rev

R/bucher.R

@@ -153,7 +153,7 @@ print.maicplus_bucher <- function(x, ci_digits = 2, pval_digits = 3,
 #' @param exponentiate whether the treatment effect and confidence
 #' interval should be exponentiated. This applies to relative
 #' treatment effects. Default is set to false.
-
+#' @keywords internal
 
 reformat <- function(x, ci_digits = 2, pval_digits = 3,
  show_pval = TRUE, exponentiate = FALSE) {

R/maic_unanchored.R

@@ -38,6 +38,7 @@
 #' @importFrom sandwich vcovHC
 #' @importFrom boot boot boot.ci
 #' @return A list, contains 'descriptive' and 'inferential'
+#' @example inst/examples/maic_anchored_ex.R
 #' @export
 
 maic_unanchored <- function(weights_object,

R/process_data.R

@@ -140,9 +140,9 @@ dummize_ipd <- function(raw_ipd, dummize_cols, dummize_ref_level) {
 #' should be followed by legal suffixes (i.e. MEAN, MEDIAN, SD, or PROP). Note that COUNT
 #' suffix is no longer accepted.
 #' @examples
-#' # load in IPD
 #' data(adsl_sat)
 #' data(agd)
+#' agd <- process_agd(agd)
 #' ipd_centered <- center_ipd(ipd = adsl_sat, agd = agd)
 #' @return centered ipd using aggregate level data averages
 #' @export
@@ -238,6 +238,7 @@ complete_agd <- function(use_agd) {
 #' @param trt values to include in treatment column
 #'
 #' @return a data frame that can be used as input to `survival::Surv`
+#' @keywords internal
 
 ext_tte_transfer <- function(dd, time_scale = "months", trt = NULL) {
  time_scale <- match.arg(time_scale, choices = c("years", "months", "weeks", "days"))

R/reporting.R

@@ -6,7 +6,7 @@
 #'
 #' @return a data frame with sample size, incidence rate, median survival time with 95% CI, hazard ratio estimate with
 #' 95% CI and Wald test of hazard ratio
-#'
+#' @example inst/examples/report_table_tte_ex.R
 #' @export
 
 report_table_tte <- function(coxobj, medSurvobj, tag = NULL) {
@@ -51,13 +51,13 @@ report_table_tte <- function(coxobj, medSurvobj, tag = NULL) {
 #' helper function: sort out a nice report table to summarize binary analysis results
 #'
 #' @param binobj object from glm()
-#' @param weighted_result object res_AB
+#' @param weighted_result weighted result object 
 #' @param eff_measure a string, binary effect measure, could be "OR", "RR", "RD"
 #' @param tag a string, by default NULL, if specified, an extra 1st column is created in the output
 #'
 #' @return a data frame with sample size, incidence rate, estimate of binary effect measure with
 #' 95% CI and Wald test of hazard ratio
-#'
+#' @example inst/examples/report_table_binary_ex.R
 #' @export
 
 report_table_binary <- function(binobj, weighted_result = NULL, eff_measure = c("OR", "RD", "RR"), tag = NULL) {

R/survival-helper.R

@@ -9,10 +9,8 @@
 #' @examples
 #' 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)
 #'
 #' # Derive median survival time
 #' medSurv <- medSurv_makeup(kmobj, legend = "before matching", time_scale = "day")

R/time-helper.R

@@ -17,7 +17,6 @@ settings_env <- new.env()
 #' # 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",

inst/examples/kmplot2_anchored_ex.R

@@ -1,4 +1,4 @@
-# anchored example using kmplot
+# anchored example using kmplot2
 
 data(weighted_twt)
 data(adtte_twt)

inst/examples/maic_anchored_binary_ex.R

@@ -0,0 +1,33 @@
+# Anchored example using maic_anchored for binary outcome
+
+data(weighted_twt)
+data(adtte_twt)
+data(pseudo_ipd_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"
+)
+
+result_binary$inferential$report_overall_robustCI
+result_binary$inferential$report_overall_bootCI

inst/examples/maic_anchored_ex.R

@@ -1,49 +1,14 @@
-# Anchored example using maic_anchored for binary outcome
-
-# weighted_twt # weighted patient level data from estimate_weights()
-# adrs_twt # patient level outcome data
-# pseudo_adrs # pseudo patient level outcome data from publication
-
-# 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,
- trt_var_ipd = "ARM",
- pseudo_ipd = pseudo_adrs,
- trt_var_agd = "ARM",
- trt_ipd = "A",
- trt_agd = "B",
- trt_common = "C",
- endpoint_name = "Binary Event",
- endpoint_type = "binary",
- eff_measure = "OR"
-)
-
-result_binary$inferential$report_overall_robustCI
-result_binary$inferential$report_overall_bootCI
-
-
 # Anchored example using maic_anchored for time-to-event data
 
-# weighted_twt # weighted patient level data from estimate_weights()
-# adtte_twt # two arm patient level time-to-event data
-# pseudo_ipd_twt # digitised 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,
- trt_var_ipd = "ARM",
  pseudo_ipd = pseudo_ipd_twt,
+ trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  trt_ipd = "A",
  trt_agd = "B",

inst/examples/report_table_binary_ex.R

@@ -0,0 +1,13 @@
+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")

inst/examples/report_table_tte_ex.R

@@ -0,0 +1,10 @@
+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)