TRUE #> 42 pseudo_binary_subj_42 B TRUE #> 43 pseudo_binary_subj_43 B TRUE #> 44 pseudo_binary_subj_44 B TRUE #> 45 pseudo_binary_subj_45 B TRUE #> 46 pseudo_binary_subj_46 B TRUE #> 47 pseudo_binary_subj_47 B TRUE #> 48 pseudo_binary_subj_48 B TRUE #> 49 pseudo_binary_subj_49 B TRUE #> 50 pseudo_binary_subj_50 B TRUE #> 51 pseudo_binary_subj_51 B TRUE #> 52 pseudo_binary_subj_52 B TRUE #> 53 pseudo_binary_subj_53 B TRUE #> 54 pseudo_binary_subj_54 B TRUE #> 55 pseudo_binary_subj_55 B TRUE #> 56 pseudo_binary_subj_56 B TRUE #> 57 pseudo_binary_subj_57 B TRUE #> 58 pseudo_binary_subj_58 B TRUE #> 59 pseudo_binary_subj_59 B TRUE #> 60 pseudo_binary_subj_60 B TRUE #> 61 pseudo_binary_subj_61 B TRUE #> 62 pseudo_binary_subj_62 B TRUE #> 63 pseudo_binary_subj_63 B TRUE #> 64 pseudo_binary_subj_64 B TRUE #> 65 pseudo_binary_subj_65 B TRUE #> 66 pseudo_binary_subj_66 B TRUE #> 67 pseudo_binary_subj_67 B TRUE #> 68 pseudo_binary_subj_68 B TRUE #> 69 pseudo_binary_subj_69 B TRUE #> 70 pseudo_binary_subj_70 B TRUE #> 71 pseudo_binary_subj_71 B TRUE #> 72 pseudo_binary_subj_72 B TRUE #> 73 pseudo_binary_subj_73 B TRUE #> 74 pseudo_binary_subj_74 B TRUE #> 75 pseudo_binary_subj_75 B TRUE #> 76 pseudo_binary_subj_76 B TRUE #> 77 pseudo_binary_subj_77 B TRUE #> 78 pseudo_binary_subj_78 B TRUE #> 79 pseudo_binary_subj_79 B TRUE #> 80 pseudo_binary_subj_80 B TRUE #> 81 pseudo_binary_subj_81 B TRUE #> 82 pseudo_binary_subj_82 B TRUE #> 83 pseudo_binary_subj_83 B TRUE #> 84 pseudo_binary_subj_84 B TRUE #> 85 pseudo_binary_subj_85 B TRUE #> 86 pseudo_binary_subj_86 B TRUE #> 87 pseudo_binary_subj_87 B TRUE #> 88 pseudo_binary_subj_88 B TRUE #> 89 pseudo_binary_subj_89 B TRUE #> 90 pseudo_binary_subj_90 B TRUE #> 91 pseudo_binary_subj_91 B TRUE #> 92 pseudo_binary_subj_92 B TRUE #> 93 pseudo_binary_subj_93 B TRUE #> 94 pseudo_binary_subj_94 B TRUE #> 95 pseudo_binary_subj_95 B TRUE #> 96 pseudo_binary_subj_96 B TRUE #> 97 pseudo_binary_subj_97 B TRUE #> 98 pseudo_binary_subj_98 B TRUE #> 99 pseudo_binary_subj_99 B TRUE #> 100 pseudo_binary_subj_100 B TRUE #> 101 pseudo_binary_subj_101 B TRUE #> 102 pseudo_binary_subj_102 B TRUE #> 103 pseudo_binary_subj_103 B TRUE #> 104 pseudo_binary_subj_104 B TRUE #> 105 pseudo_binary_subj_105 B TRUE #> 106 pseudo_binary_subj_106 B TRUE #> 107 pseudo_binary_subj_107 B TRUE #> 108 pseudo_binary_subj_108 B TRUE #> 109 pseudo_binary_subj_109 B TRUE #> 110 pseudo_binary_subj_110 B TRUE #> 111 pseudo_binary_subj_111 B TRUE #> 112 pseudo_binary_subj_112 B TRUE #> 113 pseudo_binary_subj_113 B TRUE #> 114 pseudo_binary_subj_114 B TRUE #> 115 pseudo_binary_subj_115 B TRUE #> 116 pseudo_binary_subj_116 B TRUE #> 117 pseudo_binary_subj_117 B TRUE #> 118 pseudo_binary_subj_118 B TRUE #> 119 pseudo_binary_subj_119 B TRUE #> 120 pseudo_binary_subj_120 B TRUE #> 121 pseudo_binary_subj_121 B TRUE #> 122 pseudo_binary_subj_122 B TRUE #> 123 pseudo_binary_subj_123 B TRUE #> 124 pseudo_binary_subj_124 B TRUE #> 125 pseudo_binary_subj_125 B TRUE #> 126 pseudo_binary_subj_126 B TRUE #> 127 pseudo_binary_subj_127 B TRUE #> 128 pseudo_binary_subj_128 B TRUE #> 129 pseudo_binary_subj_129 B TRUE #> 130 pseudo_binary_subj_130 B TRUE #> 131 pseudo_binary_subj_131 B TRUE #> 132 pseudo_binary_subj_132 B TRUE #> 133 pseudo_binary_subj_133 B TRUE #> 134 pseudo_binary_subj_134 B TRUE #> 135 pseudo_binary_subj_135 B TRUE #> 136 pseudo_binary_subj_136 B TRUE #> 137 pseudo_binary_subj_137 B TRUE #> 138 pseudo_binary_subj_138 B TRUE #> 139 pseudo_binary_subj_139 B TRUE #> 140 pseudo_binary_subj_140 B TRUE #> 141 pseudo_binary_subj_141 B TRUE #> 142 pseudo_binary_subj_142 B TRUE #> 143 pseudo_binary_subj_143 B TRUE #> 144 pseudo_binary_subj_144 B TRUE #> 145 pseudo_binary_subj_145 B TRUE #> 146 pseudo_binary_subj_146 B TRUE #> 147 pseudo_binary_subj_147 B TRUE #> 148 pseudo_binary_subj_148 B TRUE #> 149 pseudo_binary_subj_149 B TRUE #> 150 pseudo_binary_subj_150 B TRUE #> 151 pseudo_binary_subj_151 B TRUE #> 152 pseudo_binary_subj_152 B TRUE #> 153 pseudo_binary_subj_153 B TRUE #> 154 pseudo_binary_subj_154 B TRUE #> 155 pseudo_binary_subj_155 B TRUE #> 156 pseudo_binary_subj_156 B TRUE #> 157 pseudo_binary_subj_157 B TRUE #> 158 pseudo_binary_subj_158 B TRUE #> 159 pseudo_binary_subj_159 B TRUE #> 160 pseudo_binary_subj_160 B TRUE #> 161 pseudo_binary_subj_161 B TRUE #> 162 pseudo_binary_subj_162 B TRUE #> 163 pseudo_binary_subj_163 B TRUE #> 164 pseudo_binary_subj_164 B TRUE #> 165 pseudo_binary_subj_165 B TRUE #> 166 pseudo_binary_subj_166 B TRUE #> 167 pseudo_binary_subj_167 B TRUE #> 168 pseudo_binary_subj_168 B TRUE #> 169 pseudo_binary_subj_169 B TRUE #> 170 pseudo_binary_subj_170 B TRUE #> 171 pseudo_binary_subj_171 B TRUE #> 172 pseudo_binary_subj_172 B TRUE #> 173 pseudo_binary_subj_173 B TRUE #> 174 pseudo_binary_subj_174 B TRUE #> 175 pseudo_binary_subj_175 B TRUE #> 176 pseudo_binary_subj_176 B TRUE #> 177 pseudo_binary_subj_177 B TRUE #> 178 pseudo_binary_subj_178 B TRUE #> 179 pseudo_binary_subj_179 B TRUE #> 180 pseudo_binary_subj_180 B TRUE #> 181 pseudo_binary_subj_181 B TRUE #> 182 pseudo_binary_subj_182 B TRUE #> 183 pseudo_binary_subj_183 B TRUE #> 184 pseudo_binary_subj_184 B TRUE #> 185 pseudo_binary_subj_185 B TRUE #> 186 pseudo_binary_subj_186 B TRUE #> 187 pseudo_binary_subj_187 B TRUE #> 188 pseudo_binary_subj_188 B TRUE #> 189 pseudo_binary_subj_189 B TRUE #> 190 pseudo_binary_subj_190 B TRUE #> 191 pseudo_binary_subj_191 B TRUE #> 192 pseudo_binary_subj_192 B TRUE #> 193 pseudo_binary_subj_193 B TRUE #> 194 pseudo_binary_subj_194 B TRUE #> 195 pseudo_binary_subj_195 B TRUE #> 196 pseudo_binary_subj_196 B TRUE #> 197 pseudo_binary_subj_197 B TRUE #> 198 pseudo_binary_subj_198 B TRUE #> 199 pseudo_binary_subj_199 B TRUE #> 200 pseudo_binary_subj_200 B TRUE #> 201 pseudo_binary_subj_201 B TRUE #> 202 pseudo_binary_subj_202 B TRUE #> 203 pseudo_binary_subj_203 B TRUE #> 204 pseudo_binary_subj_204 B TRUE #> 205 pseudo_binary_subj_205 B TRUE #> 206 pseudo_binary_subj_206 B TRUE #> 207 pseudo_binary_subj_207 B TRUE #> 208 pseudo_binary_subj_208 B TRUE #> 209 pseudo_binary_subj_209 B TRUE #> 210 pseudo_binary_subj_210 B TRUE #> 211 pseudo_binary_subj_211 B TRUE #> 212 pseudo_binary_subj_212 B TRUE #> 213 pseudo_binary_subj_213 B TRUE #> 214 pseudo_binary_subj_214 B TRUE #> 215 pseudo_binary_subj_215 B TRUE #> 216 pseudo_binary_subj_216 B TRUE #> 217 pseudo_binary_subj_217 B TRUE #> 218 pseudo_binary_subj_218 B TRUE #> 219 pseudo_binary_subj_219 B TRUE #> 220 pseudo_binary_subj_220 B TRUE #> 221 pseudo_binary_subj_221 B TRUE #> 222 pseudo_binary_subj_222 B TRUE #> 223 pseudo_binary_subj_223 B TRUE #> 224 pseudo_binary_subj_224 B TRUE #> 225 pseudo_binary_subj_225 B TRUE #> 226 pseudo_binary_subj_226 B TRUE #> 227 pseudo_binary_subj_227 B TRUE #> 228 pseudo_binary_subj_228 B TRUE #> 229 pseudo_binary_subj_229 B TRUE #> 230 pseudo_binary_subj_230 B TRUE #> 231 pseudo_binary_subj_231 B TRUE #> 232 pseudo_binary_subj_232 B TRUE #> 233 pseudo_binary_subj_233 B TRUE #> 234 pseudo_binary_subj_234 B TRUE #> 235 pseudo_binary_subj_235 B TRUE #> 236 pseudo_binary_subj_236 B TRUE #> 237 pseudo_binary_subj_237 B TRUE #> 238 pseudo_binary_subj_238 B TRUE #> 239 pseudo_binary_subj_239 B TRUE #> 240 pseudo_binary_subj_240 B TRUE #> 241 pseudo_binary_subj_241 B TRUE #> 242 pseudo_binary_subj_242 B TRUE #> 243 pseudo_binary_subj_243 B TRUE #> 244 pseudo_binary_subj_244 B TRUE #> 245 pseudo_binary_subj_245 B TRUE #> 246 pseudo_binary_subj_246 B TRUE #> 247 pseudo_binary_subj_247 B TRUE #> 248 pseudo_binary_subj_248 B TRUE #> 249 pseudo_binary_subj_249 B TRUE #> 250 pseudo_binary_subj_250 B TRUE #> 251 pseudo_binary_subj_251 B TRUE #> 252 pseudo_binary_subj_252 B TRUE #> 253 pseudo_binary_subj_253 B TRUE #> 254 pseudo_binary_subj_254 B TRUE #> 255 pseudo_binary_subj_255 B TRUE #> 256 pseudo_binary_subj_256 B TRUE #> 257 pseudo_binary_subj_257 B TRUE #> 258 pseudo_binary_subj_258 B TRUE #> 259 pseudo_binary_subj_259 B TRUE #> 260 pseudo_binary_subj_260 B TRUE #> 261 pseudo_binary_subj_261 B TRUE #> 262 pseudo_binary_subj_262 B TRUE #> 263 pseudo_binary_subj_263 B TRUE #> 264 pseudo_binary_subj_264 B TRUE #> 265 pseudo_binary_subj_265 B TRUE #> 266 pseudo_binary_subj_266 B TRUE #> 267 pseudo_binary_subj_267 B TRUE #> 268 pseudo_binary_subj_268 B TRUE #> 269 pseudo_binary_subj_269 B TRUE #> 270 pseudo_binary_subj_270 B TRUE #> 271 pseudo_binary_subj_271 B TRUE #> 272 pseudo_binary_subj_272 B TRUE #> 273 pseudo_binary_subj_273 B TRUE #> 274 pseudo_binary_subj_274 B TRUE #> 275 pseudo_binary_subj_275 B TRUE #> 276 pseudo_binary_subj_276 B TRUE #> 277 pseudo_binary_subj_277 B TRUE #> 278 pseudo_binary_subj_278 B TRUE #> 279 pseudo_binary_subj_279 B TRUE #> 280 pseudo_binary_subj_280 B TRUE #> 281 pseudo_binary_subj_281 B FALSE #> 282 pseudo_binary_subj_282 B FALSE #> 283 pseudo_binary_subj_283 B FALSE #> 284 pseudo_binary_subj_284 B FALSE #> 285 pseudo_binary_subj_285 B FALSE #> 286 pseudo_binary_subj_286 B FALSE #> 287 pseudo_binary_subj_287 B FALSE #> 288 pseudo_binary_subj_288 B FALSE #> 289 pseudo_binary_subj_289 B FALSE #> 290 pseudo_binary_subj_290 B FALSE #> 291 pseudo_binary_subj_291 B FALSE #> 292 pseudo_binary_subj_292 B FALSE #> 293 pseudo_binary_subj_293 B FALSE #> 294 pseudo_binary_subj_294 B FALSE #> 295 pseudo_binary_subj_295 B FALSE #> 296 pseudo_binary_subj_296 B FALSE #> 297 pseudo_binary_subj_297 B FALSE #> 298 pseudo_binary_subj_298 B FALSE #> 299 pseudo_binary_subj_299 B FALSE #> 300 pseudo_binary_subj_300 B FALSE #> 301 pseudo_binary_subj_301 B FALSE #> 302 pseudo_binary_subj_302 B FALSE #> 303 pseudo_binary_subj_303 B FALSE #> 304 pseudo_binary_subj_304 B FALSE #> 305 pseudo_binary_subj_305 B FALSE #> 306 pseudo_binary_subj_306 B FALSE #> 307 pseudo_binary_subj_307 B FALSE #> 308 pseudo_binary_subj_308 B FALSE #> 309 pseudo_binary_subj_309 B FALSE #> 310 pseudo_binary_subj_310 B FALSE #> 311 pseudo_binary_subj_311 B FALSE #> 312 pseudo_binary_subj_312 B FALSE #> 313 pseudo_binary_subj_313 B FALSE #> 314 pseudo_binary_subj_314 B FALSE #> 315 pseudo_binary_subj_315 B FALSE #> 316 pseudo_binary_subj_316 B FALSE #> 317 pseudo_binary_subj_317 B FALSE #> 318 pseudo_binary_subj_318 B FALSE #> 319 pseudo_binary_subj_319 B FALSE #> 320 pseudo_binary_subj_320 B FALSE #> 321 pseudo_binary_subj_321 B FALSE #> 322 pseudo_binary_subj_322 B FALSE #> 323 pseudo_binary_subj_323 B FALSE #> 324 pseudo_binary_subj_324 B FALSE #> 325 pseudo_binary_subj_325 B FALSE #> 326 pseudo_binary_subj_326 B FALSE #> 327 pseudo_binary_subj_327 B FALSE #> 328 pseudo_binary_subj_328 B FALSE #> 329 pseudo_binary_subj_329 B FALSE #> 330 pseudo_binary_subj_330 B FALSE #> 331 pseudo_binary_subj_331 B FALSE #> 332 pseudo_binary_subj_332 B FALSE #> 333 pseudo_binary_subj_333 B FALSE #> 334 pseudo_binary_subj_334 B FALSE #> 335 pseudo_binary_subj_335 B FALSE #> 336 pseudo_binary_subj_336 B FALSE #> 337 pseudo_binary_subj_337 B FALSE #> 338 pseudo_binary_subj_338 B FALSE #> 339 pseudo_binary_subj_339 B FALSE #> 340 pseudo_binary_subj_340 B FALSE #> 341 pseudo_binary_subj_341 B FALSE #> 342 pseudo_binary_subj_342 B FALSE #> 343 pseudo_binary_subj_343 B FALSE #> 344 pseudo_binary_subj_344 B FALSE #> 345 pseudo_binary_subj_345 B FALSE #> 346 pseudo_binary_subj_346 B FALSE #> 347 pseudo_binary_subj_347 B FALSE #> 348 pseudo_binary_subj_348 B FALSE #> 349 pseudo_binary_subj_349 B FALSE #> 350 pseudo_binary_subj_350 B FALSE #> 351 pseudo_binary_subj_351 B FALSE #> 352 pseudo_binary_subj_352 B FALSE #> 353 pseudo_binary_subj_353 B FALSE #> 354 pseudo_binary_subj_354 B FALSE #> 355 pseudo_binary_subj_355 B FALSE #> 356 pseudo_binary_subj_356 B FALSE #> 357 pseudo_binary_subj_357 B FALSE #> 358 pseudo_binary_subj_358 B FALSE #> 359 pseudo_binary_subj_359 B FALSE #> 360 pseudo_binary_subj_360 B FALSE #> 361 pseudo_binary_subj_361 B FALSE #> 362 pseudo_binary_subj_362 B FALSE #> 363 pseudo_binary_subj_363 B FALSE #> 364 pseudo_binary_subj_364 B FALSE #> 365 pseudo_binary_subj_365 B FALSE #> 366 pseudo_binary_subj_366 B FALSE #> 367 pseudo_binary_subj_367 B FALSE #> 368 pseudo_binary_subj_368 B FALSE #> 369 pseudo_binary_subj_369 B FALSE #> 370 pseudo_binary_subj_370 B FALSE #> 371 pseudo_binary_subj_371 B FALSE #> 372 pseudo_binary_subj_372 B FALSE #> 373 pseudo_binary_subj_373 B FALSE #> 374 pseudo_binary_subj_374 B FALSE #> 375 pseudo_binary_subj_375 B FALSE #> 376 pseudo_binary_subj_376 B FALSE #> 377 pseudo_binary_subj_377 B FALSE #> 378 pseudo_binary_subj_378 B FALSE #> 379 pseudo_binary_subj_379 B FALSE #> 380 pseudo_binary_subj_380 B FALSE #> 381 pseudo_binary_subj_381 B FALSE #> 382 pseudo_binary_subj_382 B FALSE #> 383 pseudo_binary_subj_383 B FALSE #> 384 pseudo_binary_subj_384 B FALSE #> 385 pseudo_binary_subj_385 B FALSE #> 386 pseudo_binary_subj_386 B FALSE #> 387 pseudo_binary_subj_387 B FALSE #> 388 pseudo_binary_subj_388 B FALSE #> 389 pseudo_binary_subj_389 B FALSE #> 390 pseudo_binary_subj_390 B FALSE #> 391 pseudo_binary_subj_391 B FALSE #> 392 pseudo_binary_subj_392 B FALSE #> 393 pseudo_binary_subj_393 B FALSE #> 394 pseudo_binary_subj_394 B FALSE #> 395 pseudo_binary_subj_395 B FALSE #> 396 pseudo_binary_subj_396 B FALSE #> 397 pseudo_binary_subj_397 B FALSE #> 398 pseudo_binary_subj_398 B FALSE #> 399 pseudo_binary_subj_399 B FALSE #> 400 pseudo_binary_subj_400 B FALSE # example of stacked get_pseudo_ipd_binary( binary_agd = data.frame( ARM = rep(\"B\", 2), RESPONSE = c(\"YES\", \"NO\"), COUNT = c(280, 120) ), format = \"stacked\" ) #> USUBJID ARM RESPONSE #> 1 pseudo_binary_subj_1 B TRUE #> 2 pseudo_binary_subj_2 B TRUE #> 3 pseudo_binary_subj_3 B TRUE #> 4 pseudo_binary_subj_4 B TRUE #> 5 pseudo_binary_subj_5 B TRUE #> 6 pseudo_binary_subj_6 B TRUE #> 7 pseudo_binary_subj_7 B TRUE #> 8 pseudo_binary_subj_8 B TRUE #> 9 pseudo_binary_subj_9 B TRUE #> 10 pseudo_binary_subj_10 B TRUE #> 11 pseudo_binary_subj_11 B TRUE #> 12 pseudo_binary_subj_12 B TRUE #> 13 pseudo_binary_subj_13 B TRUE #> 14 pseudo_binary_subj_14 B TRUE #> 15 pseudo_binary_subj_15 B TRUE #> 16 pseudo_binary_subj_16 B TRUE #> 17 pseudo_binary_subj_17 B TRUE #> 18 pseudo_binary_subj_18 B TRUE #> 19 pseudo_binary_subj_19 B TRUE #> 20 pseudo_binary_subj_20 B TRUE #> 21 pseudo_binary_subj_21 B TRUE #> 22 pseudo_binary_subj_22 B TRUE #> 23 pseudo_binary_subj_23 B TRUE #> 24 pseudo_binary_subj_24 B TRUE #> 25 pseudo_binary_subj_25 B TRUE #> 26 pseudo_binary_subj_26 B TRUE #> 27 pseudo_binary_subj_27 B TRUE #> 28 pseudo_binary_subj_28 B TRUE #> 29 pseudo_binary_subj_29 B TRUE #> 30 pseudo_binary_subj_30 B TRUE #> 31 pseudo_binary_subj_31 B TRUE #> 32 pseudo_binary_subj_32 B TRUE #> 33 pseudo_binary_subj_33 B TRUE #> 34 pseudo_binary_subj_34 B TRUE #> 35 pseudo_binary_subj_35 B TRUE #> 36 pseudo_binary_subj_36 B TRUE #> 37 pseudo_binary_subj_37 B TRUE #> 38 pseudo_binary_subj_38 B TRUE #> 39 pseudo_binary_subj_39 B TRUE #> 40 pseudo_binary_subj_40 B TRUE #> 41 pseudo_binary_subj_41 B TRUE #> 42 pseudo_binary_subj_42 B TRUE #> 43 pseudo_binary_subj_43 B TRUE #> 44 pseudo_binary_subj_44 B TRUE #> 45 pseudo_binary_subj_45 B TRUE #> 46 pseudo_binary_subj_46 B TRUE #> 47 pseudo_binary_subj_47 B TRUE #> 48 pseudo_binary_subj_48 B TRUE #> 49 pseudo_binary_subj_49 B TRUE #> 50 pseudo_binary_subj_50 B TRUE #> 51 pseudo_binary_subj_51 B TRUE #> 52 pseudo_binary_subj_52 B TRUE #> 53 pseudo_binary_subj_53 B TRUE #> 54 pseudo_binary_subj_54 B TRUE #> 55 pseudo_binary_subj_55 B TRUE #> 56 pseudo_binary_subj_56 B TRUE #> 57 pseudo_binary_subj_57 B TRUE #> 58 pseudo_binary_subj_58 B TRUE #> 59 pseudo_binary_subj_59 B TRUE #> 60 pseudo_binary_subj_60 B TRUE #> 61 pseudo_binary_subj_61 B TRUE #> 62 pseudo_binary_subj_62 B TRUE #> 63 pseudo_binary_subj_63 B TRUE #> 64 pseudo_binary_subj_64 B TRUE #> 65 pseudo_binary_subj_65 B TRUE #> 66 pseudo_binary_subj_66 B TRUE #> 67 pseudo_binary_subj_67 B TRUE #> 68 pseudo_binary_subj_68 B TRUE #> 69 pseudo_binary_subj_69 B TRUE #> 70 pseudo_binary_subj_70 B TRUE #> 71 pseudo_binary_subj_71 B TRUE #> 72 pseudo_binary_subj_72 B TRUE #> 73 pseudo_binary_subj_73 B TRUE #> 74 pseudo_binary_subj_74 B TRUE #> 75 pseudo_binary_subj_75 B TRUE #> 76 pseudo_binary_subj_76 B TRUE #> 77 pseudo_binary_subj_77 B TRUE #> 78 pseudo_binary_subj_78 B TRUE #> 79 pseudo_binary_subj_79 B TRUE #> 80 pseudo_binary_subj_80 B TRUE #> 81 pseudo_binary_subj_81 B TRUE #> 82 pseudo_binary_subj_82 B TRUE #> 83 pseudo_binary_subj_83 B TRUE #> 84 pseudo_binary_subj_84 B TRUE #> 85 pseudo_binary_subj_85 B TRUE #> 86 pseudo_binary_subj_86 B TRUE #> 87 pseudo_binary_subj_87 B TRUE #> 88 pseudo_binary_subj_88 B TRUE #> 89 pseudo_binary_subj_89 B TRUE #> 90 pseudo_binary_subj_90 B TRUE #> 91 pseudo_binary_subj_91 B TRUE #> 92 pseudo_binary_subj_92 B TRUE #> 93 pseudo_binary_subj_93 B TRUE #> 94 pseudo_binary_subj_94 B TRUE #> 95 pseudo_binary_subj_95 B TRUE #> 96 pseudo_binary_subj_96 B TRUE #> 97 pseudo_binary_subj_97 B TRUE #> 98 pseudo_binary_subj_98 B TRUE #> 99 pseudo_binary_subj_99 B TRUE #> 100 pseudo_binary_subj_100 B TRUE #> 101 pseudo_binary_subj_101 B TRUE #> 102 pseudo_binary_subj_102 B TRUE #> 103 pseudo_binary_subj_103 B TRUE #> 104 pseudo_binary_subj_104 B TRUE #> 105 pseudo_binary_subj_105 B TRUE #> 106 pseudo_binary_subj_106 B TRUE #> 107 pseudo_binary_subj_107 B TRUE #> 108 pseudo_binary_subj_108 B TRUE #> 109 pseudo_binary_subj_109 B TRUE #> 110 pseudo_binary_subj_110 B TRUE #> 111 pseudo_binary_subj_111 B TRUE #> 112 pseudo_binary_subj_112 B TRUE #> 113 pseudo_binary_subj_113 B TRUE #> 114 pseudo_binary_subj_114 B TRUE #> 115 pseudo_binary_subj_115 B TRUE #> 116 pseudo_binary_subj_116 B TRUE #> 117 pseudo_binary_subj_117 B TRUE #> 118 pseudo_binary_subj_118 B TRUE #> 119 pseudo_binary_subj_119 B TRUE #> 120 pseudo_binary_subj_120 B TRUE #> 121 pseudo_binary_subj_121 B TRUE #> 122 pseudo_binary_subj_122 B TRUE #> 123 pseudo_binary_subj_123 B TRUE #> 124 pseudo_binary_subj_124 B TRUE #> 125 pseudo_binary_subj_125 B TRUE #> 126 pseudo_binary_subj_126 B TRUE #> 127 pseudo_binary_subj_127 B TRUE #> 128 pseudo_binary_subj_128 B TRUE #> 129 pseudo_binary_subj_129 B TRUE #> 130 pseudo_binary_subj_130 B TRUE #> 131 pseudo_binary_subj_131 B TRUE #> 132 pseudo_binary_subj_132 B TRUE #> 133 pseudo_binary_subj_133 B TRUE #> 134 pseudo_binary_subj_134 B TRUE #> 135 pseudo_binary_subj_135 B TRUE #> 136 pseudo_binary_subj_136 B TRUE #> 137 pseudo_binary_subj_137 B TRUE #> 138 pseudo_binary_subj_138 B TRUE #> 139 pseudo_binary_subj_139 B TRUE #> 140 pseudo_binary_subj_140 B TRUE #> 141 pseudo_binary_subj_141 B TRUE #> 142 pseudo_binary_subj_142 B TRUE #> 143 pseudo_binary_subj_143 B TRUE #> 144 pseudo_binary_subj_144 B TRUE #> 145 pseudo_binary_subj_145 B TRUE #> 146 pseudo_binary_subj_146 B TRUE #> 147 pseudo_binary_subj_147 B TRUE #> 148 pseudo_binary_subj_148 B TRUE #> 149 pseudo_binary_subj_149 B TRUE #> 150 pseudo_binary_subj_150 B TRUE #> 151 pseudo_binary_subj_151 B TRUE #> 152 pseudo_binary_subj_152 B TRUE #> 153 pseudo_binary_subj_153 B TRUE #> 154 pseudo_binary_subj_154 B TRUE #> 155 pseudo_binary_subj_155 B TRUE #> 156 pseudo_binary_subj_156 B TRUE #> 157 pseudo_binary_subj_157 B TRUE #> 158 pseudo_binary_subj_158 B TRUE #> 159 pseudo_binary_subj_159 B TRUE #> 160 pseudo_binary_subj_160 B TRUE #> 161 pseudo_binary_subj_161 B TRUE #> 162 pseudo_binary_subj_162 B TRUE #> 163 pseudo_binary_subj_163 B TRUE #> 164 pseudo_binary_subj_164 B TRUE #> 165 pseudo_binary_subj_165 B TRUE #> 166 pseudo_binary_subj_166 B TRUE #> 167 pseudo_binary_subj_167 B TRUE #> 168 pseudo_binary_subj_168 B TRUE #> 169 pseudo_binary_subj_169 B TRUE #> 170 pseudo_binary_subj_170 B TRUE #> 171 pseudo_binary_subj_171 B TRUE #> 172 pseudo_binary_subj_172 B TRUE #> 173 pseudo_binary_subj_173 B TRUE #> 174 pseudo_binary_subj_174 B TRUE #> 175 pseudo_binary_subj_175 B TRUE #> 176 pseudo_binary_subj_176 B TRUE #> 177 pseudo_binary_subj_177 B TRUE #> 178 pseudo_binary_subj_178 B TRUE #> 179 pseudo_binary_subj_179 B TRUE #> 180 pseudo_binary_subj_180 B TRUE #> 181 pseudo_binary_subj_181 B TRUE #> 182 pseudo_binary_subj_182 B TRUE #> 183 pseudo_binary_subj_183 B TRUE #> 184 pseudo_binary_subj_184 B TRUE #> 185 pseudo_binary_subj_185 B TRUE #> 186 pseudo_binary_subj_186 B TRUE #> 187 pseudo_binary_subj_187 B TRUE #> 188 pseudo_binary_subj_188 B TRUE #> 189 pseudo_binary_subj_189 B TRUE #> 190 pseudo_binary_subj_190 B TRUE #> 191 pseudo_binary_subj_191 B TRUE #> 192 pseudo_binary_subj_192 B TRUE #> 193 pseudo_binary_subj_193 B TRUE #> 194 pseudo_binary_subj_194 B TRUE #> 195 pseudo_binary_subj_195 B TRUE #> 196 pseudo_binary_subj_196 B TRUE #> 197 pseudo_binary_subj_197 B TRUE #> 198 pseudo_binary_subj_198 B TRUE #> 199 pseudo_binary_subj_199 B TRUE #> 200 pseudo_binary_subj_200 B TRUE #> 201 pseudo_binary_subj_201 B TRUE #> 202 pseudo_binary_subj_202 B TRUE #> 203 pseudo_binary_subj_203 B TRUE #> 204 pseudo_binary_subj_204 B TRUE #> 205 pseudo_binary_subj_205 B TRUE #> 206 pseudo_binary_subj_206 B TRUE #> 207 pseudo_binary_subj_207 B TRUE #> 208 pseudo_binary_subj_208 B TRUE #> 209 pseudo_binary_subj_209 B TRUE #> 210 pseudo_binary_subj_210 B TRUE #> 211 pseudo_binary_subj_211 B TRUE #> 212 pseudo_binary_subj_212 B TRUE #> 213 pseudo_binary_subj_213 B TRUE #> 214 pseudo_binary_subj_214 B TRUE #> 215 pseudo_binary_subj_215 B TRUE #> 216 pseudo_binary_subj_216 B TRUE #> 217 pseudo_binary_subj_217 B TRUE #> 218 pseudo_binary_subj_218 B TRUE #> 219 pseudo_binary_subj_219 B TRUE #> 220 pseudo_binary_subj_220 B TRUE #> 221 pseudo_binary_subj_221 B TRUE #> 222 pseudo_binary_subj_222 B TRUE #> 223 pseudo_binary_subj_223 B TRUE #> 224 pseudo_binary_subj_224 B TRUE #> 225 pseudo_binary_subj_225 B TRUE #> 226 pseudo_binary_subj_226 B TRUE #> 227 pseudo_binary_subj_227 B TRUE #> 228 pseudo_binary_subj_228 B TRUE #> 229 pseudo_binary_subj_229 B TRUE #> 230 pseudo_binary_subj_230 B TRUE #> 231 pseudo_binary_subj_231 B TRUE #> 232 pseudo_binary_subj_232 B TRUE #> 233 pseudo_binary_subj_233 B TRUE #> 234 pseudo_binary_subj_234 B TRUE #> 235 pseudo_binary_subj_235 B TRUE #> 236 pseudo_binary_subj_236 B TRUE #> 237 pseudo_binary_subj_237 B TRUE #> 238 pseudo_binary_subj_238 B TRUE #> 239 pseudo_binary_subj_239 B TRUE #> 240 pseudo_binary_subj_240 B TRUE #> 241 pseudo_binary_subj_241 B TRUE #> 242 pseudo_binary_subj_242 B TRUE #> 243 pseudo_binary_subj_243 B TRUE #> 244 pseudo_binary_subj_244 B TRUE #> 245 pseudo_binary_subj_245 B TRUE #> 246 pseudo_binary_subj_246 B TRUE #> 247 pseudo_binary_subj_247 B TRUE #> 248 pseudo_binary_subj_248 B TRUE #> 249 pseudo_binary_subj_249 B TRUE #> 250 pseudo_binary_subj_250 B TRUE #> 251 pseudo_binary_subj_251 B TRUE #> 252 pseudo_binary_subj_252 B TRUE #> 253 pseudo_binary_subj_253 B TRUE #> 254 pseudo_binary_subj_254 B TRUE #> 255 pseudo_binary_subj_255 B TRUE #> 256 pseudo_binary_subj_256 B TRUE #> 257 pseudo_binary_subj_257 B TRUE #> 258 pseudo_binary_subj_258 B TRUE #> 259 pseudo_binary_subj_259 B TRUE #> 260 pseudo_binary_subj_260 B TRUE #> 261 pseudo_binary_subj_261 B TRUE #> 262 pseudo_binary_subj_262 B TRUE #> 263 pseudo_binary_subj_263 B TRUE #> 264 pseudo_binary_subj_264 B TRUE #> 265 pseudo_binary_subj_265 B TRUE #> 266 pseudo_binary_subj_266 B TRUE #> 267 pseudo_binary_subj_267 B TRUE #> 268 pseudo_binary_subj_268 B TRUE #> 269 pseudo_binary_subj_269 B TRUE #> 270 pseudo_binary_subj_270 B TRUE #> 271 pseudo_binary_subj_271 B TRUE #> 272 pseudo_binary_subj_272 B TRUE #> 273 pseudo_binary_subj_273 B TRUE #> 274 pseudo_binary_subj_274 B TRUE #> 275 pseudo_binary_subj_275 B TRUE #> 276 pseudo_binary_subj_276 B TRUE #> 277 pseudo_binary_subj_277 B TRUE #> 278 pseudo_binary_subj_278 B TRUE #> 279 pseudo_binary_subj_279 B TRUE #> 280 pseudo_binary_subj_280 B TRUE #> 281 pseudo_binary_subj_281 B FALSE #> 282 pseudo_binary_subj_282 B FALSE #> 283 pseudo_binary_subj_283 B FALSE #> 284 pseudo_binary_subj_284 B FALSE #> 285 pseudo_binary_subj_285 B FALSE #> 286 pseudo_binary_subj_286 B FALSE #> 287 pseudo_binary_subj_287 B FALSE #> 288 pseudo_binary_subj_288 B FALSE #> 289 pseudo_binary_subj_289 B FALSE #> 290 pseudo_binary_subj_290 B FALSE #> 291 pseudo_binary_subj_291 B FALSE #> 292 pseudo_binary_subj_292 B FALSE #> 293 pseudo_binary_subj_293 B FALSE #> 294 pseudo_binary_subj_294 B FALSE #> 295 pseudo_binary_subj_295 B FALSE #> 296 pseudo_binary_subj_296 B FALSE #> 297 pseudo_binary_subj_297 B FALSE #> 298 pseudo_binary_subj_298 B FALSE #> 299 pseudo_binary_subj_299 B FALSE #> 300 pseudo_binary_subj_300 B FALSE #> 301 pseudo_binary_subj_301 B FALSE #> 302 pseudo_binary_subj_302 B FALSE #> 303 pseudo_binary_subj_303 B FALSE #> 304 pseudo_binary_subj_304 B FALSE #> 305 pseudo_binary_subj_305 B FALSE #> 306 pseudo_binary_subj_306 B FALSE #> 307 pseudo_binary_subj_307 B FALSE #> 308 pseudo_binary_subj_308 B FALSE #> 309 pseudo_binary_subj_309 B FALSE #> 310 pseudo_binary_subj_310 B FALSE #> 311 pseudo_binary_subj_311 B FALSE #> 312 pseudo_binary_subj_312 B FALSE #> 313 pseudo_binary_subj_313 B FALSE #> 314 pseudo_binary_subj_314 B FALSE #> 315 pseudo_binary_subj_315 B FALSE #> 316 pseudo_binary_subj_316 B FALSE #> 317 pseudo_binary_subj_317 B FALSE #> 318 pseudo_binary_subj_318 B FALSE #> 319 pseudo_binary_subj_319 B FALSE #> 320 pseudo_binary_subj_320 B FALSE #> 321 pseudo_binary_subj_321 B FALSE #> 322 pseudo_binary_subj_322 B FALSE #> 323 pseudo_binary_subj_323 B FALSE #> 324 pseudo_binary_subj_324 B FALSE #> 325 pseudo_binary_subj_325 B FALSE #> 326 pseudo_binary_subj_326 B FALSE #> 327 pseudo_binary_subj_327 B FALSE #> 328 pseudo_binary_subj_328 B FALSE #> 329 pseudo_binary_subj_329 B FALSE #> 330 pseudo_binary_subj_330 B FALSE #> 331 pseudo_binary_subj_331 B FALSE #> 332 pseudo_binary_subj_332 B FALSE #> 333 pseudo_binary_subj_333 B FALSE #> 334 pseudo_binary_subj_334 B FALSE #> 335 pseudo_binary_subj_335 B FALSE #> 336 pseudo_binary_subj_336 B FALSE #> 337 pseudo_binary_subj_337 B FALSE #> 338 pseudo_binary_subj_338 B FALSE #> 339 pseudo_binary_subj_339 B FALSE #> 340 pseudo_binary_subj_340 B FALSE #> 341 pseudo_binary_subj_341 B FALSE #> 342 pseudo_binary_subj_342 B FALSE #> 343 pseudo_binary_subj_343 B FALSE #> 344 pseudo_binary_subj_344 B FALSE #> 345 pseudo_binary_subj_345 B FALSE #> 346 pseudo_binary_subj_346 B FALSE #> 347 pseudo_binary_subj_347 B FALSE #> 348 pseudo_binary_subj_348 B FALSE #> 349 pseudo_binary_subj_349 B FALSE #> 350 pseudo_binary_subj_350 B FALSE #> 351 pseudo_binary_subj_351 B FALSE #> 352 pseudo_binary_subj_352 B FALSE #> 353 pseudo_binary_subj_353 B FALSE #> 354 pseudo_binary_subj_354 B FALSE #> 355 pseudo_binary_subj_355 B FALSE #> 356 pseudo_binary_subj_356 B FALSE #> 357 pseudo_binary_subj_357 B FALSE #> 358 pseudo_binary_subj_358 B FALSE #> 359 pseudo_binary_subj_359 B FALSE #> 360 pseudo_binary_subj_360 B FALSE #> 361 pseudo_binary_subj_361 B FALSE #> 362 pseudo_binary_subj_362 B FALSE #> 363 pseudo_binary_subj_363 B FALSE #> 364 pseudo_binary_subj_364 B FALSE #> 365 pseudo_binary_subj_365 B FALSE #> 366 pseudo_binary_subj_366 B FALSE #> 367 pseudo_binary_subj_367 B FALSE #> 368 pseudo_binary_subj_368 B FALSE #> 369 pseudo_binary_subj_369 B FALSE #> 370 pseudo_binary_subj_370 B FALSE #> 371 pseudo_binary_subj_371 B FALSE #> 372 pseudo_binary_subj_372 B FALSE #> 373 pseudo_binary_subj_373 B FALSE #> 374 pseudo_binary_subj_374 B FALSE #> 375 pseudo_binary_subj_375 B FALSE #> 376 pseudo_binary_subj_376 B FALSE #> 377 pseudo_binary_subj_377 B FALSE #> 378 pseudo_binary_subj_378 B FALSE #> 379 pseudo_binary_subj_379 B FALSE #> 380 pseudo_binary_subj_380 B FALSE #> 381 pseudo_binary_subj_381 B FALSE #> 382 pseudo_binary_subj_382 B FALSE #> 383 pseudo_binary_subj_383 B FALSE #> 384 pseudo_binary_subj_384 B FALSE #> 385 pseudo_binary_subj_385 B FALSE #> 386 pseudo_binary_subj_386 B FALSE #> 387 pseudo_binary_subj_387 B FALSE #> 388 pseudo_binary_subj_388 B FALSE #> 389 pseudo_binary_subj_389 B FALSE #> 390 pseudo_binary_subj_390 B FALSE #> 391 pseudo_binary_subj_391 B FALSE #> 392 pseudo_binary_subj_392 B FALSE #> 393 pseudo_binary_subj_393 B FALSE #> 394 pseudo_binary_subj_394 B FALSE #> 395 pseudo_binary_subj_395 B FALSE #> 396 pseudo_binary_subj_396 B FALSE #> 397 pseudo_binary_subj_397 B FALSE #> 398 pseudo_binary_subj_398 B FALSE #> 399 pseudo_binary_subj_399 B FALSE #> 400 pseudo_binary_subj_400 B FALSE"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/get_time_as.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert Time Values Using Scaling Factors — get_time_as","title":"Convert Time Values Using Scaling Factors — get_time_as","text":"Convert Time Values Using Scaling Factors","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/get_time_as.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert Time Values Using Scaling Factors — get_time_as","text":"","code":"get_time_as(times, as = NULL)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/get_time_as.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert Time Values Using Scaling Factors — get_time_as","text":"times Numeric time values time scale convert . One \"days\", \"weeks\", \"months\", \"years\"","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/get_time_as.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert Time Values Using Scaling Factors — get_time_as","text":"Returns numeric vector calculated times / get_time_conversion(factor = )","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/get_time_as.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert Time Values Using Scaling Factors — get_time_as","text":"","code":"get_time_as(50, as = \"months\") #> months #> 1.64271"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/kmplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","title":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","text":"wrapper function basic_kmplot. argument setting similar maic_anchored maic_unanchored, used two functions.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/kmplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","text":"","code":"kmplot( weights_object, tte_ipd, tte_pseudo_ipd, trt_ipd, trt_agd, trt_common = NULL, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", km_conf_type = \"log-log\", km_layout = c(\"all\", \"by_trial\", \"by_arm\"), ... )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/kmplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","text":"weights_object object returned estimate_weight tte_ipd data frame individual patient data (IPD) internal trial, contain least \"USUBJID\", \"EVENT\", \"TIME\" columns column indicating treatment assignment tte_pseudo_ipd data frame pseudo IPD digitized KM curves external trial (time--event endpoint), contain least \"EVENT\", \"TIME\" trt_ipd string, name interested investigation arm internal trial dat_igd (real IPD) trt_agd string, name interested investigation arm external trial dat_pseudo (pseudo IPD) trt_common string, name common comparator internal external trial, default NULL, indicating unanchored case trt_var_ipd string, column name tte_ipd contains treatment assignment trt_var_agd string, column name tte_pseudo_ipd contains treatment assignment km_conf_type string, pass conf.type survfit km_layout string, applicable unanchored case (trt_common = NULL), indicated desired layout output KM curve. ... arguments basic_kmplot","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/kmplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","text":"unanchored case, KM plot risk set table. anchored case, depending km_layout, \"by_trial\", 2 1 plot, first KM curves (incl. weighted) IPD trial, KM curves AgD trial, risk set table. \"by_arm\", 2 1 plot, first KM curves trt_agd trt_ipd (without weights), KM curves trt_common AgD trial IPD trial (without weights). Risk set table appended. \"\", 2 2 plot, plots \"by_trial\" \"by_arm\" without risk set table appended.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/kmplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","text":"","code":"# unanchored example using kmplot data(weighted_sat) data(adtte_sat) data(pseudo_ipd_sat) kmplot( weights_object = weighted_sat, tte_ipd = adtte_sat, tte_pseudo_ipd = pseudo_ipd_sat, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_name = \"Overall Survival\", trt_ipd = \"A\", trt_agd = \"B\", trt_common = NULL, km_conf_type = \"log-log\", time_scale = \"month\", time_grid = seq(0, 20, by = 2), use_colors = NULL, use_line_types = NULL, use_pch_cex = 0.65, use_pch_alpha = 100 ) # anchored example using kmplot data(weighted_twt) data(adtte_twt) data(pseudo_ipd_twt) # plot by trial kmplot( weights_object = weighted_twt, tte_ipd = adtte_twt, tte_pseudo_ipd = pseudo_ipd_twt, trt_ipd = \"A\", trt_agd = \"B\", trt_common = \"C\", trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_name = \"Overall Survival\", km_conf_type = \"log-log\", km_layout = \"by_trial\", time_scale = \"month\", time_grid = seq(0, 20, by = 2), use_colors = NULL, use_line_types = NULL, use_pch_cex = 0.65, use_pch_alpha = 100 ) # plot by arm kmplot( weights_object = weighted_twt, tte_ipd = adtte_twt, tte_pseudo_ipd = pseudo_ipd_twt, trt_ipd = \"A\", trt_agd = \"B\", trt_common = \"C\", trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_name = \"Overall Survival\", km_conf_type = \"log-log\", km_layout = \"by_arm\", time_scale = \"month\", time_grid = seq(0, 20, by = 2), use_colors = NULL, use_line_types = NULL, use_pch_cex = 0.65, use_pch_alpha = 100 ) # plot all kmplot( weights_object = weighted_twt, tte_ipd = adtte_twt, tte_pseudo_ipd = pseudo_ipd_twt, trt_ipd = \"A\", trt_agd = \"B\", trt_common = \"C\", trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_name = \"Overall Survival\", km_conf_type = \"log-log\", km_layout = \"all\", time_scale = \"month\", time_grid = seq(0, 20, by = 2), use_colors = NULL, use_line_types = NULL, use_pch_cex = 0.65, use_pch_alpha = 100 )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/kmplot2.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","title":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","text":"wrapper function basic_kmplot2. argument setting similar maic_anchored maic_unanchored, used two functions.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/kmplot2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","text":"","code":"kmplot2( weights_object, tte_ipd, tte_pseudo_ipd, trt_ipd, trt_agd, trt_common = NULL, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", km_conf_type = \"log-log\", km_layout = c(\"all\", \"by_trial\", \"by_arm\"), time_scale, ... )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/kmplot2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","text":"weights_object object returned estimate_weight tte_ipd data frame individual patient data (IPD) internal trial, contain least \"USUBJID\", \"EVENT\", \"TIME\" columns column indicating treatment assignment tte_pseudo_ipd data frame pseudo IPD digitized KM curves external trial (time--event endpoint), contain least \"EVENT\", \"TIME\" trt_ipd string, name interested investigation arm internal trial dat_igd (real IPD) trt_agd string, name interested investigation arm external trial dat_pseudo (pseudo IPD) trt_common string, name common comparator internal external trial, default NULL, indicating unanchored case trt_var_ipd string, column name tte_ipd contains treatment assignment trt_var_agd string, column name tte_pseudo_ipd contains treatment assignment km_conf_type string, pass conf.type survfit km_layout string, applicable unanchored case (trt_common = NULL), indicated desired layout output KM curve. time_scale string, time unit median survival time, taking value 'years', 'months', weeks' 'days' ... arguments basic_kmplot2","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/kmplot2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","text":"unanchored case, KM plot risk set table. anchored case, depending km_layout, \"by_trial\", 2 1 plot, first KM curves (incl. weighted) IPD trial, KM curves AgD trial, risk set table. \"by_arm\", 2 1 plot, first KM curves trt_agd trt_ipd (without weights), KM curves trt_common AgD trial IPD trial (without weights). Risk set table appended. \"\", 2 2 plot, plots \"by_trial\" \"by_arm\" without risk set table appended.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/kmplot2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","text":"","code":"# unanchored example using kmplot2 data(weighted_sat) data(adtte_sat) data(pseudo_ipd_sat) kmplot2( weights_object = weighted_sat, tte_ipd = adtte_sat, tte_pseudo_ipd = pseudo_ipd_sat, trt_ipd = \"A\", trt_agd = \"B\", trt_common = NULL, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_name = \"Overall Survival\", km_conf_type = \"log-log\", time_scale = \"month\", break_x_by = 2, xlim = c(0, 20) ) # anchored example using kmplot2 data(weighted_twt) data(adtte_twt) data(pseudo_ipd_twt) # plot by trial kmplot2( weights_object = weighted_twt, tte_ipd = adtte_twt, tte_pseudo_ipd = pseudo_ipd_twt, trt_ipd = \"A\", trt_agd = \"B\", trt_common = \"C\", trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_name = \"Overall Survival\", km_conf_type = \"log-log\", km_layout = \"by_trial\", time_scale = \"month\", break_x_by = 2 ) # plot by arm kmplot2( weights_object = weighted_twt, tte_ipd = adtte_twt, tte_pseudo_ipd = pseudo_ipd_twt, trt_ipd = \"A\", trt_agd = \"B\", trt_common = \"C\", trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_name = \"Overall Survival\", km_conf_type = \"log-log\", km_layout = \"by_arm\", time_scale = \"month\", break_x_by = 2 ) # plot all kmplot2( weights_object = weighted_twt, tte_ipd = adtte_twt, tte_pseudo_ipd = pseudo_ipd_twt, trt_ipd = \"A\", trt_agd = \"B\", trt_common = \"C\", trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_name = \"Overall Survival\", km_conf_type = \"log-log\", km_layout = \"all\", time_scale = \"month\", break_x_by = 2, xlim = c(0, 20), show_risk_set = FALSE )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maic_anchored.html","id":null,"dir":"Reference","previous_headings":"","what":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","title":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","text":"wrapper function provide adjusted effect estimates relevant statistics anchored case (.e. common comparator arm internal external trial).","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maic_anchored.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","text":"","code":"maic_anchored( weights_object, ipd, pseudo_ipd, trt_ipd, trt_agd, trt_common, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_type = \"tte\", endpoint_name = \"Time to Event Endpoint\", eff_measure = c(\"HR\", \"OR\", \"RR\", \"RD\"), boot_ci_type = c(\"norm\", \"basic\", \"stud\", \"perc\", \"bca\"), time_scale = \"months\", km_conf_type = \"log-log\", binary_robust_cov_type = \"HC3\" )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maic_anchored.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","text":"weights_object object returned estimate_weight ipd data frame meet format requirements 'Details', individual patient data (IPD) internal trial pseudo_ipd data frame, pseudo IPD digitized KM curve external trial (time--event endpoint) contingency table (binary endpoint) trt_ipd string, name interested investigation arm internal trial ipd (internal IPD) trt_agd string, name interested investigation arm external trial pseudo_ipd (pseudo IPD) trt_common string, name common comparator internal external trial trt_var_ipd string, column name ipd contains treatment assignment trt_var_agd string, column name ipd contains treatment assignment endpoint_type string, one following \"binary\", \"tte\" (time event) endpoint_name string, name time event endpoint, show last line title eff_measure string, \"RD\" (risk difference), \"\" (odds ratio), \"RR\" (relative risk) binary endpoint; \"HR\" time--event endpoint. default NULL, \"\" used binary case, otherwise \"HR\" used. boot_ci_type string, one c(\"norm\",\"basic\", \"stud\", \"perc\", \"bca\") select type bootstrap confidence interval. See boot::boot.ci details. time_scale string, time unit median survival time, taking value 'years', 'months', 'weeks' 'days'. NOTE: assumed values TIME column ipd pseudo_ipd unit days km_conf_type string, pass conf.type survfit binary_robust_cov_type string pass argument type sandwich::vcovHC, see possible options documentation function. Default \"HC3\"","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maic_anchored.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","text":"list, contains 'descriptive' 'inferential'","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maic_anchored.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","text":"required input ipd pseudo_ipd following columns. function sensitive upper lower case letters column names. USUBJID - character, unique subject ID ARM - character factor, treatment indicator, column name 'ARM'. User specify trt_var_ipd trt_var_agd time--event analysis, follow columns required: EVENT - numeric, 1 censored/death, 0 otherwise TIME - numeric column, observation time EVENT; unit days binary outcomes: RESPONSE - numeric, 1 event occurred, 0 otherwise","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maic_anchored.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","text":"","code":"# Anchored example using maic_anchored for time-to-event data data(weighted_twt) data(adtte_twt) data(pseudo_ipd_twt) result_tte <- maic_anchored( weights_object = weighted_twt, ipd = adtte_twt, pseudo_ipd = pseudo_ipd_twt, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", trt_ipd = \"A\", trt_agd = \"B\", trt_common = \"C\", endpoint_name = \"Overall Survival\", endpoint_type = \"tte\", eff_measure = \"HR\", time_scale = \"month\", km_conf_type = \"log-log\", ) result_tte$inferential$report_median_surv #> treatment type records n.max n.start events rmean #> 1 ARM=C IPD, before matching 500 500.0000 500.0000 500.0000 2.564797 #> 2 ARM=A IPD, before matching 500 500.0000 500.0000 190.0000 8.709690 #> 3 ARM=C IPD, after matching 500 173.4208 173.4208 173.4208 2.690665 #> 4 ARM=A IPD, after matching 500 173.4208 173.4208 55.5418 10.575301 #> 5 ARM=C AgD, external 500 500.0000 500.0000 500.0000 2.455272 #> 6 ARM=B AgD, external 300 300.0000 300.0000 178.0000 4.303551 #> se(rmean) median 0.95LCL 0.95UCL #> 1 0.11366994 1.836467 1.644765 2.045808 #> 2 0.35514766 7.587627 6.278691 10.288538 #> 3 0.20750373 1.818345 1.457222 2.352181 #> 4 0.57325902 12.166430 10.244293 NA #> 5 0.09848888 1.851987 1.670540 2.009650 #> 6 0.33672602 2.746131 2.261125 3.320857 result_tte$inferential$report_overall_robustCI #> Matching treatment N n.events(%) median[95% CI] #> 2 IPD/Overall Survival ARM=A 500 190( 38.0) 7.6[6.3;10.3] #> 1 ARM=C 500 500(100.0) 1.8[1.6; 2.0] #> 21 weighted IPD/Overall Survival ARM=A 173.4 55.5( 32.0) 12.2[10.2; NA] #> 11 ARM=C 173.4 173.4(100.0) 1.8[ 1.5;2.4] #> 22 Agd/Overall Survival ARM=B 300 178( 59.3) 2.7[2.3;3.3] #> 12 ARM=C 500 500(100.0) 1.9[1.7;2.0] #> 7 ** adj.A vs B -- -- -- -- #> HR[95% CI] p-Value #> 2 0.22[0.19;0.26] <0.001 #> 1 #> 21 0.16[0.11;0.24] <0.001 #> 11 #> 22 0.57[0.48;0.68] <0.001 #> 12 #> 7 0.29 [0.19; 0.44] <0.001 result_tte$inferential$report_overall_bootCI #> Matching treatment N n.events(%) median[95% CI] #> 2 IPD/Overall Survival ARM=A 500 190( 38.0) 7.6[6.3;10.3] #> 1 ARM=C 500 500(100.0) 1.8[1.6; 2.0] #> 21 weighted IPD/Overall Survival ARM=A 173.4 55.5( 32.0) 12.2[10.2; NA] #> 11 ARM=C 173.4 173.4(100.0) 1.8[ 1.5;2.4] #> 22 AgD/Overall Survival ARM=B 300 178( 59.3) 2.7[2.3;3.3] #> 12 ARM=C 500 500(100.0) 1.9[1.7;2.0] #> 7 ** adj.A vs B -- -- -- -- #> HR[95% CI] p-Value #> 2 0.22[0.19;0.26] <0.001 #> 1 #> 21 0.16[0.11;0.24] <0.001 #> 11 #> 22 0.57[0.48;0.68] <0.001 #> 12 #> 7 0.29 [0.20; 0.43] # Anchored example using maic_anchored for binary outcome data(weighted_twt) data(adrs_twt) # Reported summary data pseudo_adrs <- get_pseudo_ipd_binary( binary_agd = data.frame( ARM = c(\"B\", \"C\", \"B\", \"C\"), RESPONSE = c(\"YES\", \"YES\", \"NO\", \"NO\"), COUNT = c(280, 120, 200, 200) ), format = \"stacked\" ) # inferential result result_binary <- maic_anchored( weights_object = weighted_twt, ipd = adrs_twt, pseudo_ipd = pseudo_adrs, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", trt_ipd = \"A\", trt_agd = \"B\", trt_common = \"C\", endpoint_name = \"Binary Event\", endpoint_type = \"binary\", eff_measure = \"OR\" ) #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Warning: non-integer #successes in a binomial glm! #> Waiting for profiling to be done... #> Waiting for profiling to be done... #> Waiting for profiling to be done... #> Waiting for profiling to be done... result_binary$inferential$report_overall_robustCI #> Matching treatment N n.events(%) OR[95% CI] #> A IPD/Binary Event A 500 390(78.0) 1.70[1.28;2.26] #> C C 500 338(67.6) #> A1 weighted IPD/Binary Event A 500 128.8(25.8) 1.14[0.67;1.95] #> C1 C 500 124.2(24.8) #> B AgD/Binary Event B 480 280(58.3) 2.33[1.75;3.12] #> C2 C 320 120(37.5) #> 7 ** adj.A vs B -- -- -- 0.49 [0.27; 0.90] #> p-Value #> A <0.001 #> C #> A1 0.624 #> C1 #> B <0.001 #> C2 #> 7 0.022 result_binary$inferential$report_overall_bootCI #> Matching treatment N n.events(%) OR[95% CI] #> A IPD/Binary Event A 500 390(78.0) 1.70[1.28;2.26] #> C C 500 338(67.6) #> A1 weighted IPD/Binary Event A 500 128.8(25.8) 1.14[0.33;0.98] #> C1 C 500 124.2(24.8) #> B AgD/Binary Event B 480 280(58.3) 2.33[1.75;3.12] #> C2 C 320 120(37.5) #> 7 ** adj.A vs B -- -- -- 0.49 [0.14; 0.42] #> p-Value #> A <0.001 #> C #> A1 NA #> C1 #> B <0.001 #> C2 #> 7 "},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maic_unanchored.html","id":null,"dir":"Reference","previous_headings":"","what":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","title":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","text":"wrapper function provide adjusted effect estimates relevant statistics unanchored case (.e. common comparator arm internal external trial).","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maic_unanchored.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","text":"","code":"maic_unanchored( weights_object, ipd, pseudo_ipd, trt_ipd, trt_agd, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_type = \"tte\", endpoint_name = \"Time to Event Endpoint\", eff_measure = c(\"HR\", \"OR\", \"RR\", \"RD\"), boot_ci_type = c(\"norm\", \"basic\", \"stud\", \"perc\", \"bca\"), time_scale = \"months\", km_conf_type = \"log-log\", binary_robust_cov_type = \"HC3\" )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maic_unanchored.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","text":"weights_object object returned estimate_weight ipd data frame meet format requirements 'Details', individual patient data (IPD) internal trial pseudo_ipd data frame, pseudo IPD digitized KM curve external trial (time--event endpoint) contingency table (binary endpoint) trt_ipd string, name interested investigation arm internal trial dat_igd (real IPD) trt_agd string, name interested investigation arm external trial pseudo_ipd (pseudo IPD) trt_var_ipd string, column name ipd contains treatment assignment trt_var_agd string, column name ipd contains treatment assignment endpoint_type string, one following \"binary\", \"tte\" (time event) endpoint_name string, name time event endpoint, show last line title eff_measure string, \"RD\" (risk difference), \"\" (odds ratio), \"RR\" (relative risk) binary endpoint; \"HR\" time--event endpoint. default NULL, \"\" used binary case, otherwise \"HR\" used. boot_ci_type string, one c(\"norm\",\"basic\", \"stud\", \"perc\", \"bca\") select type bootstrap confidence interval. See boot::boot.ci details. time_scale string, time unit median survival time, taking value 'years', 'months', 'weeks' 'days'. NOTE: assumed values TIME column ipd pseudo_ipd unit days km_conf_type string, pass conf.type survfit binary_robust_cov_type string pass argument type sandwich::vcovHC, see possible options documentation function. Default \"HC3\"","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maic_unanchored.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","text":"list, contains 'descriptive' 'inferential'","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maic_unanchored.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","text":"time--event analysis, required input ipd pseudo_ipd following columns. function sensitive upper lower case letters column names. USUBJID - character, unique subject ID ARM - character factor, treatment indicator, column name 'ARM'. User specify trt_var_ipd trt_var_agd EVENT - numeric, 1 censored/death, 0 otherwise TIME - numeric column, observation time EVENT; unit days","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maic_unanchored.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","text":"","code":"# unanchored example using maic_unanchored for time-to-event data data(centered_ipd_sat) data(adtte_sat) data(pseudo_ipd_sat) #### derive weights weighted_data <- estimate_weights( data = centered_ipd_sat, centered_colnames = grep(\"_CENTERED$\", names(centered_ipd_sat)), start_val = 0, method = \"BFGS\" ) weighted_data2 <- estimate_weights( data = centered_ipd_sat, centered_colnames = grep(\"_CENTERED$\", names(centered_ipd_sat)), start_val = 0, method = \"BFGS\", n_boot_iteration = 500, set_seed_boot = 1234 ) # inferential result result <- maic_unanchored( weights_object = weighted_data, ipd = adtte_sat, pseudo_ipd = pseudo_ipd_sat, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", trt_ipd = \"A\", trt_agd = \"B\", endpoint_name = \"Overall Survival\", endpoint_type = \"tte\", eff_measure = \"HR\", time_scale = \"month\", km_conf_type = \"log-log\" ) result$inferential$report_median_surv #> treatment type records n.max n.start events rmean #> 1 ARM=B Before matching 300 300.0000 300.0000 178.00000 4.303551 #> 2 ARM=A Before matching 500 500.0000 500.0000 190.00000 8.709690 #> 3 ARM=B After matching 300 300.0000 300.0000 178.00000 4.303551 #> 4 ARM=A After matching 500 173.3137 173.3137 55.37392 10.584605 #> se(rmean) median 0.95LCL 0.95UCL #> 1 0.3367260 2.746131 2.261125 3.320857 #> 2 0.3551477 7.587627 6.278691 10.288538 #> 3 0.3367260 2.746131 2.261125 3.320857 #> 4 0.5739799 12.166430 10.244293 NA result$inferential$report_overall_robustCI #> Matching treatment N n.events(%) median[95% CI] #> 2 Before matching/Overall Survival ARM=A 500.0 190(38.0) 7.6[6.3;10.3] #> 1 ARM=B 300.0 178(59.3) 2.7[2.3; 3.3] #> 21 After matching/Overall Survival ARM=A 173.3 55.4(32.0) 12.2[10.2; NA] #> 11 ARM=B 300.0 178(59.3) 2.7[ 2.3;3.3] #> HR[95% CI] p-Value #> 2 0.37[0.30;0.46] <0.001 #> 1 #> 21 0.26[0.18;0.38] <0.001 #> 11 result_boot <- maic_unanchored( weights_object = weighted_data2, ipd = adtte_sat, pseudo_ipd = pseudo_ipd_sat, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", trt_ipd = \"A\", trt_agd = \"B\", endpoint_name = \"Overall Survival\", endpoint_type = \"tte\", eff_measure = \"HR\", time_scale = \"month\", km_conf_type = \"log-log\" ) result_boot$inferential$report_median_surv #> treatment type records n.max n.start events rmean #> 1 ARM=B Before matching 300 300.0000 300.0000 178.00000 4.303551 #> 2 ARM=A Before matching 500 500.0000 500.0000 190.00000 8.709690 #> 3 ARM=B After matching 300 300.0000 300.0000 178.00000 4.303551 #> 4 ARM=A After matching 500 173.3137 173.3137 55.37392 10.584605 #> se(rmean) median 0.95LCL 0.95UCL #> 1 0.3367260 2.746131 2.261125 3.320857 #> 2 0.3551477 7.587627 6.278691 10.288538 #> 3 0.3367260 2.746131 2.261125 3.320857 #> 4 0.5739799 12.166430 10.244293 NA result_boot$inferential$report_overall_robustCI #> Matching treatment N n.events(%) median[95% CI] #> 2 Before matching/Overall Survival ARM=A 500.0 190(38.0) 7.6[6.3;10.3] #> 1 ARM=B 300.0 178(59.3) 2.7[2.3; 3.3] #> 21 After matching/Overall Survival ARM=A 173.3 55.4(32.0) 12.2[10.2; NA] #> 11 ARM=B 300.0 178(59.3) 2.7[ 2.3;3.3] #> HR[95% CI] p-Value #> 2 0.37[0.30;0.46] <0.001 #> 1 #> 21 0.26[0.18;0.38] <0.001 #> 11 result_boot$inferential$report_overall_bootCI #> Matching treatment N n.events(%) median[95% CI] #> 2 Before matching/Overall Survival ARM=A 500.0 190(38.0) 7.6[6.3;10.3] #> 1 ARM=B 300.0 178(59.3) 2.7[2.3; 3.3] #> 21 After matching/Overall Survival ARM=A 173.3 55.4(32.0) 12.2[10.2; NA] #> 11 ARM=B 300.0 178(59.3) 2.7[ 2.3;3.3] #> HR[95% CI] p-Value #> 2 0.37[0.30;0.46] <0.001 #> 1 #> 21 0.26[NA;0.19] #> 11 # unanchored example using maic_unanchored for binary outcome data(centered_ipd_sat) data(adrs_sat) centered_ipd_sat #> USUBJID ARM AGE SEX SMOKE ECOG0 N_PR_THER SEX_MALE AGE_CENTERED #> 1 1 A 45 Male 0 0 4 1 -6 #> 2 2 A 71 Male 0 0 3 1 20 #> 3 3 A 58 Male 1 1 2 1 7 #> 4 4 A 48 Female 0 1 4 0 -3 #> 5 5 A 69 Male 0 1 4 1 18 #> 6 6 A 48 Female 0 1 4 0 -3 #> 7 7 A 47 Male 1 0 3 1 -4 #> 8 8 A 61 Male 1 0 1 1 10 #> 9 9 A 54 Female 1 1 1 0 3 #> 10 10 A 56 Female 1 0 3 0 5 #> 11 11 A 63 Female 0 0 4 0 12 #> 12 12 A 50 Female 0 0 1 0 -1 #> 13 13 A 57 Male 0 1 3 1 6 #> 14 14 A 62 Female 1 1 1 0 11 #> 15 15 A 57 Female 0 1 3 0 6 #> 16 16 A 66 Male 0 0 2 1 15 #> 17 17 A 75 Male 1 1 3 1 24 #> 18 18 A 47 Female 0 0 4 0 -4 #> 19 19 A 57 Male 0 0 3 1 6 #> 20 20 A 54 Male 0 0 3 1 3 #> 21 21 A 55 Male 1 0 3 1 4 #> 22 22 A 64 Male 0 1 3 1 13 #> 23 23 A 53 Female 1 0 3 0 2 #> 24 24 A 58 Male 1 1 2 1 7 #> 25 25 A 47 Male 0 0 1 1 -4 #> 26 26 A 60 Female 1 0 1 0 9 #> 27 27 A 49 Female 0 1 3 0 -2 #> 28 28 A 55 Female 0 0 1 0 4 #> 29 29 A 66 Female 0 1 2 0 15 #> 30 30 A 58 Male 0 1 4 1 7 #> 31 31 A 49 Male 0 1 4 1 -2 #> 32 32 A 61 Male 0 0 4 1 10 #> 33 33 A 66 Male 1 0 3 1 15 #> 34 34 A 45 Male 0 0 1 1 -6 #> 35 35 A 59 Female 1 1 2 0 8 #> 36 36 A 74 Female 1 0 4 0 23 #> 37 37 A 73 Female 0 0 3 0 22 #> 38 38 A 74 Male 0 1 4 1 23 #> 39 39 A 54 Male 0 0 1 1 3 #> 40 40 A 58 Female 1 1 1 0 7 #> 41 41 A 61 Female 0 1 3 0 10 #> 42 42 A 47 Female 1 1 2 0 -4 #> 43 43 A 73 Female 1 1 2 0 22 #> 44 44 A 68 Male 0 0 1 1 17 #> 45 45 A 49 Female 0 0 3 0 -2 #> 46 46 A 71 Female 0 0 2 0 20 #> 47 47 A 70 Male 0 1 4 1 19 #> 48 48 A 62 Female 1 0 1 0 11 #> 49 49 A 49 Male 0 0 1 1 -2 #> 50 50 A 74 Female 0 0 1 0 23 #> 51 51 A 46 Female 0 1 3 0 -5 #> 52 52 A 68 Female 1 0 3 0 17 #> 53 53 A 46 Male 1 0 2 1 -5 #> 54 54 A 75 Female 1 1 3 0 24 #> 55 55 A 47 Female 0 0 3 0 -4 #> 56 56 A 56 Male 0 1 3 1 5 #> 57 57 A 72 Female 0 0 3 0 21 #> 58 58 A 57 Male 1 1 4 1 6 #> 59 59 A 46 Male 0 0 1 1 -5 #> 60 60 A 56 Female 1 1 1 0 5 #> 61 61 A 73 Male 0 1 2 1 22 #> 62 62 A 60 Female 1 1 3 0 9 #> 63 63 A 75 Male 0 0 2 1 24 #> 64 64 A 69 Female 1 1 2 0 18 #> 65 65 A 47 Female 0 1 1 0 -4 #> 66 66 A 74 Male 0 0 4 1 23 #> 67 67 A 71 Female 0 1 1 0 20 #> 68 68 A 49 Female 1 1 1 0 -2 #> 69 69 A 68 Male 0 0 3 1 17 #> 70 70 A 49 Male 0 1 1 1 -2 #> 71 71 A 70 Male 0 1 1 1 19 #> 72 72 A 45 Female 0 0 2 0 -6 #> 73 73 A 47 Female 0 1 3 0 -4 #> 74 74 A 58 Male 0 1 3 1 7 #> 75 75 A 49 Female 0 1 4 0 -2 #> 76 76 A 68 Female 0 0 1 0 17 #> 77 77 A 60 Male 0 0 4 1 9 #> 78 78 A 45 Female 1 0 1 0 -6 #> 79 79 A 57 Female 0 0 1 0 6 #> 80 80 A 50 Female 0 1 1 0 -1 #> 81 81 A 63 Male 0 1 3 1 12 #> 82 82 A 47 Female 0 0 2 0 -4 #> 83 83 A 68 Female 0 1 4 0 17 #> 84 84 A 51 Male 0 0 4 1 0 #> 85 85 A 60 Male 0 0 1 1 9 #> 86 86 A 52 Female 1 0 4 0 1 #> 87 87 A 69 Male 1 1 1 1 18 #> 88 88 A 70 Female 0 1 4 0 19 #> 89 89 A 72 Male 0 0 2 1 21 #> 90 90 A 46 Female 0 1 1 0 -5 #> 91 91 A 51 Male 1 0 4 1 0 #> 92 92 A 69 Female 0 1 1 0 18 #> 93 93 A 66 Male 0 0 1 1 15 #> 94 94 A 73 Male 0 1 1 1 22 #> 95 95 A 73 Female 1 0 3 0 22 #> 96 96 A 62 Female 0 1 2 0 11 #> 97 97 A 55 Female 0 0 4 0 4 #> 98 98 A 67 Male 1 0 3 1 16 #> 99 99 A 54 Female 1 0 3 0 3 #> 100 100 A 52 Female 1 1 4 0 1 #> 101 101 A 57 Male 0 0 2 1 6 #> 102 102 A 57 Female 1 1 3 0 6 #> 103 103 A 57 Male 0 0 3 1 6 #> 104 104 A 67 Female 1 1 2 0 16 #> 105 105 A 67 Female 1 1 2 0 16 #> 106 106 A 74 Female 1 1 2 0 23 #> 107 107 A 72 Female 1 0 2 0 21 #> 108 108 A 73 Female 0 0 3 0 22 #> 109 109 A 57 Female 0 0 4 0 6 #> 110 110 A 69 Female 1 0 1 0 18 #> 111 111 A 55 Male 0 0 1 1 4 #> 112 112 A 74 Female 0 0 4 0 23 #> 113 113 A 68 Female 0 0 4 0 17 #> 114 114 A 53 Male 0 0 2 1 2 #> 115 115 A 69 Male 0 0 2 1 18 #> 116 116 A 68 Male 0 1 2 1 17 #> 117 117 A 58 Male 0 0 1 1 7 #> 118 118 A 64 Female 0 0 3 0 13 #> 119 119 A 71 Male 0 0 1 1 20 #> 120 120 A 69 Female 0 1 2 0 18 #> 121 121 A 64 Female 1 0 4 0 13 #> 122 122 A 64 Male 1 0 1 1 13 #> 123 123 A 55 Male 0 1 3 1 4 #> 124 124 A 74 Male 0 0 3 1 23 #> 125 125 A 50 Male 0 1 3 1 -1 #> 126 126 A 68 Male 0 1 1 1 17 #> 127 127 A 60 Male 0 0 2 1 9 #> 128 128 A 59 Female 0 0 3 0 8 #> 129 129 A 71 Female 1 0 3 0 20 #> 130 130 A 69 Male 0 1 4 1 18 #> 131 131 A 56 Female 0 1 1 0 5 #> 132 132 A 51 Male 0 1 3 1 0 #> 133 133 A 65 Male 0 1 2 1 14 #> 134 134 A 45 Male 1 1 3 1 -6 #> 135 135 A 49 Female 0 0 2 0 -2 #> 136 136 A 74 Female 1 0 4 0 23 #> 137 137 A 71 Female 1 1 3 0 20 #> 138 138 A 65 Male 0 0 1 1 14 #> 139 139 A 67 Female 0 0 2 0 16 #> 140 140 A 48 Female 0 0 3 0 -3 #> 141 141 A 70 Female 0 1 4 0 19 #> 142 142 A 72 Female 0 0 1 0 21 #> 143 143 A 53 Male 1 1 4 1 2 #> 144 144 A 68 Female 1 0 3 0 17 #> 145 145 A 65 Male 0 1 1 1 14 #> 146 146 A 70 Female 1 1 4 0 19 #> 147 147 A 58 Male 0 0 2 1 7 #> 148 148 A 68 Female 0 1 4 0 17 #> 149 149 A 54 Female 0 0 4 0 3 #> 150 150 A 75 Female 1 1 4 0 24 #> 151 151 A 49 Female 1 1 3 0 -2 #> 152 152 A 60 Male 0 0 4 1 9 #> 153 153 A 45 Female 1 0 4 0 -6 #> 154 154 A 73 Female 1 0 3 0 22 #> 155 155 A 71 Female 0 1 3 0 20 #> 156 156 A 73 Female 0 0 3 0 22 #> 157 157 A 58 Female 0 0 4 0 7 #> 158 158 A 59 Female 0 1 2 0 8 #> 159 159 A 75 Female 0 1 2 0 24 #> 160 160 A 65 Male 0 0 2 1 14 #> 161 161 A 48 Male 1 0 2 1 -3 #> 162 162 A 63 Female 0 1 2 0 12 #> 163 163 A 74 Female 0 0 3 0 23 #> 164 164 A 64 Female 0 0 3 0 13 #> 165 165 A 49 Female 0 0 1 0 -2 #> 166 166 A 56 Female 0 1 4 0 5 #> 167 167 A 56 Female 0 1 2 0 5 #> 168 168 A 48 Female 0 0 1 0 -3 #> 169 169 A 65 Male 0 1 3 1 14 #> 170 170 A 53 Female 0 0 3 0 2 #> 171 171 A 72 Male 0 0 2 1 21 #> 172 172 A 75 Female 1 0 4 0 24 #> 173 173 A 70 Female 0 1 2 0 19 #> 174 174 A 53 Female 1 0 2 0 2 #> 175 175 A 45 Female 1 1 3 0 -6 #> 176 176 A 53 Female 1 1 3 0 2 #> 177 177 A 52 Female 0 1 2 0 1 #> 178 178 A 61 Female 1 0 4 0 10 #> 179 179 A 70 Male 0 0 3 1 19 #> 180 180 A 58 Female 0 0 3 0 7 #> 181 181 A 54 Male 0 0 1 1 3 #> 182 182 A 53 Male 0 0 2 1 2 #> 183 183 A 74 Male 0 0 2 1 23 #> 184 184 A 64 Male 0 1 3 1 13 #> 185 185 A 52 Male 0 0 1 1 1 #> 186 186 A 73 Female 0 0 3 0 22 #> 187 187 A 55 Female 1 0 4 0 4 #> 188 188 A 71 Female 0 0 3 0 20 #> 189 189 A 57 Female 1 0 3 0 6 #> 190 190 A 49 Female 1 0 2 0 -2 #> 191 191 A 69 Male 0 0 4 1 18 #> 192 192 A 74 Female 1 0 2 0 23 #> 193 193 A 59 Female 0 0 3 0 8 #> 194 194 A 53 Male 0 0 4 1 2 #> 195 195 A 52 Female 0 1 3 0 1 #> 196 196 A 47 Female 1 1 1 0 -4 #> 197 197 A 61 Female 0 0 4 0 10 #> 198 198 A 51 Female 0 0 2 0 0 #> 199 199 A 62 Female 1 0 1 0 11 #> 200 200 A 59 Female 1 0 2 0 8 #> 201 201 A 58 Male 0 0 2 1 7 #> 202 202 A 61 Female 0 1 4 0 10 #> 203 203 A 45 Female 0 0 1 0 -6 #> 204 204 A 59 Male 1 1 2 1 8 #> 205 205 A 58 Female 1 0 2 0 7 #> 206 206 A 67 Male 0 0 1 1 16 #> 207 207 A 51 Female 1 0 2 0 0 #> 208 208 A 68 Male 1 1 3 1 17 #> 209 209 A 53 Female 1 0 2 0 2 #> 210 210 A 64 Male 1 0 3 1 13 #> 211 211 A 61 Male 0 1 1 1 10 #> 212 212 A 52 Male 1 0 2 1 1 #> 213 213 A 57 Female 0 1 4 0 6 #> 214 214 A 57 Female 1 0 2 0 6 #> 215 215 A 49 Female 0 0 4 0 -2 #> 216 216 A 51 Male 1 1 3 1 0 #> 217 217 A 60 Female 0 0 2 0 9 #> 218 218 A 60 Female 1 0 1 0 9 #> 219 219 A 71 Female 0 0 3 0 20 #> 220 220 A 54 Female 0 0 1 0 3 #> 221 221 A 51 Male 1 0 1 1 0 #> 222 222 A 71 Male 0 1 3 1 20 #> 223 223 A 47 Male 0 0 2 1 -4 #> 224 224 A 65 Male 0 0 1 1 14 #> 225 225 A 53 Female 0 0 1 0 2 #> 226 226 A 56 Female 0 0 1 0 5 #> 227 227 A 51 Male 1 0 4 1 0 #> 228 228 A 68 Female 0 0 2 0 17 #> 229 229 A 75 Female 0 1 1 0 24 #> 230 230 A 49 Female 0 0 2 0 -2 #> 231 231 A 74 Male 1 0 1 1 23 #> 232 232 A 66 Female 0 0 1 0 15 #> 233 233 A 74 Female 0 0 1 0 23 #> 234 234 A 62 Female 0 1 2 0 11 #> 235 235 A 53 Female 0 0 1 0 2 #> 236 236 A 62 Female 0 1 1 0 11 #> 237 237 A 70 Female 1 0 3 0 19 #> 238 238 A 60 Female 1 0 2 0 9 #> 239 239 A 72 Male 0 0 1 1 21 #> 240 240 A 74 Female 1 0 3 0 23 #> 241 241 A 47 Female 1 0 3 0 -4 #> 242 242 A 54 Female 0 1 3 0 3 #> 243 243 A 65 Female 0 1 2 0 14 #> 244 244 A 74 Male 0 1 1 1 23 #> 245 245 A 61 Male 0 1 2 1 10 #> 246 246 A 54 Female 1 0 3 0 3 #> 247 247 A 65 Male 0 0 3 1 14 #> 248 248 A 72 Female 1 0 3 0 21 #> 249 249 A 71 Female 1 1 2 0 20 #> 250 250 A 65 Male 0 0 3 1 14 #> 251 251 A 58 Male 0 1 1 1 7 #> 252 252 A 46 Male 0 0 1 1 -5 #> 253 253 A 53 Female 1 0 4 0 2 #> 254 254 A 71 Female 1 0 4 0 20 #> 255 255 A 47 Male 0 1 3 1 -4 #> 256 256 A 45 Male 0 0 1 1 -6 #> 257 257 A 50 Male 0 0 3 1 -1 #> 258 258 A 67 Female 1 0 3 0 16 #> 259 259 A 72 Male 1 0 2 1 21 #> 260 260 A 45 Female 0 1 2 0 -6 #> 261 261 A 75 Female 1 0 1 0 24 #> 262 262 A 65 Male 0 0 3 1 14 #> 263 263 A 60 Female 0 1 3 0 9 #> 264 264 A 75 Female 0 1 4 0 24 #> 265 265 A 60 Female 1 0 4 0 9 #> 266 266 A 49 Female 1 0 2 0 -2 #> 267 267 A 58 Female 0 1 1 0 7 #> 268 268 A 57 Male 1 1 3 1 6 #> 269 269 A 69 Male 1 0 2 1 18 #> 270 270 A 51 Male 0 1 1 1 0 #> 271 271 A 54 Female 1 0 4 0 3 #> 272 272 A 55 Male 0 1 3 1 4 #> 273 273 A 49 Female 0 0 4 0 -2 #> 274 274 A 74 Female 1 0 1 0 23 #> 275 275 A 55 Male 1 0 1 1 4 #> 276 276 A 52 Female 1 0 1 0 1 #> 277 277 A 65 Male 0 0 2 1 14 #> 278 278 A 70 Female 1 0 1 0 19 #> 279 279 A 66 Female 1 1 2 0 15 #> 280 280 A 63 Female 0 1 4 0 12 #> 281 281 A 61 Female 0 1 3 0 10 #> 282 282 A 65 Male 0 1 2 1 14 #> 283 283 A 73 Male 0 0 2 1 22 #> 284 284 A 55 Female 1 1 4 0 4 #> 285 285 A 56 Female 1 1 4 0 5 #> 286 286 A 68 Female 0 1 1 0 17 #> 287 287 A 74 Female 1 0 4 0 23 #> 288 288 A 67 Female 0 0 2 0 16 #> 289 289 A 66 Male 0 1 3 1 15 #> 290 290 A 48 Female 0 0 3 0 -3 #> 291 291 A 49 Female 1 1 3 0 -2 #> 292 292 A 60 Female 1 1 1 0 9 #> 293 293 A 69 Female 0 1 4 0 18 #> 294 294 A 58 Female 0 1 3 0 7 #> 295 295 A 45 Female 1 1 4 0 -6 #> 296 296 A 49 Female 0 1 1 0 -2 #> 297 297 A 67 Female 1 0 4 0 16 #> 298 298 A 63 Male 0 0 4 1 12 #> 299 299 A 50 Female 0 0 2 0 -1 #> 300 300 A 68 Female 0 1 3 0 17 #> 301 301 A 53 Male 0 1 2 1 2 #> 302 302 A 63 Male 0 1 2 1 12 #> 303 303 A 58 Male 0 0 4 1 7 #> 304 304 A 70 Female 0 1 4 0 19 #> 305 305 A 56 Female 0 0 1 0 5 #> 306 306 A 56 Male 0 1 3 1 5 #> 307 307 A 61 Female 0 0 3 0 10 #> 308 308 A 72 Male 0 0 2 1 21 #> 309 309 A 51 Male 0 1 1 1 0 #> 310 310 A 72 Male 0 0 4 1 21 #> 311 311 A 64 Female 0 0 3 0 13 #> 312 312 A 59 Male 0 0 2 1 8 #> 313 313 A 75 Female 0 0 3 0 24 #> 314 314 A 75 Female 0 0 1 0 24 #> 315 315 A 74 Male 0 0 2 1 23 #> 316 316 A 54 Male 0 1 4 1 3 #> 317 317 A 55 Female 0 0 3 0 4 #> 318 318 A 52 Female 1 0 1 0 1 #> 319 319 A 46 Female 0 1 1 0 -5 #> 320 320 A 53 Male 0 0 1 1 2 #> 321 321 A 54 Female 0 1 1 0 3 #> 322 322 A 62 Female 0 0 2 0 11 #> 323 323 A 54 Male 1 0 4 1 3 #> 324 324 A 56 Female 0 0 4 0 5 #> 325 325 A 48 Female 0 0 1 0 -3 #> 326 326 A 52 Female 0 1 1 0 1 #> 327 327 A 55 Female 0 1 3 0 4 #> 328 328 A 69 Female 0 0 2 0 18 #> 329 329 A 48 Female 0 0 1 0 -3 #> 330 330 A 48 Female 1 1 3 0 -3 #> 331 331 A 60 Male 0 0 3 1 9 #> 332 332 A 74 Female 0 1 2 0 23 #> 333 333 A 45 Female 0 0 1 0 -6 #> 334 334 A 64 Male 1 0 1 1 13 #> 335 335 A 75 Female 1 1 3 0 24 #> 336 336 A 62 Female 0 0 3 0 11 #> 337 337 A 71 Male 0 0 4 1 20 #> 338 338 A 48 Female 1 1 3 0 -3 #> 339 339 A 53 Female 0 0 2 0 2 #> 340 340 A 62 Male 0 0 3 1 11 #> 341 341 A 69 Female 0 0 2 0 18 #> 342 342 A 72 Female 0 0 1 0 21 #> 343 343 A 61 Female 0 0 2 0 10 #> 344 344 A 47 Male 0 0 2 1 -4 #> 345 345 A 58 Male 0 1 1 1 7 #> 346 346 A 52 Female 0 0 4 0 1 #> 347 347 A 49 Female 0 0 4 0 -2 #> 348 348 A 51 Female 0 0 1 0 0 #> 349 349 A 51 Female 1 1 2 0 0 #> 350 350 A 72 Male 0 0 3 1 21 #> 351 351 A 68 Male 0 0 3 1 17 #> 352 352 A 49 Female 0 0 4 0 -2 #> 353 353 A 45 Female 0 0 3 0 -6 #> 354 354 A 49 Female 0 0 3 0 -2 #> 355 355 A 65 Male 0 1 2 1 14 #> 356 356 A 56 Male 0 0 2 1 5 #> 357 357 A 45 Female 1 1 1 0 -6 #> 358 358 A 57 Male 1 0 2 1 6 #> 359 359 A 53 Male 1 1 2 1 2 #> 360 360 A 65 Female 0 0 2 0 14 #> 361 361 A 57 Male 0 1 4 1 6 #> 362 362 A 55 Female 0 1 4 0 4 #> 363 363 A 57 Male 0 1 2 1 6 #> 364 364 A 46 Female 0 1 4 0 -5 #> 365 365 A 69 Female 0 1 1 0 18 #> 366 366 A 67 Female 0 1 3 0 16 #> 367 367 A 55 Male 0 0 1 1 4 #> 368 368 A 53 Female 0 1 3 0 2 #> 369 369 A 46 Female 0 0 3 0 -5 #> 370 370 A 71 Male 0 0 4 1 20 #> 371 371 A 68 Male 0 1 1 1 17 #> 372 372 A 49 Female 0 1 3 0 -2 #> 373 373 A 51 Female 0 0 3 0 0 #> 374 374 A 65 Female 1 1 3 0 14 #> 375 375 A 55 Female 0 0 4 0 4 #> 376 376 A 53 Male 0 0 4 1 2 #> 377 377 A 65 Female 0 1 4 0 14 #> 378 378 A 72 Female 1 1 4 0 21 #> 379 379 A 61 Male 0 1 1 1 10 #> 380 380 A 73 Female 0 0 1 0 22 #> 381 381 A 62 Female 1 1 2 0 11 #> 382 382 A 46 Male 0 0 2 1 -5 #> 383 383 A 51 Male 0 1 4 1 0 #> 384 384 A 60 Male 1 1 4 1 9 #> 385 385 A 56 Female 0 1 3 0 5 #> 386 386 A 69 Female 0 1 1 0 18 #> 387 387 A 58 Female 1 1 2 0 7 #> 388 388 A 58 Female 1 1 3 0 7 #> 389 389 A 53 Female 0 1 2 0 2 #> 390 390 A 47 Female 0 0 2 0 -4 #> 391 391 A 59 Male 0 1 2 1 8 #> 392 392 A 47 Female 0 0 4 0 -4 #> 393 393 A 60 Female 1 0 4 0 9 #> 394 394 A 73 Female 0 0 4 0 22 #> 395 395 A 60 Male 0 0 1 1 9 #> 396 396 A 75 Male 0 0 4 1 24 #> 397 397 A 65 Female 0 0 3 0 14 #> 398 398 A 68 Male 0 0 1 1 17 #> 399 399 A 55 Female 0 0 4 0 4 #> 400 400 A 46 Female 1 0 4 0 -5 #> 401 401 A 45 Female 0 1 1 0 -6 #> 402 402 A 70 Male 1 1 3 1 19 #> 403 403 A 56 Female 0 1 4 0 5 #> 404 404 A 62 Female 0 0 3 0 11 #> 405 405 A 49 Male 0 0 4 1 -2 #> 406 406 A 52 Female 0 0 4 0 1 #> 407 407 A 67 Female 0 1 4 0 16 #> 408 408 A 50 Female 1 1 1 0 -1 #> 409 409 A 68 Female 1 1 2 0 17 #> 410 410 A 54 Female 0 0 4 0 3 #> 411 411 A 65 Male 0 0 4 1 14 #> 412 412 A 55 Female 0 1 2 0 4 #> 413 413 A 53 Female 1 1 4 0 2 #> 414 414 A 71 Female 0 0 2 0 20 #> 415 415 A 48 Male 0 0 1 1 -3 #> 416 416 A 54 Female 0 1 3 0 3 #> 417 417 A 75 Male 1 0 3 1 24 #> 418 418 A 53 Female 0 0 2 0 2 #> 419 419 A 50 Female 1 0 4 0 -1 #> 420 420 A 64 Female 1 0 1 0 13 #> 421 421 A 65 Female 0 0 2 0 14 #> 422 422 A 65 Female 0 1 4 0 14 #> 423 423 A 60 Female 1 0 2 0 9 #> 424 424 A 70 Female 1 0 3 0 19 #> 425 425 A 51 Female 0 0 2 0 0 #> 426 426 A 45 Female 0 1 1 0 -6 #> 427 427 A 75 Female 1 0 2 0 24 #> 428 428 A 52 Female 1 0 1 0 1 #> 429 429 A 70 Male 0 0 4 1 19 #> 430 430 A 69 Female 1 1 3 0 18 #> 431 431 A 64 Female 0 0 2 0 13 #> 432 432 A 68 Female 1 0 1 0 17 #> 433 433 A 51 Male 1 0 1 1 0 #> 434 434 A 59 Female 0 1 2 0 8 #> 435 435 A 57 Female 0 0 1 0 6 #> 436 436 A 47 Male 0 0 2 1 -4 #> 437 437 A 65 Male 0 1 1 1 14 #> 438 438 A 65 Male 0 1 1 1 14 #> 439 439 A 65 Male 0 0 2 1 14 #> 440 440 A 46 Male 0 0 2 1 -5 #> 441 441 A 64 Female 0 0 3 0 13 #> 442 442 A 57 Female 0 1 4 0 6 #> 443 443 A 67 Female 0 1 3 0 16 #> 444 444 A 61 Female 1 0 3 0 10 #> 445 445 A 56 Male 0 0 4 1 5 #> 446 446 A 52 Male 0 0 3 1 1 #> 447 447 A 74 Female 1 1 3 0 23 #> 448 448 A 75 Male 0 1 3 1 24 #> 449 449 A 58 Male 0 1 3 1 7 #> 450 450 A 57 Female 0 1 4 0 6 #> 451 451 A 55 Female 0 0 1 0 4 #> 452 452 A 53 Female 1 0 1 0 2 #> 453 453 A 75 Male 1 0 4 1 24 #> 454 454 A 65 Female 0 1 3 0 14 #> 455 455 A 65 Female 0 0 4 0 14 #> 456 456 A 58 Male 0 0 4 1 7 #> 457 457 A 71 Female 0 0 3 0 20 #> 458 458 A 71 Male 0 0 4 1 20 #> 459 459 A 59 Male 1 0 4 1 8 #> 460 460 A 46 Male 1 0 4 1 -5 #> 461 461 A 51 Female 0 0 4 0 0 #> 462 462 A 56 Female 0 1 4 0 5 #> 463 463 A 66 Female 0 0 1 0 15 #> 464 464 A 59 Female 1 0 2 0 8 #> 465 465 A 48 Female 1 0 2 0 -3 #> 466 466 A 68 Female 0 0 4 0 17 #> 467 467 A 57 Female 0 0 4 0 6 #> 468 468 A 63 Male 0 0 4 1 12 #> 469 469 A 62 Male 0 1 3 1 11 #> 470 470 A 70 Female 0 0 3 0 19 #> 471 471 A 55 Female 0 1 2 0 4 #> 472 472 A 56 Male 1 0 3 1 5 #> 473 473 A 51 Male 1 0 1 1 0 #> 474 474 A 51 Female 0 0 4 0 0 #> 475 475 A 46 Female 1 1 3 0 -5 #> 476 476 A 52 Male 0 0 4 1 1 #> 477 477 A 71 Male 0 1 4 1 20 #> 478 478 A 54 Male 1 0 3 1 3 #> 479 479 A 55 Male 0 1 2 1 4 #> 480 480 A 46 Female 0 0 3 0 -5 #> 481 481 A 70 Female 0 0 1 0 19 #> 482 482 A 68 Female 0 1 3 0 17 #> 483 483 A 50 Male 0 1 1 1 -1 #> 484 484 A 45 Female 0 0 2 0 -6 #> 485 485 A 68 Male 0 0 1 1 17 #> 486 486 A 56 Male 0 1 2 1 5 #> 487 487 A 59 Male 0 1 3 1 8 #> 488 488 A 51 Male 1 1 1 1 0 #> 489 489 A 61 Female 1 0 3 0 10 #> 490 490 A 60 Female 0 1 3 0 9 #> 491 491 A 68 Female 1 0 2 0 17 #> 492 492 A 67 Male 1 1 4 1 16 #> 493 493 A 45 Male 1 1 2 1 -6 #> 494 494 A 71 Female 0 1 2 0 20 #> 495 495 A 55 Male 0 1 3 1 4 #> 496 496 A 72 Female 0 0 4 0 21 #> 497 497 A 48 Female 1 0 3 0 -3 #> 498 498 A 68 Female 1 0 1 0 17 #> 499 499 A 45 Female 0 0 1 0 -6 #> 500 500 A 58 Female 1 1 3 0 7 #> AGE_MEDIAN_CENTERED AGE_SQUARED_CENTERED SEX_MALE_CENTERED ECOG0_CENTERED #> 1 -0.5 -586.5625 0.51 -0.35 #> 2 0.5 2429.4375 0.51 -0.35 #> 3 0.5 752.4375 0.51 0.65 #> 4 -0.5 -307.5625 -0.49 0.65 #> 5 0.5 2149.4375 0.51 0.65 #> 6 -0.5 -307.5625 -0.49 0.65 #> 7 -0.5 -402.5625 0.51 -0.35 #> 8 0.5 1109.4375 0.51 -0.35 #> 9 0.5 304.4375 -0.49 0.65 #> 10 0.5 524.4375 -0.49 -0.35 #> 11 0.5 1357.4375 -0.49 -0.35 #> 12 0.5 -111.5625 -0.49 -0.35 #> 13 0.5 637.4375 0.51 0.65 #> 14 0.5 1232.4375 -0.49 0.65 #> 15 0.5 637.4375 -0.49 0.65 #> 16 0.5 1744.4375 0.51 -0.35 #> 17 0.5 3013.4375 0.51 0.65 #> 18 -0.5 -402.5625 -0.49 -0.35 #> 19 0.5 637.4375 0.51 -0.35 #> 20 0.5 304.4375 0.51 -0.35 #> 21 0.5 413.4375 0.51 -0.35 #> 22 0.5 1484.4375 0.51 0.65 #> 23 0.5 197.4375 -0.49 -0.35 #> 24 0.5 752.4375 0.51 0.65 #> 25 -0.5 -402.5625 0.51 -0.35 #> 26 0.5 988.4375 -0.49 -0.35 #> 27 -0.5 -210.5625 -0.49 0.65 #> 28 0.5 413.4375 -0.49 -0.35 #> 29 0.5 1744.4375 -0.49 0.65 #> 30 0.5 752.4375 0.51 0.65 #> 31 -0.5 -210.5625 0.51 0.65 #> 32 0.5 1109.4375 0.51 -0.35 #> 33 0.5 1744.4375 0.51 -0.35 #> 34 -0.5 -586.5625 0.51 -0.35 #> 35 0.5 869.4375 -0.49 0.65 #> 36 0.5 2864.4375 -0.49 -0.35 #> 37 0.5 2717.4375 -0.49 -0.35 #> 38 0.5 2864.4375 0.51 0.65 #> 39 0.5 304.4375 0.51 -0.35 #> 40 0.5 752.4375 -0.49 0.65 #> 41 0.5 1109.4375 -0.49 0.65 #> 42 -0.5 -402.5625 -0.49 0.65 #> 43 0.5 2717.4375 -0.49 0.65 #> 44 0.5 2012.4375 0.51 -0.35 #> 45 -0.5 -210.5625 -0.49 -0.35 #> 46 0.5 2429.4375 -0.49 -0.35 #> 47 0.5 2288.4375 0.51 0.65 #> 48 0.5 1232.4375 -0.49 -0.35 #> 49 -0.5 -210.5625 0.51 -0.35 #> 50 0.5 2864.4375 -0.49 -0.35 #> 51 -0.5 -495.5625 -0.49 0.65 #> 52 0.5 2012.4375 -0.49 -0.35 #> 53 -0.5 -495.5625 0.51 -0.35 #> 54 0.5 3013.4375 -0.49 0.65 #> 55 -0.5 -402.5625 -0.49 -0.35 #> 56 0.5 524.4375 0.51 0.65 #> 57 0.5 2572.4375 -0.49 -0.35 #> 58 0.5 637.4375 0.51 0.65 #> 59 -0.5 -495.5625 0.51 -0.35 #> 60 0.5 524.4375 -0.49 0.65 #> 61 0.5 2717.4375 0.51 0.65 #> 62 0.5 988.4375 -0.49 0.65 #> 63 0.5 3013.4375 0.51 -0.35 #> 64 0.5 2149.4375 -0.49 0.65 #> 65 -0.5 -402.5625 -0.49 0.65 #> 66 0.5 2864.4375 0.51 -0.35 #> 67 0.5 2429.4375 -0.49 0.65 #> 68 -0.5 -210.5625 -0.49 0.65 #> 69 0.5 2012.4375 0.51 -0.35 #> 70 -0.5 -210.5625 0.51 0.65 #> 71 0.5 2288.4375 0.51 0.65 #> 72 -0.5 -586.5625 -0.49 -0.35 #> 73 -0.5 -402.5625 -0.49 0.65 #> 74 0.5 752.4375 0.51 0.65 #> 75 -0.5 -210.5625 -0.49 0.65 #> 76 0.5 2012.4375 -0.49 -0.35 #> 77 0.5 988.4375 0.51 -0.35 #> 78 -0.5 -586.5625 -0.49 -0.35 #> 79 0.5 637.4375 -0.49 -0.35 #> 80 0.5 -111.5625 -0.49 0.65 #> 81 0.5 1357.4375 0.51 0.65 #> 82 -0.5 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-0.49 -0.35 #> 458 0.5 2429.4375 0.51 -0.35 #> 459 0.5 869.4375 0.51 -0.35 #> 460 -0.5 -495.5625 0.51 -0.35 #> 461 0.5 -10.5625 -0.49 -0.35 #> 462 0.5 524.4375 -0.49 0.65 #> 463 0.5 1744.4375 -0.49 -0.35 #> 464 0.5 869.4375 -0.49 -0.35 #> 465 -0.5 -307.5625 -0.49 -0.35 #> 466 0.5 2012.4375 -0.49 -0.35 #> 467 0.5 637.4375 -0.49 -0.35 #> 468 0.5 1357.4375 0.51 -0.35 #> 469 0.5 1232.4375 0.51 0.65 #> 470 0.5 2288.4375 -0.49 -0.35 #> 471 0.5 413.4375 -0.49 0.65 #> 472 0.5 524.4375 0.51 -0.35 #> 473 0.5 -10.5625 0.51 -0.35 #> 474 0.5 -10.5625 -0.49 -0.35 #> 475 -0.5 -495.5625 -0.49 0.65 #> 476 0.5 92.4375 0.51 -0.35 #> 477 0.5 2429.4375 0.51 0.65 #> 478 0.5 304.4375 0.51 -0.35 #> 479 0.5 413.4375 0.51 0.65 #> 480 -0.5 -495.5625 -0.49 -0.35 #> 481 0.5 2288.4375 -0.49 -0.35 #> 482 0.5 2012.4375 -0.49 0.65 #> 483 0.5 -111.5625 0.51 0.65 #> 484 -0.5 -586.5625 -0.49 -0.35 #> 485 0.5 2012.4375 0.51 -0.35 #> 486 0.5 524.4375 0.51 0.65 #> 487 0.5 869.4375 0.51 0.65 #> 488 0.5 -10.5625 0.51 0.65 #> 489 0.5 1109.4375 -0.49 -0.35 #> 490 0.5 988.4375 -0.49 0.65 #> 491 0.5 2012.4375 -0.49 -0.35 #> 492 0.5 1877.4375 0.51 0.65 #> 493 -0.5 -586.5625 0.51 0.65 #> 494 0.5 2429.4375 -0.49 0.65 #> 495 0.5 413.4375 0.51 0.65 #> 496 0.5 2572.4375 -0.49 -0.35 #> 497 -0.5 -307.5625 -0.49 -0.35 #> 498 0.5 2012.4375 -0.49 -0.35 #> 499 -0.5 -586.5625 -0.49 -0.35 #> 500 0.5 752.4375 -0.49 0.65 #> SMOKE_CENTERED N_PR_THER_MEDIAN_CENTERED #> 1 -0.1933333 0.5 #> 2 -0.1933333 0.5 #> 3 0.8066667 -0.5 #> 4 -0.1933333 0.5 #> 5 -0.1933333 0.5 #> 6 -0.1933333 0.5 #> 7 0.8066667 0.5 #> 8 0.8066667 -0.5 #> 9 0.8066667 -0.5 #> 10 0.8066667 0.5 #> 11 -0.1933333 0.5 #> 12 -0.1933333 -0.5 #> 13 -0.1933333 0.5 #> 14 0.8066667 -0.5 #> 15 -0.1933333 0.5 #> 16 -0.1933333 -0.5 #> 17 0.8066667 0.5 #> 18 -0.1933333 0.5 #> 19 -0.1933333 0.5 #> 20 -0.1933333 0.5 #> 21 0.8066667 0.5 #> 22 -0.1933333 0.5 #> 23 0.8066667 0.5 #> 24 0.8066667 -0.5 #> 25 -0.1933333 -0.5 #> 26 0.8066667 -0.5 #> 27 -0.1933333 0.5 #> 28 -0.1933333 -0.5 #> 29 -0.1933333 -0.5 #> 30 -0.1933333 0.5 #> 31 -0.1933333 0.5 #> 32 -0.1933333 0.5 #> 33 0.8066667 0.5 #> 34 -0.1933333 -0.5 #> 35 0.8066667 -0.5 #> 36 0.8066667 0.5 #> 37 -0.1933333 0.5 #> 38 -0.1933333 0.5 #> 39 -0.1933333 -0.5 #> 40 0.8066667 -0.5 #> 41 -0.1933333 0.5 #> 42 0.8066667 -0.5 #> 43 0.8066667 -0.5 #> 44 -0.1933333 -0.5 #> 45 -0.1933333 0.5 #> 46 -0.1933333 -0.5 #> 47 -0.1933333 0.5 #> 48 0.8066667 -0.5 #> 49 -0.1933333 -0.5 #> 50 -0.1933333 -0.5 #> 51 -0.1933333 0.5 #> 52 0.8066667 0.5 #> 53 0.8066667 -0.5 #> 54 0.8066667 0.5 #> 55 -0.1933333 0.5 #> 56 -0.1933333 0.5 #> 57 -0.1933333 0.5 #> 58 0.8066667 0.5 #> 59 -0.1933333 -0.5 #> 60 0.8066667 -0.5 #> 61 -0.1933333 -0.5 #> 62 0.8066667 0.5 #> 63 -0.1933333 -0.5 #> 64 0.8066667 -0.5 #> 65 -0.1933333 -0.5 #> 66 -0.1933333 0.5 #> 67 -0.1933333 -0.5 #> 68 0.8066667 -0.5 #> 69 -0.1933333 0.5 #> 70 -0.1933333 -0.5 #> 71 -0.1933333 -0.5 #> 72 -0.1933333 -0.5 #> 73 -0.1933333 0.5 #> 74 -0.1933333 0.5 #> 75 -0.1933333 0.5 #> 76 -0.1933333 -0.5 #> 77 -0.1933333 0.5 #> 78 0.8066667 -0.5 #> 79 -0.1933333 -0.5 #> 80 -0.1933333 -0.5 #> 81 -0.1933333 0.5 #> 82 -0.1933333 -0.5 #> 83 -0.1933333 0.5 #> 84 -0.1933333 0.5 #> 85 -0.1933333 -0.5 #> 86 0.8066667 0.5 #> 87 0.8066667 -0.5 #> 88 -0.1933333 0.5 #> 89 -0.1933333 -0.5 #> 90 -0.1933333 -0.5 #> 91 0.8066667 0.5 #> 92 -0.1933333 -0.5 #> 93 -0.1933333 -0.5 #> 94 -0.1933333 -0.5 #> 95 0.8066667 0.5 #> 96 -0.1933333 -0.5 #> 97 -0.1933333 0.5 #> 98 0.8066667 0.5 #> 99 0.8066667 0.5 #> 100 0.8066667 0.5 #> 101 -0.1933333 -0.5 #> 102 0.8066667 0.5 #> 103 -0.1933333 0.5 #> 104 0.8066667 -0.5 #> 105 0.8066667 -0.5 #> 106 0.8066667 -0.5 #> 107 0.8066667 -0.5 #> 108 -0.1933333 0.5 #> 109 -0.1933333 0.5 #> 110 0.8066667 -0.5 #> 111 -0.1933333 -0.5 #> 112 -0.1933333 0.5 #> 113 -0.1933333 0.5 #> 114 -0.1933333 -0.5 #> 115 -0.1933333 -0.5 #> 116 -0.1933333 -0.5 #> 117 -0.1933333 -0.5 #> 118 -0.1933333 0.5 #> 119 -0.1933333 -0.5 #> 120 -0.1933333 -0.5 #> 121 0.8066667 0.5 #> 122 0.8066667 -0.5 #> 123 -0.1933333 0.5 #> 124 -0.1933333 0.5 #> 125 -0.1933333 0.5 #> 126 -0.1933333 -0.5 #> 127 -0.1933333 -0.5 #> 128 -0.1933333 0.5 #> 129 0.8066667 0.5 #> 130 -0.1933333 0.5 #> 131 -0.1933333 -0.5 #> 132 -0.1933333 0.5 #> 133 -0.1933333 -0.5 #> 134 0.8066667 0.5 #> 135 -0.1933333 -0.5 #> 136 0.8066667 0.5 #> 137 0.8066667 0.5 #> 138 -0.1933333 -0.5 #> 139 -0.1933333 -0.5 #> 140 -0.1933333 0.5 #> 141 -0.1933333 0.5 #> 142 -0.1933333 -0.5 #> 143 0.8066667 0.5 #> 144 0.8066667 0.5 #> 145 -0.1933333 -0.5 #> 146 0.8066667 0.5 #> 147 -0.1933333 -0.5 #> 148 -0.1933333 0.5 #> 149 -0.1933333 0.5 #> 150 0.8066667 0.5 #> 151 0.8066667 0.5 #> 152 -0.1933333 0.5 #> 153 0.8066667 0.5 #> 154 0.8066667 0.5 #> 155 -0.1933333 0.5 #> 156 -0.1933333 0.5 #> 157 -0.1933333 0.5 #> 158 -0.1933333 -0.5 #> 159 -0.1933333 -0.5 #> 160 -0.1933333 -0.5 #> 161 0.8066667 -0.5 #> 162 -0.1933333 -0.5 #> 163 -0.1933333 0.5 #> 164 -0.1933333 0.5 #> 165 -0.1933333 -0.5 #> 166 -0.1933333 0.5 #> 167 -0.1933333 -0.5 #> 168 -0.1933333 -0.5 #> 169 -0.1933333 0.5 #> 170 -0.1933333 0.5 #> 171 -0.1933333 -0.5 #> 172 0.8066667 0.5 #> 173 -0.1933333 -0.5 #> 174 0.8066667 -0.5 #> 175 0.8066667 0.5 #> 176 0.8066667 0.5 #> 177 -0.1933333 -0.5 #> 178 0.8066667 0.5 #> 179 -0.1933333 0.5 #> 180 -0.1933333 0.5 #> 181 -0.1933333 -0.5 #> 182 -0.1933333 -0.5 #> 183 -0.1933333 -0.5 #> 184 -0.1933333 0.5 #> 185 -0.1933333 -0.5 #> 186 -0.1933333 0.5 #> 187 0.8066667 0.5 #> 188 -0.1933333 0.5 #> 189 0.8066667 0.5 #> 190 0.8066667 -0.5 #> 191 -0.1933333 0.5 #> 192 0.8066667 -0.5 #> 193 -0.1933333 0.5 #> 194 -0.1933333 0.5 #> 195 -0.1933333 0.5 #> 196 0.8066667 -0.5 #> 197 -0.1933333 0.5 #> 198 -0.1933333 -0.5 #> 199 0.8066667 -0.5 #> 200 0.8066667 -0.5 #> 201 -0.1933333 -0.5 #> 202 -0.1933333 0.5 #> 203 -0.1933333 -0.5 #> 204 0.8066667 -0.5 #> 205 0.8066667 -0.5 #> 206 -0.1933333 -0.5 #> 207 0.8066667 -0.5 #> 208 0.8066667 0.5 #> 209 0.8066667 -0.5 #> 210 0.8066667 0.5 #> 211 -0.1933333 -0.5 #> 212 0.8066667 -0.5 #> 213 -0.1933333 0.5 #> 214 0.8066667 -0.5 #> 215 -0.1933333 0.5 #> 216 0.8066667 0.5 #> 217 -0.1933333 -0.5 #> 218 0.8066667 -0.5 #> 219 -0.1933333 0.5 #> 220 -0.1933333 -0.5 #> 221 0.8066667 -0.5 #> 222 -0.1933333 0.5 #> 223 -0.1933333 -0.5 #> 224 -0.1933333 -0.5 #> 225 -0.1933333 -0.5 #> 226 -0.1933333 -0.5 #> 227 0.8066667 0.5 #> 228 -0.1933333 -0.5 #> 229 -0.1933333 -0.5 #> 230 -0.1933333 -0.5 #> 231 0.8066667 -0.5 #> 232 -0.1933333 -0.5 #> 233 -0.1933333 -0.5 #> 234 -0.1933333 -0.5 #> 235 -0.1933333 -0.5 #> 236 -0.1933333 -0.5 #> 237 0.8066667 0.5 #> 238 0.8066667 -0.5 #> 239 -0.1933333 -0.5 #> 240 0.8066667 0.5 #> 241 0.8066667 0.5 #> 242 -0.1933333 0.5 #> 243 -0.1933333 -0.5 #> 244 -0.1933333 -0.5 #> 245 -0.1933333 -0.5 #> 246 0.8066667 0.5 #> 247 -0.1933333 0.5 #> 248 0.8066667 0.5 #> 249 0.8066667 -0.5 #> 250 -0.1933333 0.5 #> 251 -0.1933333 -0.5 #> 252 -0.1933333 -0.5 #> 253 0.8066667 0.5 #> 254 0.8066667 0.5 #> 255 -0.1933333 0.5 #> 256 -0.1933333 -0.5 #> 257 -0.1933333 0.5 #> 258 0.8066667 0.5 #> 259 0.8066667 -0.5 #> 260 -0.1933333 -0.5 #> 261 0.8066667 -0.5 #> 262 -0.1933333 0.5 #> 263 -0.1933333 0.5 #> 264 -0.1933333 0.5 #> 265 0.8066667 0.5 #> 266 0.8066667 -0.5 #> 267 -0.1933333 -0.5 #> 268 0.8066667 0.5 #> 269 0.8066667 -0.5 #> 270 -0.1933333 -0.5 #> 271 0.8066667 0.5 #> 272 -0.1933333 0.5 #> 273 -0.1933333 0.5 #> 274 0.8066667 -0.5 #> 275 0.8066667 -0.5 #> 276 0.8066667 -0.5 #> 277 -0.1933333 -0.5 #> 278 0.8066667 -0.5 #> 279 0.8066667 -0.5 #> 280 -0.1933333 0.5 #> 281 -0.1933333 0.5 #> 282 -0.1933333 -0.5 #> 283 -0.1933333 -0.5 #> 284 0.8066667 0.5 #> 285 0.8066667 0.5 #> 286 -0.1933333 -0.5 #> 287 0.8066667 0.5 #> 288 -0.1933333 -0.5 #> 289 -0.1933333 0.5 #> 290 -0.1933333 0.5 #> 291 0.8066667 0.5 #> 292 0.8066667 -0.5 #> 293 -0.1933333 0.5 #> 294 -0.1933333 0.5 #> 295 0.8066667 0.5 #> 296 -0.1933333 -0.5 #> 297 0.8066667 0.5 #> 298 -0.1933333 0.5 #> 299 -0.1933333 -0.5 #> 300 -0.1933333 0.5 #> 301 -0.1933333 -0.5 #> 302 -0.1933333 -0.5 #> 303 -0.1933333 0.5 #> 304 -0.1933333 0.5 #> 305 -0.1933333 -0.5 #> 306 -0.1933333 0.5 #> 307 -0.1933333 0.5 #> 308 -0.1933333 -0.5 #> 309 -0.1933333 -0.5 #> 310 -0.1933333 0.5 #> 311 -0.1933333 0.5 #> 312 -0.1933333 -0.5 #> 313 -0.1933333 0.5 #> 314 -0.1933333 -0.5 #> 315 -0.1933333 -0.5 #> 316 -0.1933333 0.5 #> 317 -0.1933333 0.5 #> 318 0.8066667 -0.5 #> 319 -0.1933333 -0.5 #> 320 -0.1933333 -0.5 #> 321 -0.1933333 -0.5 #> 322 -0.1933333 -0.5 #> 323 0.8066667 0.5 #> 324 -0.1933333 0.5 #> 325 -0.1933333 -0.5 #> 326 -0.1933333 -0.5 #> 327 -0.1933333 0.5 #> 328 -0.1933333 -0.5 #> 329 -0.1933333 -0.5 #> 330 0.8066667 0.5 #> 331 -0.1933333 0.5 #> 332 -0.1933333 -0.5 #> 333 -0.1933333 -0.5 #> 334 0.8066667 -0.5 #> 335 0.8066667 0.5 #> 336 -0.1933333 0.5 #> 337 -0.1933333 0.5 #> 338 0.8066667 0.5 #> 339 -0.1933333 -0.5 #> 340 -0.1933333 0.5 #> 341 -0.1933333 -0.5 #> 342 -0.1933333 -0.5 #> 343 -0.1933333 -0.5 #> 344 -0.1933333 -0.5 #> 345 -0.1933333 -0.5 #> 346 -0.1933333 0.5 #> 347 -0.1933333 0.5 #> 348 -0.1933333 -0.5 #> 349 0.8066667 -0.5 #> 350 -0.1933333 0.5 #> 351 -0.1933333 0.5 #> 352 -0.1933333 0.5 #> 353 -0.1933333 0.5 #> 354 -0.1933333 0.5 #> 355 -0.1933333 -0.5 #> 356 -0.1933333 -0.5 #> 357 0.8066667 -0.5 #> 358 0.8066667 -0.5 #> 359 0.8066667 -0.5 #> 360 -0.1933333 -0.5 #> 361 -0.1933333 0.5 #> 362 -0.1933333 0.5 #> 363 -0.1933333 -0.5 #> 364 -0.1933333 0.5 #> 365 -0.1933333 -0.5 #> 366 -0.1933333 0.5 #> 367 -0.1933333 -0.5 #> 368 -0.1933333 0.5 #> 369 -0.1933333 0.5 #> 370 -0.1933333 0.5 #> 371 -0.1933333 -0.5 #> 372 -0.1933333 0.5 #> 373 -0.1933333 0.5 #> 374 0.8066667 0.5 #> 375 -0.1933333 0.5 #> 376 -0.1933333 0.5 #> 377 -0.1933333 0.5 #> 378 0.8066667 0.5 #> 379 -0.1933333 -0.5 #> 380 -0.1933333 -0.5 #> 381 0.8066667 -0.5 #> 382 -0.1933333 -0.5 #> 383 -0.1933333 0.5 #> 384 0.8066667 0.5 #> 385 -0.1933333 0.5 #> 386 -0.1933333 -0.5 #> 387 0.8066667 -0.5 #> 388 0.8066667 0.5 #> 389 -0.1933333 -0.5 #> 390 -0.1933333 -0.5 #> 391 -0.1933333 -0.5 #> 392 -0.1933333 0.5 #> 393 0.8066667 0.5 #> 394 -0.1933333 0.5 #> 395 -0.1933333 -0.5 #> 396 -0.1933333 0.5 #> 397 -0.1933333 0.5 #> 398 -0.1933333 -0.5 #> 399 -0.1933333 0.5 #> 400 0.8066667 0.5 #> 401 -0.1933333 -0.5 #> 402 0.8066667 0.5 #> 403 -0.1933333 0.5 #> 404 -0.1933333 0.5 #> 405 -0.1933333 0.5 #> 406 -0.1933333 0.5 #> 407 -0.1933333 0.5 #> 408 0.8066667 -0.5 #> 409 0.8066667 -0.5 #> 410 -0.1933333 0.5 #> 411 -0.1933333 0.5 #> 412 -0.1933333 -0.5 #> 413 0.8066667 0.5 #> 414 -0.1933333 -0.5 #> 415 -0.1933333 -0.5 #> 416 -0.1933333 0.5 #> 417 0.8066667 0.5 #> 418 -0.1933333 -0.5 #> 419 0.8066667 0.5 #> 420 0.8066667 -0.5 #> 421 -0.1933333 -0.5 #> 422 -0.1933333 0.5 #> 423 0.8066667 -0.5 #> 424 0.8066667 0.5 #> 425 -0.1933333 -0.5 #> 426 -0.1933333 -0.5 #> 427 0.8066667 -0.5 #> 428 0.8066667 -0.5 #> 429 -0.1933333 0.5 #> 430 0.8066667 0.5 #> 431 -0.1933333 -0.5 #> 432 0.8066667 -0.5 #> 433 0.8066667 -0.5 #> 434 -0.1933333 -0.5 #> 435 -0.1933333 -0.5 #> 436 -0.1933333 -0.5 #> 437 -0.1933333 -0.5 #> 438 -0.1933333 -0.5 #> 439 -0.1933333 -0.5 #> 440 -0.1933333 -0.5 #> 441 -0.1933333 0.5 #> 442 -0.1933333 0.5 #> 443 -0.1933333 0.5 #> 444 0.8066667 0.5 #> 445 -0.1933333 0.5 #> 446 -0.1933333 0.5 #> 447 0.8066667 0.5 #> 448 -0.1933333 0.5 #> 449 -0.1933333 0.5 #> 450 -0.1933333 0.5 #> 451 -0.1933333 -0.5 #> 452 0.8066667 -0.5 #> 453 0.8066667 0.5 #> 454 -0.1933333 0.5 #> 455 -0.1933333 0.5 #> 456 -0.1933333 0.5 #> 457 -0.1933333 0.5 #> 458 -0.1933333 0.5 #> 459 0.8066667 0.5 #> 460 0.8066667 0.5 #> 461 -0.1933333 0.5 #> 462 -0.1933333 0.5 #> 463 -0.1933333 -0.5 #> 464 0.8066667 -0.5 #> 465 0.8066667 -0.5 #> 466 -0.1933333 0.5 #> 467 -0.1933333 0.5 #> 468 -0.1933333 0.5 #> 469 -0.1933333 0.5 #> 470 -0.1933333 0.5 #> 471 -0.1933333 -0.5 #> 472 0.8066667 0.5 #> 473 0.8066667 -0.5 #> 474 -0.1933333 0.5 #> 475 0.8066667 0.5 #> 476 -0.1933333 0.5 #> 477 -0.1933333 0.5 #> 478 0.8066667 0.5 #> 479 -0.1933333 -0.5 #> 480 -0.1933333 0.5 #> 481 -0.1933333 -0.5 #> 482 -0.1933333 0.5 #> 483 -0.1933333 -0.5 #> 484 -0.1933333 -0.5 #> 485 -0.1933333 -0.5 #> 486 -0.1933333 -0.5 #> 487 -0.1933333 0.5 #> 488 0.8066667 -0.5 #> 489 0.8066667 0.5 #> 490 -0.1933333 0.5 #> 491 0.8066667 -0.5 #> 492 0.8066667 0.5 #> 493 0.8066667 -0.5 #> 494 -0.1933333 -0.5 #> 495 -0.1933333 0.5 #> 496 -0.1933333 0.5 #> 497 0.8066667 0.5 #> 498 0.8066667 -0.5 #> 499 -0.1933333 -0.5 #> 500 0.8066667 0.5 centered_colnames <- grep(\"_CENTERED$\", colnames(centered_ipd_sat), value = TRUE) weighted_data <- estimate_weights(data = centered_ipd_sat, centered_colnames = centered_colnames) weighted_data2 <- estimate_weights( data = centered_ipd_sat, centered_colnames = centered_colnames, n_boot_iteration = 500 ) # get dummy binary IPD pseudo_adrs <- get_pseudo_ipd_binary( binary_agd = data.frame( ARM = rep(\"B\", 2), RESPONSE = c(\"YES\", \"NO\"), COUNT = c(280, 120) ), format = \"stacked\" ) # unanchored binary MAIC, with CI based on sandwich estimator maic_unanchored( weights_object = weighted_data, ipd = adrs_sat, pseudo_ipd = pseudo_adrs, trt_ipd = \"A\", trt_agd = \"B\", trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_type = \"binary\", endpoint_name = \"Binary Endpoint\", eff_measure = \"RR\", # binary specific args binary_robust_cov_type = \"HC3\" ) #> Waiting for profiling to be done... #> $descriptive #> list() #> #> $inferential #> $inferential$model_before #> #> Call: glm(formula = RESPONSE ~ ARM, family = glm_link, data = dat) #> #> Coefficients: #> (Intercept) ARMA #> -0.3567 0.1082 #> #> Degrees of Freedom: 899 Total (i.e. Null); 898 Residual #> Null Deviance:\t 395.5 #> Residual Deviance: 393.5 \tAIC: 1738 #> #> $inferential$model_after #> #> Call: glm(formula = RESPONSE ~ ARM, family = glm_link, data = dat, #> weights = weights) #> #> Coefficients: #> (Intercept) ARMA #> -0.35667 0.05611 #> #> Degrees of Freedom: 899 Total (i.e. Null); 898 Residual #> Null Deviance:\t 277.2 #> Residual Deviance: 276.9 \tAIC: 1098 #> #> $inferential$report_overall_robustCI #> Matching treatment N n.events(%) RR[95% CI] #> A Before matching/Binary Endpoint A 500 390(78.0) 1.11[0.96;1.30] #> B B 400 280(70.0) #> A1 After matching/Binary Endpoint A 500 128.3(25.7) 1.06[0.94;1.19] #> B1 B 400 280(70.0) #> p-Value #> A 0.167 #> B #> A1 0.367 #> B1 #> #> # unanchored binary MAIC, with bootstrapped CI maic_unanchored( weights_object = weighted_data2, ipd = adrs_sat, pseudo_ipd = pseudo_adrs, trt_ipd = \"A\", trt_agd = \"B\", trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_type = \"binary\", endpoint_name = \"Binary Endpoint\", eff_measure = \"RR\", # binary specific args binary_robust_cov_type = \"HC3\" ) #> Waiting for profiling to be done... #> Waiting for profiling to be done... #> $descriptive #> list() #> #> $inferential #> $inferential$model_before #> #> Call: glm(formula = RESPONSE ~ ARM, family = glm_link, data = dat) #> #> Coefficients: #> (Intercept) ARMA #> -0.3567 0.1082 #> #> Degrees of Freedom: 899 Total (i.e. Null); 898 Residual #> Null Deviance:\t 395.5 #> Residual Deviance: 393.5 \tAIC: 1738 #> #> $inferential$model_after #> #> Call: glm(formula = RESPONSE ~ ARM, family = glm_link, data = dat, #> weights = weights) #> #> Coefficients: #> (Intercept) ARMA #> -0.35667 0.05611 #> #> Degrees of Freedom: 899 Total (i.e. Null); 898 Residual #> Null Deviance:\t 277.2 #> Residual Deviance: 276.9 \tAIC: 1098 #> #> $inferential$boot_est #> #> ORDINARY NONPARAMETRIC BOOTSTRAP #> #> #> Call: #> boot(data = boot_ipd, statistic = stat_fun, R = R, w_obj = weights_object, #> pseudo_ipd = pseudo_ipd) #> #> #> Bootstrap Statistics : #> original bias std. error #> t1* 0.05611408 0.0022793426 0.0513740040 #> t2* 0.01136433 0.0002412849 0.0008190764 #> #> $inferential$report_overall_robustCI #> Matching treatment N n.events(%) RR[95% CI] #> A Before matching/Binary Endpoint A 500 390(78.0) 1.11[0.96;1.30] #> B B 400 280(70.0) #> A1 After matching/Binary Endpoint A 500 128.3(25.7) 1.06[0.94;1.19] #> B1 B 400 280(70.0) #> p-Value #> A 0.167 #> B #> A1 0.367 #> B1 #> #> $inferential$report_overall_bootCI #> Matching treatment N n.events(%) RR[95% CI] #> A Before matching/Binary Endpoint A 500 390(78.0) 1.11[0.96;1.30] #> B B 400 280(70.0) #> A1 After matching/Binary Endpoint A 500 128.3(25.7) 1.06[0.95;1.17] #> B1 B 400 280(70.0) #> p-Value #> A 0.167 #> B #> A1 NA #> B1 #> #>"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maicplus-package.html","id":null,"dir":"Reference","previous_headings":"","what":"maicplus: Matching Adjusted Indirect Comparison — maicplus-package","title":"maicplus: Matching Adjusted Indirect Comparison — maicplus-package","text":"maicplus package facilitates performing matching adjusted indirect comparison (MAIC) analysis endpoint interest either time--event (e.g. overall survival) binary (e.g. objective tumor response).","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maicplus-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"maicplus: Matching Adjusted Indirect Comparison — maicplus-package","text":"Maintainer: hta-pharma hta-pharma@example.com","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/medSurv_makeup.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","title":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","text":"Extract display median survival time confidence interval","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/medSurv_makeup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","text":"","code":"medSurv_makeup(km_fit, legend = \"before matching\", time_scale)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/medSurv_makeup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","text":"km_fit returned object survival::survfit legend character string, name used 'type' column returned data frame time_scale character string, 'years', 'months', 'weeks' 'days', time unit median survival time","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/medSurv_makeup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","text":"data frame index column 'type', median survival time confidence interval","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/medSurv_makeup.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","text":"","code":"data(adtte_sat) data(pseudo_ipd_sat) library(survival) combined_data <- rbind(adtte_sat[, c(\"TIME\", \"EVENT\", \"ARM\")], pseudo_ipd_sat) kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data, conf.type = \"log-log\") # Derive median survival time medSurv <- medSurv_makeup(kmobj, legend = \"before matching\", time_scale = \"day\") medSurv #> treatment type records n.max n.start events rmean se(rmean) #> 1 ARM=A before matching 500 500 500 190 265.1012 10.80981 #> 2 ARM=B before matching 300 300 300 178 130.9893 10.24910 #> median 0.95LCL 0.95UCL #> 1 230.94839 191.10767 313.1574 #> 2 83.58535 68.82298 101.0786"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnosis plot of proportional hazard assumption for anchored and unanchored — ph_diagplot","title":"Diagnosis plot of proportional hazard assumption for anchored and unanchored — ph_diagplot","text":"Diagnosis plot proportional hazard assumption anchored unanchored","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnosis plot of proportional hazard assumption for anchored and unanchored — ph_diagplot","text":"","code":"ph_diagplot( weights_object, tte_ipd, tte_pseudo_ipd, trt_ipd, trt_agd, trt_common = NULL, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", endpoint_name = \"Time to Event Endpoint\", time_scale, zph_transform = \"log\", zph_log_hazard = TRUE )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnosis plot of proportional hazard assumption for anchored and unanchored — ph_diagplot","text":"weights_object object returned estimate_weight tte_ipd data frame individual patient data (IPD) internal trial, contain least \"USUBJID\", \"EVENT\", \"TIME\" columns column indicating treatment assignment tte_pseudo_ipd data frame pseudo IPD digitized KM curves external trial (time--event endpoint), contain least \"EVENT\", \"TIME\" trt_ipd string, name interested investigation arm internal trial tte_ipd (real IPD) trt_agd string, name interested investigation arm external trial tte_pseudo_ipd (pseudo IPD) trt_common string, name common comparator internal external trial, default NULL, indicating unanchored case trt_var_ipd string, column name tte_ipd contains treatment assignment trt_var_agd string, column name tte_pseudo_ipd contains treatment assignment endpoint_name string, name time event endpoint, show last line title time_scale string, time unit median survival time, taking value 'years', 'months', 'weeks' 'days' zph_transform string, pass survival::cox.zph, default \"log\" zph_log_hazard logical, TRUE (default), y axis time dependent hazard function log-hazard, otherwise, hazard.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnosis plot of proportional hazard assumption for anchored and unanchored — ph_diagplot","text":"3 2 plot, include log-cumulative hazard plot, time dependent hazard function unscaled Schoenfeld residual plot, matching","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diagnosis plot of proportional hazard assumption for anchored and unanchored — ph_diagplot","text":"","code":"# unanchored example using ph_diagplot data(weighted_sat) data(adtte_sat) data(pseudo_ipd_sat) ph_diagplot( weights_object = weighted_sat, tte_ipd = adtte_sat, tte_pseudo_ipd = pseudo_ipd_sat, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", trt_ipd = \"A\", trt_agd = \"B\", trt_common = NULL, endpoint_name = \"Overall Survival\", time_scale = \"week\", zph_transform = \"log\", zph_log_hazard = TRUE ) # anchored example using ph_diagplot data(weighted_twt) data(adtte_twt) data(pseudo_ipd_twt) ph_diagplot( weights_object = weighted_twt, tte_ipd = adtte_twt, tte_pseudo_ipd = pseudo_ipd_twt, trt_var_ipd = \"ARM\", trt_var_agd = \"ARM\", trt_ipd = \"A\", trt_agd = \"B\", trt_common = \"C\", endpoint_name = \"Overall Survival\", time_scale = \"week\", zph_transform = \"log\", zph_log_hazard = TRUE )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot_lch.html","id":null,"dir":"Reference","previous_headings":"","what":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"plot also known log negative log survival rate.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot_lch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"","code":"ph_diagplot_lch( km_fit, time_scale, log_time = TRUE, endpoint_name = \"\", subtitle = \"\", exclude_censor = TRUE )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot_lch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"km_fit returned object survival::survfit time_scale character string, 'years', 'months', 'weeks' 'days', time unit median survival time log_time logical, TRUE (default) FALSE endpoint_name character string, name endpoint subtitle character string, subtitle plot exclude_censor logical, censored data point plotted","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot_lch.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"plot log cumulative hazard rate","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot_lch.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"diagnosis plot proportional hazard assumption, versus log-time (default) time","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot_lch.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"","code":"library(survival) data(adtte_sat) data(pseudo_ipd_sat) combined_data <- rbind(adtte_sat[, c(\"TIME\", \"EVENT\", \"ARM\")], pseudo_ipd_sat) kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data, conf.type = \"log-log\") ph_diagplot_lch(kmobj, time_scale = \"month\", log_time = TRUE, endpoint_name = \"OS\", subtitle = \"(Before Matching)\" )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot_schoenfeld.html","id":null,"dir":"Reference","previous_headings":"","what":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","title":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","text":"PH Diagnosis Plot Schoenfeld residuals Cox model fit","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot_schoenfeld.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","text":"","code":"ph_diagplot_schoenfeld( coxobj, time_scale = \"months\", log_time = TRUE, endpoint_name = \"\", subtitle = \"\" )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot_schoenfeld.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","text":"coxobj object returned coxph time_scale character string, 'years', 'months', 'weeks' 'days', time unit median survival time log_time logical, TRUE (default) FALSE endpoint_name character string, name endpoint subtitle character string, subtitle plot","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot_schoenfeld.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","text":"plot Schoenfeld residuals","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot_schoenfeld.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","text":"","code":"library(survival) data(adtte_sat) data(pseudo_ipd_sat) combined_data <- rbind(adtte_sat[, c(\"TIME\", \"EVENT\", \"ARM\")], pseudo_ipd_sat) unweighted_cox <- coxph(Surv(TIME, EVENT == 1) ~ ARM, data = combined_data) ph_diagplot_schoenfeld(unweighted_cox, time_scale = \"month\", log_time = TRUE, endpoint_name = \"OS\", subtitle = \"(Before Matching)\" )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/plot_weights_base.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot MAIC weights in a histogram with key statistics in legend — plot_weights_base","title":"Plot MAIC weights in a histogram with key statistics in legend — plot_weights_base","text":"Generates base R histogram weights. Default plot either unscaled scaled weights .","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/plot_weights_base.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot MAIC weights in a histogram with key statistics in legend — plot_weights_base","text":"","code":"plot_weights_base( weighted_data, bin_col, vline_col, main_title, scaled_weights )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/plot_weights_base.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot MAIC weights in a histogram with key statistics in legend — plot_weights_base","text":"weighted_data object returned calculating weights using estimate_weights bin_col string, color bins histogram vline_col string, color vertical line histogram main_title title plot scaled_weights indicator using scaled weights instead regular weights","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/plot_weights_base.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot MAIC weights in a histogram with key statistics in legend — plot_weights_base","text":"plot unscaled scaled weights","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/plot_weights_ggplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot MAIC weights in a histogram with key statistics in legend using ggplot2 — plot_weights_ggplot","title":"Plot MAIC weights in a histogram with key statistics in legend using ggplot2 — plot_weights_ggplot","text":"Generates ggplot histogram weights. Default plot unscaled scaled weights graph.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/plot_weights_ggplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot MAIC weights in a histogram with key statistics in legend using ggplot2 — plot_weights_ggplot","text":"","code":"plot_weights_ggplot(weighted_data, bin_col, vline_col, main_title, bins)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/plot_weights_ggplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot MAIC weights in a histogram with key statistics in legend using ggplot2 — plot_weights_ggplot","text":"weighted_data object returned calculating weights using estimate_weights bin_col string, color bins histogram vline_col string, color vertical line histogram main_title Name scaled weights plot unscaled weights plot, respectively. bins number bin parameter use","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/plot_weights_ggplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot MAIC weights in a histogram with key statistics in legend using ggplot2 — plot_weights_ggplot","text":"plot unscaled scaled weights","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/process_agd.html","id":null,"dir":"Reference","previous_headings":"","what":"Pre-process aggregate data — process_agd","title":"Pre-process aggregate data — process_agd","text":"function checks format aggregate data. Data required three columns: STUDY, ARM, N. Column names legal suffixes (MEAN, MEDIAN, SD, COUNT, PROP) dropped. variable count variable, converted proportions dividing sample size (N). Note, count specified, proportion always calculated based count, , specified proportion ignored applicable. aggregated data comes multiple sources (.e. different analysis population) sample size differs variable, one option specify proportion directly instead count using suffix _PROP.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/process_agd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pre-process aggregate data — process_agd","text":"","code":"process_agd(raw_agd)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/process_agd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pre-process aggregate data — process_agd","text":"raw_agd raw aggregate data contain STUDY, ARM, N. Variable names followed legal suffixes (.e. MEAN, MEDIAN, SD, COUNT, PROP).","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/process_agd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pre-process aggregate data — process_agd","text":"pre-processed aggregate level data","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/process_agd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pre-process aggregate data — process_agd","text":"","code":"data(agd) agd <- process_agd(agd)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/pseudo_ipd_sat.html","id":null,"dir":"Reference","previous_headings":"","what":"Pseudo individual patient survival data from published study — pseudo_ipd_sat","title":"Pseudo individual patient survival data from published study — pseudo_ipd_sat","text":"Pseudo individual patient survival data published study","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/pseudo_ipd_sat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pseudo individual patient survival data from published study — pseudo_ipd_sat","text":"","code":"pseudo_ipd_sat"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/pseudo_ipd_sat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pseudo individual patient survival data from published study — pseudo_ipd_sat","text":"data frame 300 rows 3 columns: TIME Survival time days. EVENT Event indicator 0/1. ARM Assigned treatment arm, \"B\".","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/pseudo_ipd_twt.html","id":null,"dir":"Reference","previous_headings":"","what":"Pseudo individual patient survival data from published two arm study — pseudo_ipd_twt","title":"Pseudo individual patient survival data from published two arm study — pseudo_ipd_twt","text":"Pseudo individual patient survival data published two arm study","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/pseudo_ipd_twt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pseudo individual patient survival data from published two arm study — pseudo_ipd_twt","text":"","code":"pseudo_ipd_twt"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/pseudo_ipd_twt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pseudo individual patient survival data from published two arm study — pseudo_ipd_twt","text":"data frame 800 rows 3 columns: TIME Survival time days. EVENT Event indicator 0/1. ARM Assigned treatment arm, \"B\", \"C\".","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/reformat.html","id":null,"dir":"Reference","previous_headings":"","what":"Reformat maicplus_bucher alike object — reformat","title":"Reformat maicplus_bucher alike object — reformat","text":"Reformat maicplus_bucher alike object","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/reformat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reformat maicplus_bucher alike object — reformat","text":"","code":"reformat( x, ci_digits = 2, pval_digits = 3, show_pval = TRUE, exponentiate = FALSE )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/reformat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reformat maicplus_bucher alike object — reformat","text":"x list, structured like maicplus_bucher object ci_digits integer, number decimal places point estimate derived confidence limits pval_digits integer, number decimal places display Z-test p-value show_pval logical value, default TRUE. FALSE, p-value output second element character vector exponentiate whether treatment effect confidence interval exponentiated. applies relative treatment effects. Default set false.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/report_table_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"helper function: sort out a nice report table to summarize binary analysis results — report_table_binary","title":"helper function: sort out a nice report table to summarize binary analysis results — report_table_binary","text":"helper function: sort nice report table summarize binary analysis results","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/report_table_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"helper function: sort out a nice report table to summarize binary analysis results — report_table_binary","text":"","code":"report_table_binary( binobj, weighted_result = NULL, eff_measure = c(\"OR\", \"RD\", \"RR\"), tag = NULL )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/report_table_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"helper function: sort out a nice report table to summarize binary analysis results — report_table_binary","text":"binobj object glm() weighted_result weighted result object eff_measure string, binary effect measure, \"\", \"RR\", \"RD\" tag string, default NULL, specified, extra 1st column created output","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/report_table_binary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"helper function: sort out a nice report table to summarize binary analysis results — report_table_binary","text":"data frame sample size, incidence rate, estimate binary effect measure 95% CI Wald test hazard ratio","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/report_table_binary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"helper function: sort out a nice report table to summarize binary analysis results — report_table_binary","text":"","code":"data(adrs_sat) testdat <- data.frame(Yes = 280, No = 120) rownames(testdat) <- \"B\" pseudo_ipd_binary_sat <- get_pseudo_ipd_binary( binary_agd = testdat, format = \"unstacked\" ) combined_data <- rbind(adrs_sat[, c(\"USUBJID\", \"RESPONSE\", \"ARM\")], pseudo_ipd_binary_sat) combined_data$ARM <- as.factor(combined_data$ARM) binobj_dat <- glm(RESPONSE ~ ARM, combined_data, family = binomial(link = \"logit\")) report_table_binary(binobj_dat, eff_measure = \"OR\") #> Waiting for profiling to be done... #> treatment N n.events(%) OR[95% CI] p-Value #> B B 400 280(70.0) 0.66[0.49;0.89] 0.006 #> A A 500 390(78.0)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/report_table_tte.html","id":null,"dir":"Reference","previous_headings":"","what":"helper function: sort out a nice report table to summarize survival analysis results — report_table_tte","title":"helper function: sort out a nice report table to summarize survival analysis results — report_table_tte","text":"helper function: sort nice report table summarize survival analysis results","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/report_table_tte.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"helper function: sort out a nice report table to summarize survival analysis results — report_table_tte","text":"","code":"report_table_tte(coxobj, medSurvobj, tag = NULL)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/report_table_tte.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"helper function: sort out a nice report table to summarize survival analysis results — report_table_tte","text":"coxobj returned object coxph medSurvobj returned object medSurv_makeup tag string, default NULL, specified, extra 1st column created output","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/report_table_tte.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"helper function: sort out a nice report table to summarize survival analysis results — report_table_tte","text":"data frame sample size, incidence rate, median survival time 95% CI, hazard ratio estimate 95% CI Wald test hazard ratio","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/report_table_tte.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"helper function: sort out a nice report table to summarize survival analysis results — report_table_tte","text":"","code":"library(survival) data(adtte_sat) data(pseudo_ipd_sat) combined_data <- rbind(adtte_sat[, c(\"TIME\", \"EVENT\", \"ARM\")], pseudo_ipd_sat) unweighted_cox <- coxph(Surv(TIME, EVENT == 1) ~ ARM, data = combined_data) # Derive median survival time kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data, conf.type = \"log-log\") medSurv <- medSurv_makeup(kmobj, legend = \"before matching\", time_scale = \"day\") report_table_tte(unweighted_cox, medSurv) #> treatment N n.events(%) median[95% CI] HR[95% CI] p-Value #> 2 ARM=B 300 178(59.3) 83.6[ 68.8;101.1] 2.67[2.16;3.29] <0.001 #> 1 ARM=A 500 190(38.0) 230.9[191.1;313.2]"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/survfit_makeup.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","title":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","text":"Helper function select set variables used Kaplan-Meier plot","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/survfit_makeup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","text":"","code":"survfit_makeup(km_fit, single_trt_name = \"treatment\")"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/survfit_makeup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","text":"km_fit returned object survival::survfit single_trt_name name treatment strata specified km_fit","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/survfit_makeup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","text":"list data frames variables survival::survfit(). Data frame divided treatment.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/survfit_makeup.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","text":"","code":"library(survival) data(adtte_sat) data(pseudo_ipd_sat) combined_data <- rbind(adtte_sat[, c(\"TIME\", \"EVENT\", \"ARM\")], pseudo_ipd_sat) kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data, conf.type = \"log-log\") survfit_makeup(kmobj) #> $A #> time treatment n.risk n.event censor surv lower upper #> 1 0.2046028 A 500 0 1 1.0000000 NA NA #> 2 0.3321626 A 499 1 0 0.9979960 0.9858600 0.9997175 #> 3 0.4303082 A 498 1 0 0.9959920 0.9840702 0.9989961 #> 4 1.2688996 A 497 1 0 0.9939880 0.9814767 0.9980570 #> 5 1.2808132 A 496 0 1 0.9939880 0.9814767 0.9980570 #> 6 1.9459751 A 495 1 0 0.9919799 0.9787735 0.9969824 #> 7 2.0300958 A 494 0 1 0.9919799 0.9787735 0.9969824 #> 8 2.4121713 A 493 0 1 0.9919799 0.9787735 0.9969824 #> 9 2.6752029 A 492 1 0 0.9899637 0.9760567 0.9958104 #> 10 2.9969434 A 491 0 1 0.9899637 0.9760567 0.9958104 #> 11 3.2894058 A 490 0 1 0.9899637 0.9760567 0.9958104 #> 12 3.3942755 A 489 1 0 0.9879392 0.9733519 0.9945636 #> 13 3.8751813 A 488 0 1 0.9879392 0.9733519 0.9945636 #> 14 4.0793628 A 487 0 1 0.9879392 0.9733519 0.9945636 #> 15 4.2866692 A 486 0 1 0.9879392 0.9733519 0.9945636 #> 16 4.6245131 A 485 0 1 0.9879392 0.9733519 0.9945636 #> 17 5.1546502 A 484 1 0 0.9858980 0.9706473 0.9932524 #> 18 5.4278376 A 483 0 1 0.9858980 0.9706473 0.9932524 #> 19 5.5735189 A 482 0 1 0.9858980 0.9706473 0.9932524 #> 20 5.8283381 A 481 1 0 0.9838484 0.9679619 0.9918902 #> 21 6.5182421 A 480 0 1 0.9838484 0.9679619 0.9918902 #> 22 6.8416124 A 479 1 0 0.9817944 0.9653018 0.9904863 #> 23 6.8633253 A 478 0 1 0.9817944 0.9653018 0.9904863 #> 24 7.2337488 A 477 1 0 0.9797361 0.9626636 0.9890463 #> 25 7.4378994 A 476 1 0 0.9776779 0.9600523 0.9875768 #> 26 8.3675931 A 475 1 0 0.9756196 0.9574650 0.9860816 #> 27 8.3998372 A 474 0 1 0.9756196 0.9574650 0.9860816 #> 28 8.6078129 A 473 1 0 0.9735570 0.9548917 0.9845610 #> 29 8.7321192 A 472 0 1 0.9735570 0.9548917 0.9845610 #> 30 8.7683064 A 471 0 1 0.9735570 0.9548917 0.9845610 #> 31 9.1691822 A 470 0 1 0.9735570 0.9548917 0.9845610 #> 32 9.3282041 A 469 0 1 0.9735570 0.9548917 0.9845610 #> 33 10.2292442 A 468 0 1 0.9735570 0.9548917 0.9845610 #> 34 10.2380585 A 467 1 0 0.9714723 0.9523009 0.9830067 #> 35 10.2701408 A 466 0 1 0.9714723 0.9523009 0.9830067 #> 36 10.3618326 A 465 0 1 0.9714723 0.9523009 0.9830067 #> 37 10.3717562 A 464 1 0 0.9693786 0.9497145 0.9814279 #> 38 10.9352310 A 463 0 1 0.9693786 0.9497145 0.9814279 #> 39 11.1663130 A 462 1 0 0.9672803 0.9471387 0.9798289 #> 40 11.3527364 A 461 1 0 0.9651821 0.9445796 0.9782137 #> 41 12.0051551 A 460 1 0 0.9630839 0.9420357 0.9765837 #> 42 12.1850070 A 459 0 1 0.9630839 0.9420357 0.9765837 #> 43 12.3885075 A 458 0 1 0.9630839 0.9420357 0.9765837 #> 44 12.6075825 A 457 0 1 0.9630839 0.9420357 0.9765837 #> 45 12.6413833 A 456 1 0 0.9609719 0.9394843 0.9749312 #> 46 12.7575511 A 455 1 0 0.9588598 0.9369464 0.9732659 #> 47 12.8976681 A 454 1 0 0.9567478 0.9344210 0.9715885 #> 48 12.9398765 A 453 0 1 0.9567478 0.9344210 0.9715885 #> 49 13.0073322 A 452 0 1 0.9567478 0.9344210 0.9715885 #> 50 13.1747627 A 451 0 1 0.9567478 0.9344210 0.9715885 #> 51 13.2112969 A 450 0 1 0.9567478 0.9344210 0.9715885 #> 52 13.4000154 A 449 1 0 0.9546170 0.9318787 0.9698877 #> 53 13.7869996 A 448 0 1 0.9546170 0.9318787 0.9698877 #> 54 13.8523925 A 447 0 1 0.9546170 0.9318787 0.9698877 #> 55 13.8586577 A 446 0 1 0.9546170 0.9318787 0.9698877 #> 56 14.1516358 A 445 1 0 0.9524718 0.9293264 0.9681666 #> 57 14.6109023 A 444 1 0 0.9503266 0.9267852 0.9664352 #> 58 15.2886098 A 443 0 1 0.9503266 0.9267852 0.9664352 #> 59 15.7821636 A 442 1 0 0.9481765 0.9242474 0.9646908 #> 60 15.8151577 A 441 0 1 0.9481765 0.9242474 0.9646908 #> 61 15.8302898 A 440 1 0 0.9460216 0.9217123 0.9629339 #> 62 15.8860250 A 439 0 1 0.9460216 0.9217123 0.9629339 #> 63 15.9789179 A 438 0 1 0.9460216 0.9217123 0.9629339 #> 64 16.0028888 A 437 0 1 0.9460216 0.9217123 0.9629339 #> 65 16.1304526 A 436 0 1 0.9460216 0.9217123 0.9629339 #> 66 16.6777043 A 435 1 0 0.9438468 0.9191578 0.9611548 #> 67 17.0752816 A 434 1 0 0.9416720 0.9166127 0.9593672 #> 68 17.1922456 A 433 0 1 0.9416720 0.9166127 0.9593672 #> 69 18.0631542 A 432 0 1 0.9416720 0.9166127 0.9593672 #> 70 18.4767328 A 431 0 1 0.9416720 0.9166127 0.9593672 #> 71 18.6336849 A 430 0 1 0.9416720 0.9166127 0.9593672 #> 72 19.5843537 A 429 0 1 0.9416720 0.9166127 0.9593672 #> 73 19.6747055 A 428 1 0 0.9394719 0.9140400 0.9575539 #> 74 19.9823259 A 427 0 1 0.9394719 0.9140400 0.9575539 #> 75 20.0770245 A 426 1 0 0.9372665 0.9114688 0.9557291 #> 76 20.4779808 A 425 0 1 0.9372665 0.9114688 0.9557291 #> 77 20.9126041 A 424 1 0 0.9350560 0.9088988 0.9538931 #> 78 20.9492488 A 423 0 1 0.9350560 0.9088988 0.9538931 #> 79 20.9775017 A 422 1 0 0.9328402 0.9063296 0.9520460 #> 80 20.9988640 A 421 0 1 0.9328402 0.9063296 0.9520460 #> 81 21.5799492 A 420 1 0 0.9306192 0.9037610 0.9501881 #> 82 21.8873529 A 419 0 1 0.9306192 0.9037610 0.9501881 #> 83 22.6567004 A 418 1 0 0.9283928 0.9011926 0.9483197 #> 84 22.8324762 A 417 1 0 0.9261665 0.8986315 0.9464447 #> 85 23.2247571 A 416 0 1 0.9261665 0.8986315 0.9464447 #> 86 23.2462453 A 415 1 0 0.9239347 0.8960701 0.9445595 #> 87 23.7400288 A 414 0 1 0.9239347 0.8960701 0.9445595 #> 88 25.3781343 A 413 0 1 0.9239347 0.8960701 0.9445595 #> 89 25.5091635 A 412 1 0 0.9216922 0.8935005 0.9426605 #> 90 25.5794699 A 411 1 0 0.9194496 0.8909376 0.9407555 #> 91 25.6490953 A 410 0 1 0.9194496 0.8909376 0.9407555 #> 92 26.3512119 A 409 0 1 0.9194496 0.8909376 0.9407555 #> 93 27.2088851 A 408 0 1 0.9194496 0.8909376 0.9407555 #> 94 28.0451023 A 407 1 0 0.9171905 0.8883585 0.9388328 #> 95 28.2886213 A 406 0 1 0.9171905 0.8883585 0.9388328 #> 96 28.5374060 A 405 0 1 0.9171905 0.8883585 0.9388328 #> 97 29.5678669 A 404 0 1 0.9171905 0.8883585 0.9388328 #> 98 29.5736758 A 403 0 1 0.9171905 0.8883585 0.9388328 #> 99 30.4157094 A 402 1 0 0.9149090 0.8857551 0.9368882 #> 100 30.4968240 A 401 1 0 0.9126274 0.8831581 0.9349379 #> 101 30.7223985 A 400 1 0 0.9103458 0.8805673 0.9329822 #> 102 30.8273271 A 399 1 0 0.9080642 0.8779825 0.9310213 #> 103 30.8717178 A 398 1 0 0.9057827 0.8754033 0.9290553 #> 104 31.4891944 A 397 0 1 0.9057827 0.8754033 0.9290553 #> 105 31.7437313 A 396 0 1 0.9057827 0.8754033 0.9290553 #> 106 32.3195134 A 395 0 1 0.9057827 0.8754033 0.9290553 #> 107 32.8199139 A 394 0 1 0.9057827 0.8754033 0.9290553 #> 108 33.3162513 A 393 0 1 0.9057827 0.8754033 0.9290553 #> 109 33.8249243 A 392 0 1 0.9057827 0.8754033 0.9290553 #> 110 33.8799882 A 391 0 1 0.9057827 0.8754033 0.9290553 #> 111 34.0150132 A 390 1 0 0.9034602 0.8727748 0.9270542 #> 112 34.0826279 A 389 0 1 0.9034602 0.8727748 0.9270542 #> 113 34.0987472 A 388 0 1 0.9034602 0.8727748 0.9270542 #> 114 34.5806822 A 387 1 0 0.9011256 0.8701359 0.9250392 #> 115 34.5820833 A 386 0 1 0.9011256 0.8701359 0.9250392 #> 116 35.6976157 A 385 1 0 0.8987851 0.8674947 0.9230147 #> 117 35.7879285 A 384 0 1 0.8987851 0.8674947 0.9230147 #> 118 35.8691523 A 383 1 0 0.8964384 0.8648510 0.9209809 #> 119 36.0369583 A 382 1 0 0.8940917 0.8622127 0.9189424 #> 120 36.3408479 A 381 1 0 0.8917450 0.8595797 0.9168992 #> 121 37.0950110 A 380 0 1 0.8917450 0.8595797 0.9168992 #> 122 37.1862175 A 379 1 0 0.8893921 0.8569437 0.9148470 #> 123 37.7626359 A 378 1 0 0.8870392 0.8543127 0.9127904 #> 124 37.8814246 A 377 0 1 0.8870392 0.8543127 0.9127904 #> 125 38.5668907 A 376 0 1 0.8870392 0.8543127 0.9127904 #> 126 39.2364700 A 375 1 0 0.8846737 0.8516701 0.9107201 #> 127 39.5099966 A 374 0 1 0.8846737 0.8516701 0.9107201 #> 128 39.6943191 A 373 0 1 0.8846737 0.8516701 0.9107201 #> 129 39.8458329 A 372 0 1 0.8846737 0.8516701 0.9107201 #> 130 39.9270162 A 371 1 0 0.8822892 0.8490072 0.9086311 #> 131 40.1599769 A 370 0 1 0.8822892 0.8490072 0.9086311 #> 132 41.5788383 A 369 0 1 0.8822892 0.8490072 0.9086311 #> 133 41.6267738 A 368 1 0 0.8798917 0.8463323 0.9065281 #> 134 42.1096005 A 367 1 0 0.8774941 0.8436623 0.9044209 #> 135 42.4329399 A 366 0 1 0.8774941 0.8436623 0.9044209 #> 136 42.5864914 A 365 0 1 0.8774941 0.8436623 0.9044209 #> 137 43.0372554 A 364 0 1 0.8774941 0.8436623 0.9044209 #> 138 43.3221132 A 363 0 1 0.8774941 0.8436623 0.9044209 #> 139 43.6389358 A 362 0 1 0.8774941 0.8436623 0.9044209 #> 140 44.1549084 A 361 1 0 0.8750634 0.8409537 0.9022845 #> 141 44.5140716 A 360 0 1 0.8750634 0.8409537 0.9022845 #> 142 45.4720260 A 359 0 1 0.8750634 0.8409537 0.9022845 #> 143 46.1566080 A 358 0 1 0.8750634 0.8409537 0.9022845 #> 144 47.0546355 A 357 0 1 0.8750634 0.8409537 0.9022845 #> 145 48.0823337 A 356 0 1 0.8750634 0.8409537 0.9022845 #> 146 48.3479226 A 355 0 1 0.8750634 0.8409537 0.9022845 #> 147 48.4040249 A 354 0 1 0.8750634 0.8409537 0.9022845 #> 148 48.7539111 A 353 1 0 0.8725845 0.8381871 0.9001073 #> 149 49.2037680 A 352 1 0 0.8701055 0.8354258 0.8979258 #> 150 49.3435304 A 351 1 0 0.8676266 0.8326694 0.8957402 #> 151 49.6663522 A 350 0 1 0.8676266 0.8326694 0.8957402 #> 152 49.8173522 A 349 0 1 0.8676266 0.8326694 0.8957402 #> 153 50.1248807 A 348 0 1 0.8676266 0.8326694 0.8957402 #> 154 50.3150662 A 347 1 0 0.8651262 0.8298901 0.8935341 #> 155 50.6666237 A 346 1 0 0.8626259 0.8271158 0.8913239 #> 156 50.8566442 A 345 1 0 0.8601255 0.8243462 0.8891097 #> 157 51.6937890 A 344 0 1 0.8601255 0.8243462 0.8891097 #> 158 52.2469200 A 343 1 0 0.8576178 0.8215719 0.8868860 #> 159 52.4467296 A 342 1 0 0.8551102 0.8188021 0.8846584 #> 160 52.6613128 A 341 1 0 0.8526025 0.8160368 0.8824271 #> 161 52.7997845 A 340 1 0 0.8500949 0.8132759 0.8801920 #> 162 53.4114635 A 339 1 0 0.8475872 0.8105191 0.8779534 #> 163 53.9061795 A 338 0 1 0.8475872 0.8105191 0.8779534 #> 164 53.9968261 A 337 0 1 0.8475872 0.8105191 0.8779534 #> 165 54.6123914 A 336 0 1 0.8475872 0.8105191 0.8779534 #> 166 54.6861744 A 335 0 1 0.8475872 0.8105191 0.8779534 #> 167 55.0706664 A 334 0 1 0.8475872 0.8105191 0.8779534 #> 168 55.6752080 A 333 1 0 0.8450419 0.8077186 0.8756820 #> 169 56.4060157 A 332 0 1 0.8450419 0.8077186 0.8756820 #> 170 56.6565761 A 331 1 0 0.8424889 0.8049126 0.8734009 #> 171 56.6804001 A 330 0 1 0.8424889 0.8049126 0.8734009 #> 172 56.7837124 A 329 0 1 0.8424889 0.8049126 0.8734009 #> 173 57.1719156 A 328 0 1 0.8424889 0.8049126 0.8734009 #> 174 58.1487885 A 327 1 0 0.8399125 0.8020811 0.8710981 #> 175 58.9000652 A 326 0 1 0.8399125 0.8020811 0.8710981 #> 176 59.3667629 A 325 0 1 0.8399125 0.8020811 0.8710981 #> 177 59.6044136 A 324 0 1 0.8399125 0.8020811 0.8710981 #> 178 60.0808303 A 323 0 1 0.8399125 0.8020811 0.8710981 #> 179 60.2611923 A 322 0 1 0.8399125 0.8020811 0.8710981 #> 180 60.2893293 A 321 0 1 0.8399125 0.8020811 0.8710981 #> 181 60.6304562 A 320 0 1 0.8399125 0.8020811 0.8710981 #> 182 61.1170188 A 319 1 0 0.8372796 0.7991821 0.8687477 #> 183 61.6891086 A 318 0 1 0.8372796 0.7991821 0.8687477 #> 184 61.8689337 A 317 1 0 0.8346383 0.7962771 0.8663871 #> 185 62.6283981 A 316 0 1 0.8346383 0.7962771 0.8663871 #> 186 62.6601429 A 315 0 1 0.8346383 0.7962771 0.8663871 #> 187 63.5048707 A 314 0 1 0.8346383 0.7962771 0.8663871 #> 188 63.9452407 A 313 0 1 0.8346383 0.7962771 0.8663871 #> 189 64.6872731 A 312 0 1 0.8346383 0.7962771 0.8663871 #> 190 66.0390115 A 311 0 1 0.8346383 0.7962771 0.8663871 #> 191 66.0441764 A 310 0 1 0.8346383 0.7962771 0.8663871 #> 192 66.0733700 A 309 0 1 0.8346383 0.7962771 0.8663871 #> 193 67.3416893 A 308 1 0 0.8319284 0.7932893 0.8639695 #> 194 67.4707314 A 307 1 0 0.8292186 0.7903065 0.8615479 #> 195 67.6855902 A 306 0 1 0.8292186 0.7903065 0.8615479 #> 196 68.7223284 A 305 0 1 0.8292186 0.7903065 0.8615479 #> 197 70.2602449 A 304 0 1 0.8292186 0.7903065 0.8615479 #> 198 70.5995378 A 303 0 1 0.8292186 0.7903065 0.8615479 #> 199 71.2587985 A 302 1 0 0.8264728 0.7872829 0.8590945 #> 200 71.2672138 A 301 0 1 0.8264728 0.7872829 0.8590945 #> 201 71.8214219 A 300 0 1 0.8264728 0.7872829 0.8590945 #> 202 72.1670941 A 299 1 0 0.8237087 0.7842410 0.8566229 #> 203 72.1695415 A 298 1 0 0.8209446 0.7812042 0.8541472 #> 204 72.3471050 A 297 1 0 0.8181804 0.7781723 0.8516674 #> 205 73.0774123 A 296 0 1 0.8181804 0.7781723 0.8516674 #> 206 74.4971040 A 295 0 1 0.8181804 0.7781723 0.8516674 #> 207 75.4758943 A 294 1 0 0.8153975 0.7751215 0.8491691 #> 208 76.2621398 A 293 1 0 0.8126146 0.7720755 0.8466669 #> 209 76.6013852 A 292 1 0 0.8098317 0.7690343 0.8441608 #> 210 76.6524844 A 291 1 0 0.8070487 0.7659977 0.8416510 #> 211 76.9672003 A 290 1 0 0.8042658 0.7629656 0.8391374 #> 212 77.9263829 A 289 0 1 0.8042658 0.7629656 0.8391374 #> 213 79.8316941 A 288 0 1 0.8042658 0.7629656 0.8391374 #> 214 79.9076509 A 287 0 1 0.8042658 0.7629656 0.8391374 #> 215 80.5380440 A 286 1 0 0.8014537 0.7599013 0.8365973 #> 216 80.7172811 A 285 0 1 0.8014537 0.7599013 0.8365973 #> 217 81.0661957 A 284 0 1 0.8014537 0.7599013 0.8365973 #> 218 81.6143745 A 283 1 0 0.7986217 0.7568167 0.8340378 #> 219 83.7537322 A 282 0 1 0.7986217 0.7568167 0.8340378 #> 220 84.3199651 A 281 0 1 0.7986217 0.7568167 0.8340378 #> 221 84.7166775 A 280 0 1 0.7986217 0.7568167 0.8340378 #> 222 85.1705400 A 279 0 1 0.7986217 0.7568167 0.8340378 #> 223 85.1873869 A 278 0 1 0.7986217 0.7568167 0.8340378 #> 224 85.2629108 A 277 1 0 0.7957386 0.7536725 0.8314344 #> 225 86.8743175 A 276 1 0 0.7928555 0.7505332 0.8288271 #> 226 86.9340820 A 275 0 1 0.7928555 0.7505332 0.8288271 #> 227 87.0820950 A 274 1 0 0.7899618 0.7473854 0.8262078 #> 228 87.2997415 A 273 1 0 0.7870682 0.7442423 0.8235846 #> 229 87.4381573 A 272 1 0 0.7841746 0.7411038 0.8209576 #> 230 88.0871919 A 271 0 1 0.7841746 0.7411038 0.8209576 #> 231 88.3243550 A 270 1 0 0.7812702 0.7379564 0.8183186 #> 232 88.3276166 A 269 0 1 0.7812702 0.7379564 0.8183186 #> 233 88.6145506 A 268 0 1 0.7812702 0.7379564 0.8183186 #> 234 89.1750066 A 267 0 1 0.7812702 0.7379564 0.8183186 #> 235 89.4307290 A 266 1 0 0.7783331 0.7347728 0.8156499 #> 236 89.6167692 A 265 1 0 0.7753960 0.7315938 0.8129774 #> 237 89.7755695 A 264 1 0 0.7724589 0.7284193 0.8103013 #> 238 91.8647559 A 263 0 1 0.7724589 0.7284193 0.8103013 #> 239 91.9824456 A 262 1 0 0.7695106 0.7252353 0.8076127 #> 240 93.2408592 A 261 0 1 0.7695106 0.7252353 0.8076127 #> 241 93.3136053 A 260 0 1 0.7695106 0.7252353 0.8076127 #> 242 93.4905863 A 259 0 1 0.7695106 0.7252353 0.8076127 #> 243 93.7104093 A 258 0 1 0.7695106 0.7252353 0.8076127 #> 244 94.0876845 A 257 1 0 0.7665164 0.7219989 0.8048840 #> 245 94.7052270 A 256 0 1 0.7665164 0.7219989 0.8048840 #> 246 96.7897325 A 255 0 1 0.7665164 0.7219989 0.8048840 #> 247 97.4835043 A 254 1 0 0.7634986 0.7187379 0.8021327 #> 248 97.5095606 A 253 1 0 0.7604808 0.7154816 0.7993777 #> 249 98.2021703 A 252 1 0 0.7574630 0.7122299 0.7966190 #> 250 98.5382896 A 251 1 0 0.7544453 0.7089826 0.7938565 #> 251 99.3645367 A 250 0 1 0.7544453 0.7089826 0.7938565 #> 252 99.6052279 A 249 0 1 0.7544453 0.7089826 0.7938565 #> 253 99.6448581 A 248 0 1 0.7544453 0.7089826 0.7938565 #> 254 99.9203054 A 247 0 1 0.7544453 0.7089826 0.7938565 #> 255 100.0319960 A 246 0 1 0.7544453 0.7089826 0.7938565 #> 256 101.1286638 A 245 0 1 0.7544453 0.7089826 0.7938565 #> 257 101.2849073 A 244 0 1 0.7544453 0.7089826 0.7938565 #> 258 101.3714715 A 243 0 1 0.7544453 0.7089826 0.7938565 #> 259 101.4314849 A 242 0 1 0.7544453 0.7089826 0.7938565 #> 260 101.6631927 A 241 0 1 0.7544453 0.7089826 0.7938565 #> 261 101.6701105 A 240 0 1 0.7544453 0.7089826 0.7938565 #> 262 101.7250316 A 239 0 1 0.7544453 0.7089826 0.7938565 #> 263 102.0646836 A 238 0 1 0.7544453 0.7089826 0.7938565 #> 264 102.4731386 A 237 0 1 0.7544453 0.7089826 0.7938565 #> 265 103.3599337 A 236 0 1 0.7544453 0.7089826 0.7938565 #> 266 103.3858997 A 235 1 0 0.7512349 0.7055012 0.7909378 #> 267 104.1193509 A 234 0 1 0.7512349 0.7055012 0.7909378 #> 268 104.5408470 A 233 0 1 0.7512349 0.7055012 0.7909378 #> 269 104.8671198 A 232 0 1 0.7512349 0.7055012 0.7909378 #> 270 105.0762188 A 231 1 0 0.7479828 0.7019734 0.7879817 #> 271 105.9591180 A 230 0 1 0.7479828 0.7019734 0.7879817 #> 272 106.6420898 A 229 1 0 0.7447165 0.6984336 0.7850100 #> 273 106.7080111 A 228 0 1 0.7447165 0.6984336 0.7850100 #> 274 108.2199961 A 227 0 1 0.7447165 0.6984336 0.7850100 #> 275 108.6286686 A 226 0 1 0.7447165 0.6984336 0.7850100 #> 276 108.9082910 A 225 0 1 0.7447165 0.6984336 0.7850100 #> 277 108.9450103 A 224 0 1 0.7447165 0.6984336 0.7850100 #> 278 109.4524597 A 223 0 1 0.7447165 0.6984336 0.7850100 #> 279 110.6752035 A 222 0 1 0.7447165 0.6984336 0.7850100 #> 280 111.1416159 A 221 1 0 0.7413467 0.6947704 0.7819525 #> 281 111.9904588 A 220 1 0 0.7379769 0.6911137 0.7788900 #> 282 113.1377720 A 219 0 1 0.7379769 0.6911137 0.7788900 #> 283 114.1935322 A 218 1 0 0.7345917 0.6874440 0.7758105 #> 284 115.1863723 A 217 0 1 0.7345917 0.6874440 0.7758105 #> 285 116.0163469 A 216 0 1 0.7345917 0.6874440 0.7758105 #> 286 117.0809444 A 215 1 0 0.7311750 0.6837414 0.7727012 #> 287 117.1680451 A 214 1 0 0.7277583 0.6800452 0.7695870 #> 288 119.6814391 A 213 0 1 0.7277583 0.6800452 0.7695870 #> 289 120.7169260 A 212 0 1 0.7277583 0.6800452 0.7695870 #> 290 122.1766793 A 211 0 1 0.7277583 0.6800452 0.7695870 #> 291 124.8525835 A 210 1 0 0.7242928 0.6762944 0.7664294 #> 292 124.8827926 A 209 1 0 0.7208273 0.6725502 0.7632668 #> 293 125.7206051 A 208 0 1 0.7208273 0.6725502 0.7632668 #> 294 126.0500878 A 207 0 1 0.7208273 0.6725502 0.7632668 #> 295 126.7874323 A 206 0 1 0.7208273 0.6725502 0.7632668 #> 296 126.8931381 A 205 0 1 0.7208273 0.6725502 0.7632668 #> 297 127.9743928 A 204 0 1 0.7208273 0.6725502 0.7632668 #> 298 128.1287128 A 203 0 1 0.7208273 0.6725502 0.7632668 #> 299 128.8321170 A 202 1 0 0.7172588 0.6686841 0.7600184 #> 300 129.3533244 A 201 0 1 0.7172588 0.6686841 0.7600184 #> 301 129.6704036 A 200 1 0 0.7136725 0.6648029 0.7567505 #> 302 130.1803275 A 199 0 1 0.7136725 0.6648029 0.7567505 #> 303 130.7031601 A 198 0 1 0.7136725 0.6648029 0.7567505 #> 304 130.9639163 A 197 0 1 0.7136725 0.6648029 0.7567505 #> 305 130.9970045 A 196 1 0 0.7100313 0.6608601 0.7534342 #> 306 131.4908055 A 195 1 0 0.7063902 0.6569248 0.7501121 #> 307 132.0808389 A 194 0 1 0.7063902 0.6569248 0.7501121 #> 308 132.7882079 A 193 0 1 0.7063902 0.6569248 0.7501121 #> 309 132.8690671 A 192 0 1 0.7063902 0.6569248 0.7501121 #> 310 133.8500312 A 191 0 1 0.7063902 0.6569248 0.7501121 #> 311 133.9676829 A 190 0 1 0.7063902 0.6569248 0.7501121 #> 312 134.5847123 A 189 0 1 0.7063902 0.6569248 0.7501121 #> 313 134.9211996 A 188 0 1 0.7063902 0.6569248 0.7501121 #> 314 135.1019149 A 187 0 1 0.7063902 0.6569248 0.7501121 #> 315 135.1588000 A 186 1 0 0.7025924 0.6528006 0.7466625 #> 316 135.2069709 A 185 1 0 0.6987946 0.6486849 0.7432064 #> 317 135.8094446 A 184 1 0 0.6949968 0.6445774 0.7397439 #> 318 136.1992022 A 183 1 0 0.6911990 0.6404780 0.7362752 #> 319 136.3800132 A 182 1 0 0.6874012 0.6363864 0.7328003 #> 320 137.1939184 A 181 1 0 0.6836034 0.6323025 0.7293194 #> 321 139.0211403 A 180 0 1 0.6836034 0.6323025 0.7293194 #> 322 141.3017821 A 179 0 1 0.6836034 0.6323025 0.7293194 #> 323 141.6016337 A 178 0 1 0.6836034 0.6323025 0.7293194 #> 324 142.4839370 A 177 0 1 0.6836034 0.6323025 0.7293194 #> 325 143.3116393 A 176 1 0 0.6797193 0.6281190 0.7257650 #> 326 144.5084460 A 175 1 0 0.6758352 0.6239437 0.7222041 #> 327 145.2570428 A 174 1 0 0.6719511 0.6197762 0.7186371 #> 328 146.5126322 A 173 0 1 0.6719511 0.6197762 0.7186371 #> 329 147.5909236 A 172 1 0 0.6680444 0.6155886 0.7150461 #> 330 149.0394051 A 171 0 1 0.6680444 0.6155886 0.7150461 #> 331 151.8747627 A 170 1 0 0.6641147 0.6113805 0.7114309 #> 332 152.3036359 A 169 1 0 0.6601850 0.6071802 0.7078095 #> 333 152.6837825 A 168 0 1 0.6601850 0.6071802 0.7078095 #> 334 153.0189691 A 167 0 1 0.6601850 0.6071802 0.7078095 #> 335 153.7818843 A 166 0 1 0.6601850 0.6071802 0.7078095 #> 336 154.3107802 A 165 0 1 0.6601850 0.6071802 0.7078095 #> 337 156.3530945 A 164 0 1 0.6601850 0.6071802 0.7078095 #> 338 157.0907872 A 163 0 1 0.6601850 0.6071802 0.7078095 #> 339 158.0674152 A 162 0 1 0.6601850 0.6071802 0.7078095 #> 340 158.4800477 A 161 0 1 0.6601850 0.6071802 0.7078095 #> 341 158.5194710 A 160 1 0 0.6560589 0.6027445 0.7040278 #> 342 158.7392013 A 159 0 1 0.6560589 0.6027445 0.7040278 #> 343 161.5846889 A 158 1 0 0.6519066 0.5982855 0.7002185 #> 344 164.7372721 A 157 1 0 0.6477543 0.5938359 0.6964019 #> 345 166.2426128 A 156 1 0 0.6436020 0.5893954 0.6925782 #> 346 168.1161018 A 155 1 0 0.6394498 0.5849638 0.6887474 #> 347 168.2281259 A 154 1 0 0.6352975 0.5805409 0.6849098 #> 348 168.4294215 A 153 0 1 0.6352975 0.5805409 0.6849098 #> 349 168.8770094 A 152 1 0 0.6311179 0.5760931 0.6810437 #> 350 169.4244480 A 151 1 0 0.6269383 0.5716540 0.6771708 #> 351 169.4422522 A 150 1 0 0.6227587 0.5672233 0.6732911 #> 352 170.4704706 A 149 0 1 0.6227587 0.5672233 0.6732911 #> 353 170.5776237 A 148 1 0 0.6185509 0.5627666 0.6693824 #> 354 172.9990508 A 147 0 1 0.6185509 0.5627666 0.6693824 #> 355 173.7507167 A 146 0 1 0.6185509 0.5627666 0.6693824 #> 356 173.8815563 A 145 0 1 0.6185509 0.5627666 0.6693824 #> 357 173.9223130 A 144 1 0 0.6142554 0.5582111 0.6653973 #> 358 174.2766212 A 143 1 0 0.6099599 0.5536648 0.6614050 #> 359 174.5461325 A 142 1 0 0.6056644 0.5491273 0.6574057 #> 360 174.7162274 A 141 0 1 0.6056644 0.5491273 0.6574057 #> 361 176.6590842 A 140 1 0 0.6013383 0.5445612 0.6533749 #> 362 176.9610656 A 139 1 0 0.5970121 0.5400039 0.6493370 #> 363 178.5031223 A 138 0 1 0.5970121 0.5400039 0.6493370 #> 364 178.9681939 A 137 0 1 0.5970121 0.5400039 0.6493370 #> 365 179.5550296 A 136 0 1 0.5970121 0.5400039 0.6493370 #> 366 180.0471671 A 135 1 0 0.5925898 0.5353386 0.6452156 #> 367 180.2837518 A 134 0 1 0.5925898 0.5353386 0.6452156 #> 368 180.6996302 A 133 1 0 0.5881342 0.5306421 0.6410602 #> 369 180.9009377 A 132 1 0 0.5836786 0.5259552 0.6368973 #> 370 181.3283312 A 131 0 1 0.5836786 0.5259552 0.6368973 #> 371 184.0466008 A 130 1 0 0.5791888 0.5212360 0.6326994 #> 372 185.7438835 A 129 0 1 0.5791888 0.5212360 0.6326994 #> 373 187.2930109 A 128 1 0 0.5746639 0.5164839 0.6284660 #> 374 187.3284384 A 127 0 1 0.5746639 0.5164839 0.6284660 #> 375 187.8449785 A 126 0 1 0.5746639 0.5164839 0.6284660 #> 376 189.3613424 A 125 0 1 0.5746639 0.5164839 0.6284660 #> 377 189.4981770 A 124 1 0 0.5700295 0.5116085 0.6241378 #> 378 189.7859562 A 123 1 0 0.5653951 0.5067437 0.6198012 #> 379 190.6912802 A 122 1 0 0.5607607 0.5018892 0.6154563 #> 380 191.1076682 A 121 1 0 0.5561264 0.4970449 0.6111031 #> 381 198.6208194 A 120 1 0 0.5514920 0.4922108 0.6067419 #> 382 201.8084701 A 119 1 0 0.5468576 0.4873865 0.6023726 #> 383 203.2206009 A 118 0 1 0.5468576 0.4873865 0.6023726 #> 384 203.4020672 A 117 1 0 0.5421836 0.4825244 0.5979635 #> 385 204.6085827 A 116 0 1 0.5421836 0.4825244 0.5979635 #> 386 204.7131962 A 115 0 1 0.5421836 0.4825244 0.5979635 #> 387 204.8322250 A 114 1 0 0.5374276 0.4775738 0.5934806 #> 388 205.0363326 A 113 1 0 0.5326716 0.4726338 0.5889891 #> 389 205.1596244 A 112 0 1 0.5326716 0.4726338 0.5889891 #> 390 207.4070734 A 111 0 1 0.5326716 0.4726338 0.5889891 #> 391 208.5095081 A 110 1 0 0.5278291 0.4676004 0.5844199 #> 392 208.5440529 A 109 0 1 0.5278291 0.4676004 0.5844199 #> 393 209.6796639 A 108 1 0 0.5229418 0.4625242 0.5798058 #> 394 210.5303223 A 107 0 1 0.5229418 0.4625242 0.5798058 #> 395 211.4006422 A 106 0 1 0.5229418 0.4625242 0.5798058 #> 396 213.0554394 A 105 1 0 0.5179614 0.4573474 0.5751083 #> 397 214.0049455 A 104 0 1 0.5179614 0.4573474 0.5751083 #> 398 215.4027340 A 103 1 0 0.5129327 0.4521245 0.5703625 #> 399 217.0832170 A 102 1 0 0.5079039 0.4469140 0.5656067 #> 400 219.5385758 A 101 0 1 0.5079039 0.4469140 0.5656067 #> 401 221.0842591 A 100 1 0 0.5028249 0.4416555 0.5608007 #> 402 222.4007682 A 99 0 1 0.5028249 0.4416555 0.5608007 #> 403 222.7575312 A 98 0 1 0.5028249 0.4416555 0.5608007 #> 404 222.9677203 A 97 0 1 0.5028249 0.4416555 0.5608007 #> 405 227.9757161 A 96 0 1 0.5028249 0.4416555 0.5608007 #> 406 230.9483853 A 95 1 0 0.4975320 0.4361517 0.5558152 #> 407 233.3291179 A 94 0 1 0.4975320 0.4361517 0.5558152 #> 408 234.7546470 A 93 0 1 0.4975320 0.4361517 0.5558152 #> 409 236.1039880 A 92 0 1 0.4975320 0.4361517 0.5558152 #> 410 237.0593233 A 91 1 0 0.4920646 0.4304507 0.5506811 #> 411 237.8774307 A 90 1 0 0.4865972 0.4247664 0.5455337 #> 412 238.4480442 A 89 0 1 0.4865972 0.4247664 0.5455337 #> 413 241.1774392 A 88 0 1 0.4865972 0.4247664 0.5455337 #> 414 241.5604544 A 87 1 0 0.4810041 0.4189457 0.5402751 #> 415 242.1178978 A 86 0 1 0.4810041 0.4189457 0.5402751 #> 416 242.3122311 A 85 0 1 0.4810041 0.4189457 0.5402751 #> 417 245.3458498 A 84 1 0 0.4752779 0.4129803 0.5348990 #> 418 245.5501089 A 83 0 1 0.4752779 0.4129803 0.5348990 #> 419 245.8857517 A 82 0 1 0.4752779 0.4129803 0.5348990 #> 420 246.1299544 A 81 0 1 0.4752779 0.4129803 0.5348990 #> 421 247.7760192 A 80 0 1 0.4752779 0.4129803 0.5348990 #> 422 249.9233069 A 79 0 1 0.4752779 0.4129803 0.5348990 #> 423 252.7425757 A 78 0 1 0.4752779 0.4129803 0.5348990 #> 424 254.5446271 A 77 1 0 0.4691055 0.4064842 0.5291673 #> 425 256.4404196 A 76 0 1 0.4691055 0.4064842 0.5291673 #> 426 259.3829166 A 75 0 1 0.4691055 0.4064842 0.5291673 #> 427 271.3474961 A 74 0 1 0.4691055 0.4064842 0.5291673 #> 428 273.1257002 A 73 0 1 0.4691055 0.4064842 0.5291673 #> 429 280.3909651 A 72 0 1 0.4691055 0.4064842 0.5291673 #> 430 281.5194863 A 71 0 1 0.4691055 0.4064842 0.5291673 #> 431 282.6318510 A 70 0 1 0.4691055 0.4064842 0.5291673 #> 432 284.9961410 A 69 0 1 0.4691055 0.4064842 0.5291673 #> 433 287.8308610 A 68 0 1 0.4691055 0.4064842 0.5291673 #> 434 293.6815759 A 67 1 0 0.4621039 0.3989645 0.5228094 #> 435 295.1617767 A 66 1 0 0.4551023 0.3914862 0.5164191 #> 436 295.6642687 A 65 0 1 0.4551023 0.3914862 0.5164191 #> 437 299.0591141 A 64 0 1 0.4551023 0.3914862 0.5164191 #> 438 301.7347889 A 63 0 1 0.4551023 0.3914862 0.5164191 #> 439 303.3503769 A 62 0 1 0.4551023 0.3914862 0.5164191 #> 440 304.6405555 A 61 0 1 0.4551023 0.3914862 0.5164191 #> 441 310.0655345 A 60 0 1 0.4551023 0.3914862 0.5164191 #> 442 311.0184998 A 59 0 1 0.4551023 0.3914862 0.5164191 #> 443 311.8106687 A 58 1 0 0.4472557 0.3829516 0.5094087 #> 444 312.7128096 A 57 1 0 0.4394091 0.3744784 0.5023504 #> 445 313.1573766 A 56 1 0 0.4315625 0.3660646 0.4952459 #> 446 313.3722027 A 55 0 1 0.4315625 0.3660646 0.4952459 #> 447 316.3884586 A 54 0 1 0.4315625 0.3660646 0.4952459 #> 448 317.5266710 A 53 0 1 0.4315625 0.3660646 0.4952459 #> 449 318.7833017 A 52 1 0 0.4232633 0.3571188 0.4877865 #> 450 320.7036093 A 51 1 0 0.4149640 0.3482425 0.4802730 #> 451 326.3333469 A 50 1 0 0.4066647 0.3394335 0.4727070 #> 452 326.7511055 A 49 0 1 0.4066647 0.3394335 0.4727070 #> 453 329.3894821 A 48 0 1 0.4066647 0.3394335 0.4727070 #> 454 329.8200403 A 47 0 1 0.4066647 0.3394335 0.4727070 #> 455 330.7110893 A 46 0 1 0.4066647 0.3394335 0.4727070 #> 456 332.3420058 A 45 0 1 0.4066647 0.3394335 0.4727070 #> 457 336.8420868 A 44 1 0 0.3974223 0.3294592 0.4644560 #> 458 337.3663345 A 43 0 1 0.3974223 0.3294592 0.4644560 #> 459 341.3657643 A 42 0 1 0.3974223 0.3294592 0.4644560 #> 460 343.7001138 A 41 1 0 0.3877291 0.3189841 0.4558378 #> 461 344.6852715 A 40 0 1 0.3877291 0.3189841 0.4558378 #> 462 350.2207221 A 39 1 0 0.3777873 0.3082914 0.4469729 #> 463 350.2654152 A 38 0 1 0.3777873 0.3082914 0.4469729 #> 464 350.3644114 A 37 0 1 0.3777873 0.3082914 0.4469729 #> 465 352.4383084 A 36 0 1 0.3777873 0.3082914 0.4469729 #> 466 357.7553493 A 35 0 1 0.3777873 0.3082914 0.4469729 #> 467 359.1658176 A 34 0 1 0.3777873 0.3082914 0.4469729 #> 468 362.2067038 A 33 1 0 0.3663392 0.2956825 0.4370957 #> 469 363.8263817 A 32 1 0 0.3548911 0.2832713 0.4270651 #> 470 366.6874542 A 31 1 0 0.3434430 0.2710479 0.4168890 #> 471 370.0496553 A 30 0 1 0.3434430 0.2710479 0.4168890 #> 472 370.3157123 A 29 1 0 0.3316002 0.2584805 0.4063326 #> 473 373.8745493 A 28 0 1 0.3316002 0.2584805 0.4063326 #> 474 379.0575377 A 27 0 1 0.3316002 0.2584805 0.4063326 #> 475 382.6979071 A 26 1 0 0.3188463 0.2448977 0.3950803 #> 476 389.5516777 A 25 1 0 0.3060925 0.2315671 0.3836334 #> 477 391.6005114 A 24 0 1 0.3060925 0.2315671 0.3836334 #> 478 402.2578995 A 23 0 1 0.3060925 0.2315671 0.3836334 #> 479 404.9665031 A 22 1 0 0.2921792 0.2169492 0.3713229 #> 480 405.1024439 A 21 0 1 0.2921792 0.2169492 0.3713229 #> 481 406.0771660 A 20 0 1 0.2921792 0.2169492 0.3713229 #> 482 409.5208072 A 19 0 1 0.2921792 0.2169492 0.3713229 #> 483 414.9602508 A 18 1 0 0.2759470 0.1995282 0.3575339 #> 484 418.5750209 A 17 1 0 0.2597148 0.1826716 0.3433122 #> 485 425.2838139 A 16 0 1 0.2597148 0.1826716 0.3433122 #> 486 452.7208595 A 15 1 0 0.2424005 0.1649256 0.3281277 #> 487 453.8755271 A 14 0 1 0.2424005 0.1649256 0.3281277 #> 488 464.9161960 A 13 1 0 0.2237543 0.1461040 0.3117891 #> 489 500.0000000 A 12 0 12 0.2237543 0.1461040 0.3117891 #> cumhaz #> 1 0.000000000 #> 2 0.002004008 #> 3 0.004012040 #> 4 0.006024113 #> 5 0.006024113 #> 6 0.008044315 #> 7 0.008044315 #> 8 0.008044315 #> 9 0.010076835 #> 10 0.010076835 #> 11 0.010076835 #> 12 0.012121825 #> 13 0.012121825 #> 14 0.012121825 #> 15 0.012121825 #> 16 0.012121825 #> 17 0.014187940 #> 18 0.014187940 #> 19 0.014187940 #> 20 0.016266942 #> 21 0.016266942 #> 22 0.018354625 #> 23 0.018354625 #> 24 0.020451061 #> 25 0.022551902 #> 26 0.024657165 #> 27 0.024657165 #> 28 0.026771330 #> 29 0.026771330 #> 30 0.026771330 #> 31 0.026771330 #> 32 0.026771330 #> 33 0.026771330 #> 34 0.028912657 #> 35 0.028912657 #> 36 0.028912657 #> 37 0.031067830 #> 38 0.031067830 #> 39 0.033232332 #> 40 0.035401529 #> 41 0.037575442 #> 42 0.037575442 #> 43 0.037575442 #> 44 0.037575442 #> 45 0.039768425 #> 46 0.041966227 #> 47 0.044168870 #> 48 0.044168870 #> 49 0.044168870 #> 50 0.044168870 #> 51 0.044168870 #> 52 0.046396042 #> 53 0.046396042 #> 54 0.046396042 #> 55 0.046396042 #> 56 0.048643233 #> 57 0.050895485 #> 58 0.050895485 #> 59 0.053157928 #> 60 0.053157928 #> 61 0.055430656 #> 62 0.055430656 #> 63 0.055430656 #> 64 0.055430656 #> 65 0.055430656 #> 66 0.057729506 #> 67 0.060033654 #> 68 0.060033654 #> 69 0.060033654 #> 70 0.060033654 #> 71 0.060033654 #> 72 0.060033654 #> 73 0.062370102 #> 74 0.062370102 #> 75 0.064717520 #> 76 0.064717520 #> 77 0.067076011 #> 78 0.067076011 #> 79 0.069445679 #> 80 0.069445679 #> 81 0.071826631 #> 82 0.071826631 #> 83 0.074218976 #> 84 0.076617057 #> 85 0.076617057 #> 86 0.079026696 #> 87 0.079026696 #> 88 0.079026696 #> 89 0.081453880 #> 90 0.083886970 #> 91 0.083886970 #> 92 0.083886970 #> 93 0.083886970 #> 94 0.086343973 #> 95 0.086343973 #> 96 0.086343973 #> 97 0.086343973 #> 98 0.086343973 #> 99 0.088831535 #> 100 0.091325301 #> 101 0.093825301 #> 102 0.096331566 #> 103 0.098844129 #> 104 0.098844129 #> 105 0.098844129 #> 106 0.098844129 #> 107 0.098844129 #> 108 0.098844129 #> 109 0.098844129 #> 110 0.098844129 #> 111 0.101408232 #> 112 0.101408232 #> 113 0.101408232 #> 114 0.103992211 #> 115 0.103992211 #> 116 0.106589613 #> 117 0.106589613 #> 118 0.109200580 #> 119 0.111818381 #> 120 0.114443053 #> 121 0.114443053 #> 122 0.117081575 #> 123 0.119727078 #> 124 0.119727078 #> 125 0.119727078 #> 126 0.122393744 #> 127 0.122393744 #> 128 0.122393744 #> 129 0.122393744 #> 130 0.125089162 #> 131 0.125089162 #> 132 0.125089162 #> 133 0.127806553 #> 134 0.130531349 #> 135 0.130531349 #> 136 0.130531349 #> 137 0.130531349 #> 138 0.130531349 #> 139 0.130531349 #> 140 0.133301432 #> 141 0.133301432 #> 142 0.133301432 #> 143 0.133301432 #> 144 0.133301432 #> 145 0.133301432 #> 146 0.133301432 #> 147 0.133301432 #> 148 0.136134293 #> 149 0.138975202 #> 150 0.141824205 #> 151 0.141824205 #> 152 0.141824205 #> 153 0.141824205 #> 154 0.144706050 #> 155 0.147596223 #> 156 0.150494774 #> 157 0.150494774 #> 158 0.153410226 #> 159 0.156334202 #> 160 0.159266754 #> 161 0.162207930 #> 162 0.165157783 #> 163 0.165157783 #> 164 0.165157783 #> 165 0.165157783 #> 166 0.165157783 #> 167 0.165157783 #> 168 0.168160786 #> 169 0.168160786 #> 170 0.171181934 #> 171 0.171181934 #> 172 0.171181934 #> 173 0.171181934 #> 174 0.174240038 #> 175 0.174240038 #> 176 0.174240038 #> 177 0.174240038 #> 178 0.174240038 #> 179 0.174240038 #> 180 0.174240038 #> 181 0.174240038 #> 182 0.177374834 #> 183 0.177374834 #> 184 0.180529408 #> 185 0.180529408 #> 186 0.180529408 #> 187 0.180529408 #> 188 0.180529408 #> 189 0.180529408 #> 190 0.180529408 #> 191 0.180529408 #> 192 0.180529408 #> 193 0.183776161 #> 194 0.187033490 #> 195 0.187033490 #> 196 0.187033490 #> 197 0.187033490 #> 198 0.187033490 #> 199 0.190344748 #> 200 0.190344748 #> 201 0.190344748 #> 202 0.193689230 #> 203 0.197044935 #> 204 0.200411938 #> 205 0.200411938 #> 206 0.200411938 #> 207 0.203813299 #> 208 0.207226268 #> 209 0.210650925 #> 210 0.214087352 #> 211 0.217535627 #> 212 0.217535627 #> 213 0.217535627 #> 214 0.217535627 #> 215 0.221032131 #> 216 0.221032131 #> 217 0.221032131 #> 218 0.224565700 #> 219 0.224565700 #> 220 0.224565700 #> 221 0.224565700 #> 222 0.224565700 #> 223 0.224565700 #> 224 0.228175808 #> 225 0.231798997 #> 226 0.231798997 #> 227 0.235448632 #> 228 0.239111635 #> 229 0.242788106 #> 230 0.242788106 #> 231 0.246491810 #> 232 0.246491810 #> 233 0.246491810 #> 234 0.246491810 #> 235 0.250251208 #> 236 0.254024793 #> 237 0.257812672 #> 238 0.257812672 #> 239 0.261629466 #> 240 0.261629466 #> 241 0.261629466 #> 242 0.261629466 #> 243 0.261629466 #> 244 0.265520516 #> 245 0.265520516 #> 246 0.265520516 #> 247 0.269457524 #> 248 0.273410093 #> 249 0.277378347 #> 250 0.281362411 #> 251 0.281362411 #> 252 0.281362411 #> 253 0.281362411 #> 254 0.281362411 #> 255 0.281362411 #> 256 0.281362411 #> 257 0.281362411 #> 258 0.281362411 #> 259 0.281362411 #> 260 0.281362411 #> 261 0.281362411 #> 262 0.281362411 #> 263 0.281362411 #> 264 0.281362411 #> 265 0.281362411 #> 266 0.285617730 #> 267 0.285617730 #> 268 0.285617730 #> 269 0.285617730 #> 270 0.289946734 #> 271 0.289946734 #> 272 0.294313547 #> 273 0.294313547 #> 274 0.294313547 #> 275 0.294313547 #> 276 0.294313547 #> 277 0.294313547 #> 278 0.294313547 #> 279 0.294313547 #> 280 0.298838434 #> 281 0.303383888 #> 282 0.303383888 #> 283 0.307971044 #> 284 0.307971044 #> 285 0.307971044 #> 286 0.312622207 #> 287 0.317295104 #> 288 0.317295104 #> 289 0.317295104 #> 290 0.317295104 #> 291 0.322057009 #> 292 0.326841698 #> 293 0.326841698 #> 294 0.326841698 #> 295 0.326841698 #> 296 0.326841698 #> 297 0.326841698 #> 298 0.326841698 #> 299 0.331792193 #> 300 0.331792193 #> 301 0.336792193 #> 302 0.336792193 #> 303 0.336792193 #> 304 0.336792193 #> 305 0.341894234 #> 306 0.347022439 #> 307 0.347022439 #> 308 0.347022439 #> 309 0.347022439 #> 310 0.347022439 #> 311 0.347022439 #> 312 0.347022439 #> 313 0.347022439 #> 314 0.347022439 #> 315 0.352398783 #> 316 0.357804188 #> 317 0.363238971 #> 318 0.368703452 #> 319 0.374197957 #> 320 0.379722819 #> 321 0.379722819 #> 322 0.379722819 #> 323 0.379722819 #> 324 0.379722819 #> 325 0.385404637 #> 326 0.391118923 #> 327 0.396866049 #> 328 0.396866049 #> 329 0.402680003 #> 330 0.402680003 #> 331 0.408562356 #> 332 0.414479516 #> 333 0.414479516 #> 334 0.414479516 #> 335 0.414479516 #> 336 0.414479516 #> 337 0.414479516 #> 338 0.414479516 #> 339 0.414479516 #> 340 0.414479516 #> 341 0.420729516 #> 342 0.420729516 #> 343 0.427058630 #> 344 0.433428056 #> 345 0.439838313 #> 346 0.446289926 #> 347 0.452783432 #> 348 0.452783432 #> 349 0.459362380 #> 350 0.465984896 #> 351 0.472651563 #> 352 0.472651563 #> 353 0.479408320 #> 354 0.479408320 #> 355 0.479408320 #> 356 0.479408320 #> 357 0.486352764 #> 358 0.493345771 #> 359 0.500388024 #> 360 0.500388024 #> 361 0.507530882 #> 362 0.514725126 #> 363 0.514725126 #> 364 0.514725126 #> 365 0.514725126 #> 366 0.522132534 #> 367 0.522132534 #> 368 0.529651331 #> 369 0.537227088 #> 370 0.537227088 #> 371 0.544919396 #> 372 0.544919396 #> 373 0.552731896 #> 374 0.552731896 #> 375 0.552731896 #> 376 0.552731896 #> 377 0.560796412 #> 378 0.568926493 #> 379 0.577123215 #> 380 0.585387677 #> 381 0.593721011 #> 382 0.602124372 #> 383 0.602124372 #> 384 0.610671381 #> 385 0.610671381 #> 386 0.610671381 #> 387 0.619443310 #> 388 0.628292868 #> 389 0.628292868 #> 390 0.628292868 #> 391 0.637383777 #> 392 0.637383777 #> 393 0.646643036 #> 394 0.646643036 #> 395 0.646643036 #> 396 0.656166846 #> 397 0.656166846 #> 398 0.665875584 #> 399 0.675679505 #> 400 0.675679505 #> 401 0.685679505 #> 402 0.685679505 #> 403 0.685679505 #> 404 0.685679505 #> 405 0.685679505 #> 406 0.696205821 #> 407 0.696205821 #> 408 0.696205821 #> 409 0.696205821 #> 410 0.707194832 #> 411 0.718305943 #> 412 0.718305943 #> 413 0.718305943 #> 414 0.729800196 #> 415 0.729800196 #> 416 0.729800196 #> 417 0.741704958 #> 418 0.741704958 #> 419 0.741704958 #> 420 0.741704958 #> 421 0.741704958 #> 422 0.741704958 #> 423 0.741704958 #> 424 0.754691971 #> 425 0.754691971 #> 426 0.754691971 #> 427 0.754691971 #> 428 0.754691971 #> 429 0.754691971 #> 430 0.754691971 #> 431 0.754691971 #> 432 0.754691971 #> 433 0.754691971 #> 434 0.769617344 #> 435 0.784768859 #> 436 0.784768859 #> 437 0.784768859 #> 438 0.784768859 #> 439 0.784768859 #> 440 0.784768859 #> 441 0.784768859 #> 442 0.784768859 #> 443 0.802010239 #> 444 0.819554098 #> 445 0.837411241 #> 446 0.837411241 #> 447 0.837411241 #> 448 0.837411241 #> 449 0.856642010 #> 450 0.876249853 #> 451 0.896249853 #> 452 0.896249853 #> 453 0.896249853 #> 454 0.896249853 #> 455 0.896249853 #> 456 0.896249853 #> 457 0.918977126 #> 458 0.918977126 #> 459 0.918977126 #> 460 0.943367370 #> 461 0.943367370 #> 462 0.969008396 #> 463 0.969008396 #> 464 0.969008396 #> 465 0.969008396 #> 466 0.969008396 #> 467 0.969008396 #> 468 0.999311426 #> 469 1.030561426 #> 470 1.062819491 #> 471 1.062819491 #> 472 1.097302249 #> 473 1.097302249 #> 474 1.097302249 #> 475 1.135763788 #> 476 1.175763788 #> 477 1.175763788 #> 478 1.175763788 #> 479 1.221218333 #> 480 1.221218333 #> 481 1.221218333 #> 482 1.221218333 #> 483 1.276773889 #> 484 1.335597418 #> 485 1.335597418 #> 486 1.402264085 #> 487 1.402264085 #> 488 1.479187162 #> 489 1.479187162 #> #> $B #> time treatment n.risk n.event censor surv lower upper #> 490 0.8492261 B 300 1 0 0.99666667 0.97657559 0.9995298 #> 491 1.4030134 B 299 1 0 0.99333333 0.97360892 0.9983285 #> 492 1.7198339 B 298 1 0 0.99000000 0.96931862 0.9967638 #> 493 2.0758651 B 297 0 1 0.99000000 0.96931862 0.9967638 #> 494 2.3393342 B 296 1 0 0.98665541 0.96483781 0.9949706 #> 495 2.7979480 B 295 0 1 0.98665541 0.96483781 0.9949706 #> 496 2.9805455 B 294 0 1 0.98665541 0.96483781 0.9949706 #> 497 3.0113133 B 293 1 0 0.98328798 0.96031714 0.9930100 #> 498 3.0685160 B 292 1 0 0.97992056 0.95585305 0.9909291 #> 499 3.1712902 B 291 1 0 0.97655313 0.95144783 0.9887532 #> 500 3.2412819 B 290 1 0 0.97318571 0.94709776 0.9864998 #> 501 3.4211613 B 289 0 1 0.97318571 0.94709776 0.9864998 #> 502 3.4450075 B 288 1 0 0.96980659 0.94277571 0.9841754 #> 503 3.4515079 B 287 1 0 0.96642747 0.93850041 0.9817950 #> 504 3.6143976 B 286 1 0 0.96304836 0.93426704 0.9793660 #> 505 3.7608198 B 285 0 1 0.96304836 0.93426704 0.9793660 #> 506 4.6203917 B 284 1 0 0.95965734 0.93005084 0.9768874 #> 507 5.5211413 B 283 0 1 0.95965734 0.93005084 0.9768874 #> 508 5.5773040 B 282 1 0 0.95625430 0.92584945 0.9743634 #> 509 6.1533491 B 281 0 1 0.95625430 0.92584945 0.9743634 #> 510 6.4806603 B 280 1 0 0.95283911 0.92166069 0.9717974 #> 511 6.7140516 B 279 1 0 0.94942391 0.91750264 0.9691997 #> 512 7.3859528 B 278 1 0 0.94600872 0.91337265 0.9665731 #> 513 7.4210626 B 277 0 1 0.94600872 0.91337265 0.9665731 #> 514 7.5020884 B 276 1 0 0.94258115 0.90924888 0.9639122 #> 515 7.7622930 B 275 1 0 0.93915358 0.90514953 0.9612267 #> 516 8.2221920 B 274 1 0 0.93572602 0.90107282 0.9585184 #> 517 8.6680839 B 273 1 0 0.93229845 0.89701715 0.9557889 #> 518 8.7919587 B 272 0 1 0.93229845 0.89701715 0.9557889 #> 519 9.0396962 B 271 1 0 0.92885823 0.89296211 0.9530312 #> 520 9.0521882 B 270 0 1 0.92885823 0.89296211 0.9530312 #> 521 10.3249249 B 269 0 1 0.92885823 0.89296211 0.9530312 #> 522 10.5896188 B 268 1 0 0.92539234 0.88888760 0.9502379 #> 523 11.9051673 B 267 1 0 0.92192645 0.88483162 0.9474266 #> 524 12.1075238 B 266 0 1 0.92192645 0.88483162 0.9474266 #> 525 12.2191633 B 265 1 0 0.91844749 0.88077388 0.9445896 #> 526 12.2323488 B 264 1 0 0.91496852 0.87673294 0.9417364 #> 527 12.4915818 B 263 0 1 0.91496852 0.87673294 0.9417364 #> 528 12.7049720 B 262 0 1 0.91496852 0.87673294 0.9417364 #> 529 13.0554772 B 261 1 0 0.91146289 0.87266934 0.9388497 #> 530 13.2074189 B 260 1 0 0.90795727 0.86862143 0.9359482 #> 531 13.4869944 B 259 0 1 0.90795727 0.86862143 0.9359482 #> 532 13.5637730 B 258 0 1 0.90795727 0.86862143 0.9359482 #> 533 13.6943879 B 257 1 0 0.90442436 0.86454949 0.9330138 #> 534 13.9126408 B 256 1 0 0.90089145 0.86049230 0.9300655 #> 535 13.9279249 B 255 1 0 0.89735854 0.85644913 0.9271042 #> 536 14.2204744 B 254 1 0 0.89382563 0.85241930 0.9241303 #> 537 14.2519825 B 253 0 1 0.89382563 0.85241930 0.9241303 #> 538 14.4693075 B 252 1 0 0.89027871 0.84838272 0.9211345 #> 539 15.1259399 B 251 0 1 0.89027871 0.84838272 0.9211345 #> 540 15.2915139 B 250 0 1 0.89027871 0.84838272 0.9211345 #> 541 16.0775600 B 249 0 1 0.89027871 0.84838272 0.9211345 #> 542 16.3430735 B 248 0 1 0.89027871 0.84838272 0.9211345 #> 543 16.5377021 B 247 1 0 0.88667434 0.84427896 0.9180862 #> 544 16.7345687 B 246 0 1 0.88667434 0.84427896 0.9180862 #> 545 17.0540489 B 245 1 0 0.88305526 0.84016783 0.9150158 #> 546 17.7346078 B 244 1 0 0.87943618 0.83606940 0.9119339 #> 547 18.3219669 B 243 0 1 0.87943618 0.83606940 0.9119339 #> 548 18.5735263 B 242 1 0 0.87580215 0.83196263 0.9088302 #> 549 18.6707015 B 241 0 1 0.87580215 0.83196263 0.9088302 #> 550 18.8265045 B 240 1 0 0.87215297 0.82784712 0.9057050 #> 551 18.9679808 B 239 0 1 0.87215297 0.82784712 0.9057050 #> 552 18.9849356 B 238 1 0 0.86848846 0.82372246 0.9025581 #> 553 19.2886739 B 237 1 0 0.86482396 0.81960925 0.8994010 #> 554 19.3144386 B 236 0 1 0.86482396 0.81960925 0.8994010 #> 555 19.5837178 B 235 0 1 0.86482396 0.81960925 0.8994010 #> 556 19.7480647 B 234 1 0 0.86112813 0.81546487 0.8962111 #> 557 20.2311676 B 233 1 0 0.85743230 0.81133155 0.8930115 #> 558 20.3595135 B 232 1 0 0.85373647 0.80720889 0.8898023 #> 559 20.8240812 B 231 0 1 0.85373647 0.80720889 0.8898023 #> 560 20.8436242 B 230 1 0 0.85002457 0.80307513 0.8865720 #> 561 20.9476736 B 229 0 1 0.85002457 0.80307513 0.8865720 #> 562 21.4476172 B 228 0 1 0.85002457 0.80307513 0.8865720 #> 563 21.6935437 B 227 0 1 0.85002457 0.80307513 0.8865720 #> 564 22.2428646 B 226 1 0 0.84626340 0.79888609 0.8832962 #> 565 22.4233793 B 225 1 0 0.84250223 0.79470751 0.8800113 #> 566 22.5998941 B 224 1 0 0.83874106 0.79053903 0.8767175 #> 567 23.8855711 B 223 0 1 0.83874106 0.79053903 0.8767175 #> 568 24.2416986 B 222 0 1 0.83874106 0.79053903 0.8767175 #> 569 24.9889490 B 221 0 1 0.83874106 0.79053903 0.8767175 #> 570 25.3284722 B 220 0 1 0.83874106 0.79053903 0.8767175 #> 571 26.2260987 B 219 1 0 0.83491119 0.78628985 0.8733637 #> 572 26.2748075 B 218 1 0 0.83108132 0.78205100 0.8700011 #> 573 26.6127303 B 217 1 0 0.82725146 0.77782215 0.8666298 #> 574 27.0121949 B 216 1 0 0.82342159 0.77360296 0.8632501 #> 575 27.1385849 B 215 1 0 0.81959172 0.76939316 0.8598623 #> 576 27.1427844 B 214 1 0 0.81576185 0.76519245 0.8564664 #> 577 27.3660973 B 213 0 1 0.81576185 0.76519245 0.8564664 #> 578 27.4099098 B 212 1 0 0.81191392 0.76097725 0.8530490 #> 579 27.8727602 B 211 0 1 0.81191392 0.76097725 0.8530490 #> 580 28.7679537 B 210 1 0 0.80804766 0.75674724 0.8496099 #> 581 28.7863941 B 209 0 1 0.80804766 0.75674724 0.8496099 #> 582 28.8516511 B 208 0 1 0.80804766 0.75674724 0.8496099 #> 583 29.7580207 B 207 0 1 0.80804766 0.75674724 0.8496099 #> 584 29.8875161 B 206 1 0 0.80412510 0.75245351 0.8461202 #> 585 31.0753223 B 205 1 0 0.80020254 0.74816888 0.8426226 #> 586 31.3239259 B 204 1 0 0.79627998 0.74389311 0.8391174 #> 587 31.3737925 B 203 1 0 0.79235742 0.73962596 0.8356047 #> 588 31.4008382 B 202 1 0 0.78843486 0.73536720 0.8320847 #> 589 31.8360820 B 201 1 0 0.78451229 0.73111663 0.8285575 #> 590 32.1776180 B 200 1 0 0.78058973 0.72687405 0.8250232 #> 591 32.2845542 B 199 1 0 0.77666717 0.72263926 0.8214821 #> 592 32.3201275 B 198 0 1 0.77666717 0.72263926 0.8214821 #> 593 32.6268562 B 197 1 0 0.77272470 0.71838704 0.8179187 #> 594 32.8579123 B 196 1 0 0.76878223 0.71414244 0.8143486 #> 595 33.5083467 B 195 1 0 0.76483975 0.70990530 0.8107719 #> 596 33.7408843 B 194 1 0 0.76089728 0.70567545 0.8071887 #> 597 34.0285658 B 193 1 0 0.75695481 0.70145275 0.8035991 #> 598 34.3307012 B 192 0 1 0.75695481 0.70145275 0.8035991 #> 599 34.7955872 B 191 1 0 0.75299169 0.69721141 0.7999870 #> 600 35.3155408 B 190 1 0 0.74902858 0.69297709 0.7963686 #> 601 35.3873079 B 189 0 1 0.74902858 0.69297709 0.7963686 #> 602 35.3912534 B 188 0 1 0.74902858 0.69297709 0.7963686 #> 603 36.6028967 B 187 0 1 0.74902858 0.69297709 0.7963686 #> 604 37.1140694 B 186 1 0 0.74500154 0.68867044 0.7926935 #> 605 37.3202892 B 185 1 0 0.74097451 0.68437108 0.7890120 #> 606 37.3557166 B 184 0 1 0.74097451 0.68437108 0.7890120 #> 607 37.6872463 B 183 1 0 0.73692547 0.68005164 0.7853068 #> 608 37.8779261 B 182 1 0 0.73287643 0.67573939 0.7815953 #> 609 37.9046463 B 181 1 0 0.72882738 0.67143419 0.7778776 #> 610 38.2870764 B 180 1 0 0.72477834 0.66713590 0.7741537 #> 611 38.4278985 B 179 0 1 0.72477834 0.66713590 0.7741537 #> 612 38.4697339 B 178 1 0 0.72070655 0.66281652 0.7704057 #> 613 38.4803842 B 177 1 0 0.71663477 0.65850400 0.7666517 #> 614 38.8236166 B 176 1 0 0.71256298 0.65419821 0.7628917 #> 615 38.9656553 B 175 0 1 0.71256298 0.65419821 0.7628917 #> 616 39.0678457 B 174 1 0 0.70846779 0.64987050 0.7591070 #> 617 39.7824824 B 173 0 1 0.70846779 0.64987050 0.7591070 #> 618 40.1446851 B 172 1 0 0.70434879 0.64552047 0.7552973 #> 619 40.2919970 B 171 0 1 0.70434879 0.64552047 0.7552973 #> 620 40.8238759 B 170 1 0 0.70020556 0.64114771 0.7514621 #> 621 41.0376828 B 169 0 1 0.70020556 0.64114771 0.7514621 #> 622 41.0662129 B 168 0 1 0.70020556 0.64114771 0.7514621 #> 623 41.2949253 B 167 1 0 0.69601271 0.63672135 0.7475812 #> 624 42.5311133 B 166 0 1 0.69601271 0.63672135 0.7475812 #> 625 44.6248777 B 165 1 0 0.69179445 0.63227115 0.7436736 #> 626 44.8460398 B 164 1 0 0.68757620 0.62782812 0.7397599 #> 627 44.9419892 B 163 0 1 0.68757620 0.62782812 0.7397599 #> 628 46.6101451 B 162 0 1 0.68757620 0.62782812 0.7397599 #> 629 47.1405277 B 161 0 1 0.68757620 0.62782812 0.7397599 #> 630 47.8510476 B 160 0 1 0.68757620 0.62782812 0.7397599 #> 631 47.8891543 B 159 1 0 0.68325182 0.62326300 0.7357548 #> 632 48.0896160 B 158 1 0 0.67892744 0.61870565 0.7317431 #> 633 48.1972482 B 157 1 0 0.67460306 0.61415594 0.7277249 #> 634 48.2754867 B 156 1 0 0.67027868 0.60961373 0.7237003 #> 635 49.0816396 B 155 1 0 0.66595430 0.60507893 0.7196693 #> 636 49.7949898 B 154 0 1 0.66595430 0.60507893 0.7196693 #> 637 49.9062966 B 153 1 0 0.66160166 0.60051727 0.7156092 #> 638 51.2082623 B 152 1 0 0.65724902 0.59596301 0.7115427 #> 639 52.2920989 B 151 0 1 0.65724902 0.59596301 0.7115427 #> 640 53.3559798 B 150 1 0 0.65286736 0.59138107 0.7074464 #> 641 53.6840931 B 149 0 1 0.65286736 0.59138107 0.7074464 #> 642 54.9236065 B 148 1 0 0.64845609 0.58677089 0.7033198 #> 643 55.0154610 B 147 1 0 0.64404483 0.58216823 0.6991868 #> 644 55.3458512 B 146 0 1 0.64404483 0.58216823 0.6991868 #> 645 56.7171853 B 145 0 1 0.64404483 0.58216823 0.6991868 #> 646 56.8481500 B 144 1 0 0.63957229 0.57749934 0.6949977 #> 647 56.9334980 B 143 1 0 0.63509976 0.57283823 0.6908020 #> 648 57.1030595 B 142 1 0 0.63062723 0.56818477 0.6865997 #> 649 58.4270646 B 141 1 0 0.62615469 0.56353888 0.6823909 #> 650 58.6084245 B 140 1 0 0.62168216 0.55890044 0.6781757 #> 651 58.7730149 B 139 1 0 0.61720962 0.55426938 0.6739542 #> 652 59.6546380 B 138 1 0 0.61273709 0.54964561 0.6697263 #> 653 60.2527239 B 137 0 1 0.61273709 0.54964561 0.6697263 #> 654 61.1832674 B 136 0 1 0.61273709 0.54964561 0.6697263 #> 655 61.2068279 B 135 1 0 0.60819830 0.54495030 0.6654379 #> 656 61.3301293 B 134 1 0 0.60365950 0.54026258 0.6611430 #> 657 62.4040822 B 133 1 0 0.59912071 0.53558237 0.6568416 #> 658 62.4816013 B 132 1 0 0.59458192 0.53090958 0.6525338 #> 659 63.0700888 B 131 1 0 0.59004313 0.52624415 0.6482196 #> 660 63.3996256 B 130 0 1 0.59004313 0.52624415 0.6482196 #> 661 64.0457781 B 129 1 0 0.58546915 0.52154448 0.6438701 #> 662 64.5608166 B 128 0 1 0.58546915 0.52154448 0.6438701 #> 663 64.6149696 B 127 1 0 0.58085915 0.51680977 0.6394845 #> 664 64.9965467 B 126 1 0 0.57624916 0.51208270 0.6350923 #> 665 65.7530859 B 125 0 1 0.57624916 0.51208270 0.6350923 #> 666 67.4200893 B 124 1 0 0.57160199 0.50731942 0.6306629 #> 667 67.5251343 B 123 1 0 0.56695482 0.50256389 0.6262268 #> 668 68.5812580 B 122 0 1 0.56695482 0.50256389 0.6262268 #> 669 68.8229804 B 121 1 0 0.56226924 0.49777091 0.6217523 #> 670 69.0486551 B 120 1 0 0.55758367 0.49298583 0.6172710 #> 671 69.4935935 B 119 1 0 0.55289809 0.48820856 0.6127830 #> 672 69.5240787 B 118 0 1 0.55289809 0.48820856 0.6127830 #> 673 69.7451992 B 117 1 0 0.54817246 0.48339217 0.6082551 #> 674 70.1227875 B 116 0 1 0.54817246 0.48339217 0.6082551 #> 675 70.2628625 B 115 0 1 0.54817246 0.48339217 0.6082551 #> 676 72.9266499 B 114 1 0 0.54336393 0.47848660 0.6036517 #> 677 73.2620925 B 113 1 0 0.53855540 0.47358955 0.5990411 #> 678 73.5593273 B 112 1 0 0.53374687 0.46870096 0.5944232 #> 679 73.6926698 B 111 1 0 0.52893834 0.46382076 0.5897981 #> 680 74.0347874 B 110 1 0 0.52412981 0.45894889 0.5851659 #> 681 75.1656036 B 109 1 0 0.51932128 0.45408527 0.5805264 #> 682 75.4571413 B 108 0 1 0.51932128 0.45408527 0.5805264 #> 683 75.5205919 B 107 0 1 0.51932128 0.45408527 0.5805264 #> 684 75.5904943 B 106 0 1 0.51932128 0.45408527 0.5805264 #> 685 76.1546928 B 105 1 0 0.51437536 0.44906987 0.5757659 #> 686 76.4192775 B 104 1 0 0.50942945 0.44406367 0.5709975 #> 687 77.1943697 B 103 0 1 0.50942945 0.44406367 0.5709975 #> 688 77.4276997 B 102 0 1 0.50942945 0.44406367 0.5709975 #> 689 78.4347848 B 101 1 0 0.50438559 0.43895232 0.5661404 #> 690 78.8443008 B 100 0 1 0.50438559 0.43895232 0.5661404 #> 691 80.4921978 B 99 0 1 0.50438559 0.43895232 0.5661404 #> 692 82.5469181 B 98 0 1 0.50438559 0.43895232 0.5661404 #> 693 83.5830935 B 97 0 1 0.50438559 0.43895232 0.5661404 #> 694 83.5853505 B 96 1 0 0.49913158 0.43360420 0.5611027 #> 695 84.2445319 B 95 0 1 0.49913158 0.43360420 0.5611027 #> 696 84.6593786 B 94 0 1 0.49913158 0.43360420 0.5611027 #> 697 85.2637815 B 93 1 0 0.49376457 0.42813436 0.5559635 #> 698 88.7049039 B 92 0 1 0.49376457 0.42813436 0.5559635 #> 699 90.5420118 B 91 1 0 0.48833858 0.42260739 0.5507660 #> 700 90.6121802 B 90 1 0 0.48291260 0.41709349 0.5455577 #> 701 91.3637782 B 89 1 0 0.47748662 0.41159256 0.5403387 #> 702 91.8865159 B 88 1 0 0.47206063 0.40610446 0.5351090 #> 703 93.2119835 B 87 1 0 0.46663465 0.40062912 0.5298687 #> 704 93.6487147 B 86 1 0 0.46120866 0.39516642 0.5246177 #> 705 93.7520019 B 85 1 0 0.45578268 0.38971628 0.5193562 #> 706 93.8619589 B 84 0 1 0.45578268 0.38971628 0.5193562 #> 707 94.4459064 B 83 1 0 0.45029132 0.38420250 0.5140306 #> 708 95.9224128 B 82 1 0 0.44479996 0.37870177 0.5086939 #> 709 95.9720009 B 81 0 1 0.44479996 0.37870177 0.5086939 #> 710 96.2560394 B 80 1 0 0.43923996 0.37313424 0.5032901 #> 711 101.0785894 B 79 1 0 0.43367996 0.36758030 0.4978747 #> 712 101.8512269 B 78 1 0 0.42811997 0.36203990 0.4924480 #> 713 102.0480708 B 77 0 1 0.42811997 0.36203990 0.4924480 #> 714 102.1683344 B 76 1 0 0.42248681 0.35642831 0.4869497 #> 715 103.4746398 B 75 0 1 0.42248681 0.35642831 0.4869497 #> 716 105.5323827 B 74 0 1 0.42248681 0.35642831 0.4869497 #> 717 107.3694875 B 73 1 0 0.41669932 0.35065176 0.4813133 #> 718 109.4392228 B 72 1 0 0.41091183 0.34489101 0.4756636 #> 719 111.0420553 B 71 0 1 0.41091183 0.34489101 0.4756636 #> 720 113.1300571 B 70 0 1 0.41091183 0.34489101 0.4756636 #> 721 114.2058151 B 69 1 0 0.40495658 0.33895090 0.4698642 #> 722 114.4152727 B 68 0 1 0.40495658 0.33895090 0.4698642 #> 723 114.7067767 B 67 1 0 0.39891245 0.33292475 0.4639783 #> 724 114.8396735 B 66 0 1 0.39891245 0.33292475 0.4639783 #> 725 115.3801046 B 65 0 1 0.39891245 0.33292475 0.4639783 #> 726 115.6529216 B 64 0 1 0.39891245 0.33292475 0.4639783 #> 727 116.2388631 B 63 0 1 0.39891245 0.33292475 0.4639783 #> 728 116.5770227 B 62 1 0 0.39247838 0.32645875 0.4577657 #> 729 117.1516860 B 61 1 0 0.38604431 0.32001678 0.4515334 #> 730 119.8663712 B 60 1 0 0.37961024 0.31359868 0.4452816 #> 731 121.0960158 B 59 0 1 0.37961024 0.31359868 0.4452816 #> 732 123.7513692 B 58 1 0 0.37306524 0.30707385 0.4389223 #> 733 124.2150196 B 57 1 0 0.36652023 0.30057433 0.4325423 #> 734 127.4336784 B 56 0 1 0.36652023 0.30057433 0.4325423 #> 735 128.6077718 B 55 0 1 0.36652023 0.30057433 0.4325423 #> 736 128.9828791 B 54 0 1 0.36652023 0.30057433 0.4325423 #> 737 129.1025209 B 53 1 0 0.35960475 0.29366314 0.4258504 #> 738 129.7187580 B 52 1 0 0.35268928 0.28678334 0.4191330 #> 739 129.7250459 B 51 1 0 0.34577380 0.27993472 0.4123901 #> 740 133.1594500 B 50 0 1 0.34577380 0.27993472 0.4123901 #> 741 135.4086771 B 49 1 0 0.33871719 0.27295114 0.4055113 #> 742 139.8042160 B 48 1 0 0.33166059 0.26600114 0.3986051 #> 743 139.8137748 B 47 1 0 0.32460398 0.25908458 0.3916715 #> 744 140.2541753 B 46 1 0 0.31754737 0.25220139 0.3847103 #> 745 143.5751793 B 45 0 1 0.31754737 0.25220139 0.3847103 #> 746 144.2521772 B 44 0 1 0.31754737 0.25220139 0.3847103 #> 747 150.1996343 B 43 0 1 0.31754737 0.25220139 0.3847103 #> 748 150.6582394 B 42 1 0 0.30998672 0.24476184 0.3773298 #> 749 155.7676131 B 41 1 0 0.30242607 0.23736642 0.3699134 #> 750 157.8603589 B 40 1 0 0.29486541 0.23001505 0.3624608 #> 751 159.8315468 B 39 0 1 0.29486541 0.23001505 0.3624608 #> 752 160.7520774 B 38 0 1 0.29486541 0.23001505 0.3624608 #> 753 163.3827108 B 37 1 0 0.28689608 0.22222871 0.3546600 #> 754 163.6438166 B 36 1 0 0.27892674 0.21449687 0.3468147 #> 755 168.2133266 B 35 1 0 0.27095741 0.20681951 0.3389248 #> 756 170.6566002 B 34 0 1 0.27095741 0.20681951 0.3389248 #> 757 172.4208727 B 33 0 1 0.27095741 0.20681951 0.3389248 #> 758 175.4929594 B 32 0 1 0.27095741 0.20681951 0.3389248 #> 759 177.5091814 B 31 0 1 0.27095741 0.20681951 0.3389248 #> 760 184.5103590 B 30 0 1 0.27095741 0.20681951 0.3389248 #> 761 188.0853029 B 29 1 0 0.26161405 0.19756100 0.3299907 #> 762 191.6321564 B 28 1 0 0.25227069 0.18840587 0.3209734 #> 763 195.5002523 B 27 1 0 0.24292733 0.17935342 0.3118732 #> 764 196.4246807 B 26 0 1 0.24292733 0.17935342 0.3118732 #> 765 199.8426419 B 25 1 0 0.23321024 0.16995843 0.3024280 #> 766 201.1106058 B 24 0 1 0.23321024 0.16995843 0.3024280 #> 767 203.2845550 B 23 0 1 0.23321024 0.16995843 0.3024280 #> 768 204.6022754 B 22 0 1 0.23321024 0.16995843 0.3024280 #> 769 210.0548161 B 21 0 1 0.23321024 0.16995843 0.3024280 #> 770 212.7464896 B 20 1 0 0.22154973 0.15830211 0.2916309 #> 771 215.0981459 B 19 1 0 0.20988921 0.14689054 0.2806380 #> 772 215.5597419 B 18 1 0 0.19822870 0.13572014 0.2694522 #> 773 220.6392416 B 17 1 0 0.18656819 0.12478957 0.2580743 #> 774 230.5868701 B 16 1 0 0.17490768 0.11409964 0.2465032 #> 775 230.9729773 B 15 1 0 0.16324717 0.10365340 0.2347358 #> 776 232.5020784 B 14 0 1 0.16324717 0.10365340 0.2347358 #> 777 236.3826316 B 13 0 1 0.16324717 0.10365340 0.2347358 #> 778 253.9413828 B 12 0 1 0.16324717 0.10365340 0.2347358 #> 779 258.5498424 B 11 1 0 0.14840652 0.08975672 0.2209452 #> 780 294.1386975 B 10 1 0 0.13356586 0.07651535 0.2066334 #> 781 297.0789867 B 9 1 0 0.11872521 0.06393665 0.1917953 #> 782 320.4212314 B 8 1 0 0.10388456 0.05204555 0.1764110 #> 783 324.6217046 B 7 0 1 0.10388456 0.05204555 0.1764110 #> 784 333.8879947 B 6 1 0 0.08657047 0.03842706 0.1592925 #> 785 341.8226476 B 5 0 1 0.08657047 0.03842706 0.1592925 #> 786 364.3259341 B 4 1 0 0.06492785 0.02233017 0.1399053 #> 787 401.8506175 B 3 1 0 0.04328523 0.00996229 0.1177545 #> 788 417.7060892 B 2 0 1 0.04328523 0.00996229 0.1177545 #> 789 500.0000000 B 1 0 1 0.04328523 0.00996229 0.1177545 #> cumhaz #> 490 0.003333333 #> 491 0.006677815 #> 492 0.010033520 #> 493 0.010033520 #> 494 0.013411898 #> 495 0.013411898 #> 496 0.013411898 #> 497 0.016824867 #> 498 0.020249525 #> 499 0.023685951 #> 500 0.027134227 #> 501 0.027134227 #> 502 0.030606449 #> 503 0.034090770 #> 504 0.037587273 #> 505 0.037587273 #> 506 0.041108400 #> 507 0.041108400 #> 508 0.044654499 #> 509 0.044654499 #> 510 0.048225928 #> 511 0.051810157 #> 512 0.055407279 #> 513 0.055407279 #> 514 0.059030468 #> 515 0.062666831 #> 516 0.066316466 #> 517 0.069979470 #> 518 0.069979470 #> 519 0.073669507 #> 520 0.073669507 #> 521 0.073669507 #> 522 0.077400850 #> 523 0.081146169 #> 524 0.081146169 #> 525 0.084919754 #> 526 0.088707632 #> 527 0.088707632 #> 528 0.088707632 #> 529 0.092539050 #> 530 0.096385204 #> 531 0.096385204 #> 532 0.096385204 #> 533 0.100276254 #> 534 0.104182504 #> 535 0.108104073 #> 536 0.112041081 #> 537 0.112041081 #> 538 0.116009335 #> 539 0.116009335 #> 540 0.116009335 #> 541 0.116009335 #> 542 0.116009335 #> 543 0.120057918 #> 544 0.120057918 #> 545 0.124139551 #> 546 0.128237911 #> 547 0.128237911 #> 548 0.132370143 #> 549 0.132370143 #> 550 0.136536809 #> 551 0.136536809 #> 552 0.140738490 #> 553 0.144957899 #> 554 0.144957899 #> 555 0.144957899 #> 556 0.149231404 #> 557 0.153523249 #> 558 0.157833594 #> 559 0.157833594 #> 560 0.162181420 #> 561 0.162181420 #> 562 0.162181420 #> 563 0.162181420 #> 564 0.166606199 #> 565 0.171050643 #> 566 0.175514929 #> 567 0.175514929 #> 568 0.175514929 #> 569 0.175514929 #> 570 0.175514929 #> 571 0.180081139 #> 572 0.184668295 #> 573 0.189276590 #> 574 0.193906219 #> 575 0.198557382 #> 576 0.203230279 #> 577 0.203230279 #> 578 0.207947261 #> 579 0.207947261 #> 580 0.212709165 #> 581 0.212709165 #> 582 0.212709165 #> 583 0.212709165 #> 584 0.217563534 #> 585 0.222441583 #> 586 0.227343544 #> 587 0.232269652 #> 588 0.237220147 #> 589 0.242195272 #> 590 0.247195272 #> 591 0.252220397 #> 592 0.252220397 #> 593 0.257296539 #> 594 0.262398580 #> 595 0.267526785 #> 596 0.272681424 #> 597 0.277862772 #> 598 0.277862772 #> 599 0.283098374 #> 600 0.288361532 #> 601 0.288361532 #> 602 0.288361532 #> 603 0.288361532 #> 604 0.293737876 #> 605 0.299143281 #> 606 0.299143281 #> 607 0.304607762 #> 608 0.310102267 #> 609 0.315627129 #> 610 0.321182685 #> 611 0.321182685 #> 612 0.326800662 #> 613 0.332450380 #> 614 0.338132198 #> 615 0.338132198 #> 616 0.343879325 #> 617 0.343879325 #> 618 0.349693278 #> 619 0.349693278 #> 620 0.355575631 #> 621 0.355575631 #> 622 0.355575631 #> 623 0.361563655 #> 624 0.361563655 #> 625 0.367624261 #> 626 0.373721822 #> 627 0.373721822 #> 628 0.373721822 #> 629 0.373721822 #> 630 0.373721822 #> 631 0.380011130 #> 632 0.386340244 #> 633 0.392709671 #> 634 0.399119927 #> 635 0.405571540 #> 636 0.405571540 #> 637 0.412107488 #> 638 0.418686435 #> 639 0.418686435 #> 640 0.425353102 #> 641 0.425353102 #> 642 0.432109859 #> 643 0.438912580 #> 644 0.438912580 #> 645 0.438912580 #> 646 0.445857024 #> 647 0.452850031 #> 648 0.459892285 #> 649 0.466984483 #> 650 0.474127340 #> 651 0.481321585 #> 652 0.488567962 #> 653 0.488567962 #> 654 0.488567962 #> 655 0.495975369 #> 656 0.503438056 #> 657 0.510956853 #> 658 0.518532610 #> 659 0.526166198 #> 660 0.526166198 #> 661 0.533918136 #> 662 0.533918136 #> 663 0.541792152 #> 664 0.549728660 #> 665 0.549728660 #> 666 0.557793176 #> 667 0.565923257 #> 668 0.565923257 #> 669 0.574187720 #> 670 0.582521053 #> 671 0.590924415 #> 672 0.590924415 #> 673 0.599471423 #> 674 0.599471423 #> 675 0.599471423 #> 676 0.608243353 #> 677 0.617092911 #> 678 0.626021482 #> 679 0.635030491 #> 680 0.644121400 #> 681 0.653295712 #> 682 0.653295712 #> 683 0.653295712 #> 684 0.653295712 #> 685 0.662819522 #> 686 0.672434906 #> 687 0.672434906 #> 688 0.672434906 #> 689 0.682335896 #> 690 0.682335896 #> 691 0.682335896 #> 692 0.682335896 #> 693 0.682335896 #> 694 0.692752563 #> 695 0.692752563 #> 696 0.692752563 #> 697 0.703505251 #> 698 0.703505251 #> 699 0.714494262 #> 700 0.725605373 #> 701 0.736841328 #> 702 0.748204965 #> 703 0.759699218 #> 704 0.771327125 #> 705 0.783091830 #> 706 0.783091830 #> 707 0.795140023 #> 708 0.807335145 #> 709 0.807335145 #> 710 0.819835145 #> 711 0.832493373 #> 712 0.845313886 #> 713 0.845313886 #> 714 0.858471781 #> 715 0.858471781 #> 716 0.858471781 #> 717 0.872170411 #> 718 0.886059300 #> 719 0.886059300 #> 720 0.886059300 #> 721 0.900552053 #> 722 0.900552053 #> 723 0.915477426 #> 724 0.915477426 #> 725 0.915477426 #> 726 0.915477426 #> 727 0.915477426 #> 728 0.931606459 #> 729 0.947999901 #> 730 0.964666568 #> 731 0.964666568 #> 732 0.981907947 #> 733 0.999451807 #> 734 0.999451807 #> 735 0.999451807 #> 736 0.999451807 #> 737 1.018319731 #> 738 1.037550501 #> 739 1.057158344 #> 740 1.057158344 #> 741 1.077566507 #> 742 1.098399840 #> 743 1.119676436 #> 744 1.141415567 #> 745 1.141415567 #> 746 1.141415567 #> 747 1.141415567 #> 748 1.165225090 #> 749 1.189615334 #> 750 1.214615334 #> 751 1.214615334 #> 752 1.214615334 #> 753 1.241642361 #> 754 1.269420139 #> 755 1.297991568 #> 756 1.297991568 #> 757 1.297991568 #> 758 1.297991568 #> 759 1.297991568 #> 760 1.297991568 #> 761 1.332474326 #> 762 1.368188612 #> 763 1.405225649 #> 764 1.405225649 #> 765 1.445225649 #> 766 1.445225649 #> 767 1.445225649 #> 768 1.445225649 #> 769 1.445225649 #> 770 1.495225649 #> 771 1.547857228 #> 772 1.603412783 #> 773 1.662236313 #> 774 1.724736313 #> 775 1.791402980 #> 776 1.791402980 #> 777 1.791402980 #> 778 1.791402980 #> 779 1.882312070 #> 780 1.982312070 #> 781 2.093423182 #> 782 2.218423182 #> 783 2.218423182 #> 784 2.385089848 #> 785 2.385089848 #> 786 2.635089848 #> 787 2.968423182 #> 788 2.968423182 #> 789 2.968423182 #>"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/time_conversion.html","id":null,"dir":"Reference","previous_headings":"","what":"Get and Set Time Conversion Factors — set_time_conversion","title":"Get and Set Time Conversion Factors — set_time_conversion","text":"Get Set Time Conversion Factors","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/time_conversion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get and Set Time Conversion Factors — set_time_conversion","text":"","code":"set_time_conversion( default = \"days\", days = 1, weeks = 7, months = 365.25/12, years = 365.25 ) get_time_conversion(factor = c(\"days\", \"weeks\", \"months\", \"years\"))"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/time_conversion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get and Set Time Conversion Factors — set_time_conversion","text":"default default time scale, commonly whichever factor = 1 days Factor divide data time units get time days weeks Factor divide data time units get time weeks months Factor divide data time units get time months years Factor divide data time units get time years factor Time factor get.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/time_conversion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get and Set Time Conversion Factors — set_time_conversion","text":"value returned. Conversion factors stored internally used within functions.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/time_conversion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get and Set Time Conversion Factors — set_time_conversion","text":"","code":"# The default time scale is days: set_time_conversion(default = \"days\", days = 1, weeks = 7, months = 365.25 / 12, years = 365.25) # Set the default time scale to years set_time_conversion( default = \"years\", days = 1 / 365.25, weeks = 1 / 52.17857, months = 1 / 12, years = 1 ) # Get time scale factors: get_time_conversion(\"years\") #> years #> 1 get_time_conversion(\"weeks\") #> weeks #> 0.01916496"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/weighted_sat.html","id":null,"dir":"Reference","previous_headings":"","what":"Weighted object for single arm trial data — weighted_sat","title":"Weighted object for single arm trial data — weighted_sat","text":"Weighted object single arm trial data","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/weighted_sat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Weighted object for single arm trial data — weighted_sat","text":"","code":"weighted_sat"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/weighted_sat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Weighted object for single arm trial data — weighted_sat","text":"maicplus_estimate_weights object created estimate_weights() containing data patient level data weights centered_colnames Columns used MAIC nr_missing Number observations missing data ess Expected sample size opt Information optim weight calculation boot Parameters bootstrap sample weights, NULL object","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/weighted_twt.html","id":null,"dir":"Reference","previous_headings":"","what":"Weighted object for two arm trial data — weighted_twt","title":"Weighted object for two arm trial data — weighted_twt","text":"weighted patient data two arm trial generated centered patient data (centered_ipd_twt). weights calculated 100 bootstrap samples. object generated using following code:","code":"estimate_weights( data = centered_ipd_twt, centered_colnames = c( \"AGE_CENTERED\", \"AGE_MEDIAN_CENTERED\", \"AGE_SQUARED_CENTERED\", \"SEX_MALE_CENTERED\", \"ECOG0_CENTERED\", \"SMOKE_CENTERED\" ), n_boot_iteration = 100 )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/weighted_twt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Weighted object for two arm trial data — weighted_twt","text":"","code":"weighted_twt"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/weighted_twt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Weighted object for two arm trial data — weighted_twt","text":"maicplus_estimate_weights object created estimate_weights() containing data patient level data weights centered_colnames Columns used MAIC nr_missing Number observations missing data ess Expected sample size opt Information optim weight calculation boot Parameters bootstrap sample weights 100 samples","code":""},{"path":[]},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/news/index.html","id":"new-features-0-1-0","dir":"Changelog","previous_headings":"","what":"New features","title":"maicplus 0.1.0","text":"Add initializer script.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/news/index.html","id":"enhancements-0-1-0","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"maicplus 0.1.0","text":"Documentation use initialize package.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/news/index.html","id":"bug-fixes-0-1-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"maicplus 0.1.0","text":"None.","code":""}] diff --git a/pkgdown.yml b/pkgdown.yml index e4556d2..9dfc3fa 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -2,7 +2,7 @@ pandoc: '3.2' pkgdown: 2.1.0 pkgdown_sha: ~ articles: {} -last_built: 2024-08-15T12:31Z +last_built: 2024-09-03T07:34Z urls: reference: https://hta-pharma.github.io/maicplus/main/reference article: https://hta-pharma.github.io/maicplus/main/articles