109 adapt all examples and tests to datasets

NAMESPACE

@@ -5,16 +5,15 @@ S3method(print,maicplus_bucher)
 S3method(print,maicplus_check_weights)
 export(basic_kmplot)
 export(basic_kmplot2)
-export(bootstrap_HR)
 export(bucher)
 export(calculate_weights_legend)
 export(center_ipd)
 export(check_weights)
 export(dummize_ipd)
 export(estimate_weights)
 export(find_SE_from_CI)
-export(generate_survival_data)
 export(get_pseudo_ipd_binary)
+export(get_time_as)
 export(get_time_conversion)
 export(kmplot)
 export(kmplot2)

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/data.R

@@ -38,8 +38,8 @@
 #' Pseudo individual patient survival data from published study
 #' @format A data frame with 300 rows and 3 columns:
 #' \describe{
-#' \item{Time}{Survival time in days.}
-#' \item{Event}{Event indicator `0`/`1`.}
+#' \item{TIME}{Survival time in days.}
+#' \item{EVENT}{Event indicator `0`/`1`.}
 #' \item{ARM}{Assigned treatment arm, `"B"`.}
 #' }
 #' @family unanchored datasets
@@ -169,13 +169,15 @@
 #' \item{PARAM}{Parameter type of `AVAL`.}
 #' \item{RESPONSE}{Indicator of response.}
 #' }
+#' @family anchored datasets
+#' @keywords dataset
 "adrs_twt"
 
 #' Pseudo individual patient survival data from published two arm study
 #' @format A data frame with 800 rows and 3 columns:
 #' \describe{
-#' \item{Time}{Survival time in days.}
-#' \item{Event}{Event indicator `0`/`1`.}
+#' \item{TIME}{Survival time in days.}
+#' \item{EVENT}{Event indicator `0`/`1`.}
 #' \item{ARM}{Assigned treatment arm, `"B"`, `"C"`.}
 #' }
 #' @family anchored datasets

R/maic_anchored.R

@@ -47,6 +47,7 @@
 #' @importFrom boot boot boot.ci
 #' @return A list, contains 'descriptive' and 'inferential'
 #' @example inst/examples/maic_anchored_ex.R
+#' @example inst/examples/maic_anchored_binary_ex.R
 #' @export
 
 maic_anchored <- function(weights_object,

R/maic_unanchored.R

@@ -38,6 +38,8 @@
 #' @importFrom sandwich vcovHC
 #' @importFrom boot boot boot.ci
 #' @return A list, contains 'descriptive' and 'inferential'
+#' @example inst/examples/maic_unanchored_ex.R
+#' @example inst/examples/maic_unanchored_binary_ex.R
 #' @export
 
 maic_unanchored <- function(weights_object,

R/matching.R

@@ -37,17 +37,14 @@
 #' }
 #'
 #' @examples
-#' data(agd)
-#' data(adsl_sat)
-#' ipd_centered <- center_ipd(ipd = adsl_sat, agd = process_agd(agd))
-#' centered_colnames <- grep("_CENTERED", colnames(ipd_centered), value = TRUE)
-#' centered_colnames
-#' weighted_data <- estimate_weights(data = ipd_centered, centered_colnames = centered_colnames)
+#' 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 = ipd_centered, centered_colnames = centered_colnames, n_boot_iteration = 500
+#' data = centered_ipd_sat, centered_colnames = centered_colnames, n_boot_iteration = 100
 #' )
 #' }
 #' @export
@@ -400,6 +397,8 @@ plot.maicplus_estimate_weights <- function(x, ggplot = FALSE,
 #' aggregated data following the same naming convention
 #'
 #' @examples
+#' data(weighted_sat)
+#' data(agd)
 #' check_weights(weighted_sat, process_agd(agd))
 #'
 #' @import DescTools

R/plot_km.R

@@ -28,8 +28,8 @@
 #' appended.
 #' \item if "all", 2 by 2 plot, all plots in "by_trial" and "by_arm" without risk set table appended.
 #' }
-#' @example inst/examples/kmplot_anchored_ex.R
 #' @example inst/examples/kmplot_unanchored_ex.R
+#' @example inst/examples/kmplot_anchored_ex.R
 #' @export
 
 kmplot <- function(weights_object,
@@ -444,12 +444,13 @@ basic_kmplot <- function(kmdat,
 #' "TIME" columns and a column indicating treatment assignment
 #' @param tte_pseudo_ipd a data frame of pseudo IPD by digitized KM curves of external trial (for time-to-event
 #' endpoint), contain at least "EVENT", "TIME"
-#' @param trt_ipd a string, name of the interested investigation arm in internal trial \code{dat_igd} (real IPD)
-#' @param trt_agd a string, name of the interested investigation arm in external trial \code{dat_pseudo} (pseudo IPD)
+#' @param trt_ipd a string, name of the interested investigation arm in internal trial \code{tte_ipd} (real IPD)
+#' @param trt_agd a string, name of the interested investigation arm in external trial
+#' \code{tte_pseudo_ipd} (pseudo IPD)
 #' @param trt_common a string, name of the common comparator in internal and external trial, by default is NULL,
 #' indicating unanchored case
-#' @param trt_var_ipd a string, column name in \code{dat_ipd} that contains the treatment assignment
-#' @param trt_var_agd a string, column name in \code{dat_ipd} that contains the treatment assignment
+#' @param trt_var_ipd a string, column name in \code{tte_ipd} that contains the treatment assignment
+#' @param trt_var_agd a string, column name in \code{tte_pseudo_ipd} that contains the treatment assignment
 #' @param endpoint_name a string, name of time to event endpoint, to be show in the last line of title
 #' @param time_scale a string, time unit of median survival time, taking a value of 'years', 'months', 'weeks' or 'days'
 #' @param zph_transform a string, pass to \code{survival::cox.zph}, default is "log"
@@ -458,6 +459,9 @@ basic_kmplot <- function(kmdat,
 #'
 #' @return a 3 by 2 plot, include log-cumulative hazard plot, time dependent hazard function and unscaled Schoenfeld
 #' residual plot, before and after matching
+#'
+#' @example inst/examples/ph_diagplot_unanchored_ex.R
+#' @example inst/examples/ph_diagplot_anchored_ex.R
 #' @export
 ph_diagplot <- function(weights_object,
  tte_ipd,
@@ -611,8 +615,10 @@ ph_diagplot <- function(weights_object,
 #' @param exclude_censor logical, should censored data point be plotted
 #' @examples
 #' library(survival)
-#' load(system.file("extdata", "combined_data_tte.rda", package = "maicplus", mustWork = TRUE))
-#' kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data_tte, conf.type = "log-log")
+#' 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)"
@@ -687,8 +693,10 @@ ph_diagplot_lch <- function(km_fit,
 #' @param subtitle a character string, subtitle of the plot
 #' @examples
 #' library(survival)
-#' load(system.file("extdata", "combined_data_tte.rda", package = "maicplus", mustWork = TRUE))
-#' unweighted_cox <- coxph(Surv(TIME, EVENT == 1) ~ ARM, data = combined_data_tte)
+#' 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)"

R/plot_km2.R

@@ -31,8 +31,8 @@
 #' appended.
 #' \item if "all", 2 by 2 plot, all plots in "by_trial" and "by_arm" without risk set table appended.
 #' }
-#' @example inst/examples/kmplot2_anchored_ex.R
 #' @example inst/examples/kmplot2_unanchored_ex.R
+#' @example inst/examples/kmplot2_anchored_ex.R
 #' @export
 
 kmplot2 <- function(weights_object,
@@ -160,6 +160,7 @@ kmplot2 <- function(weights_object,
 #' it will be passed to 'col' of \code{lines()}
 #' @param use_line_types a numeric vector of length up to 4, line type to the KM curves,
 #' it will be passed to \code{lty} of \code{lines()}
+#' @example inst/examples/basic_kmplot2_ex.R
 #' @export
 
 basic_kmplot2 <- function(kmlist,

R/process_data.R

@@ -18,19 +18,8 @@
 #' by legal suffixes (i.e. MEAN, MEDIAN, SD, COUNT, or PROP).
 #'
 #' @examples
-#' target_pop <- read.csv(system.file("extdata", "aggregate_data_example_1.csv",
-#' package = "maicplus", mustWork = TRUE
-#' ))
-#' target_pop2 <- read.csv(system.file("extdata", "aggregate_data_example_2.csv",
-#' package = "maicplus", mustWork = TRUE
-#' ))
-#' target_pop3 <- read.csv(system.file("extdata", "aggregate_data_example_3.csv",
-#' package = "maicplus", mustWork = TRUE
-#' ))
-#'
-#' target_pop <- process_agd(target_pop)
-#' target_pop2 <- process_agd(target_pop2)
-#' target_pop3 <- process_agd(target_pop3)
+#' data(agd)
+#' agd <- process_agd(agd)
 #'
 #' @return pre-processed aggregate level data
 #' @export
@@ -151,20 +140,10 @@ 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)
-#' adsl <- dummize_ipd(adsl_sat, dummize_cols = c("SEX"), dummize_ref_level = c("Female"))
-#'
-#' # Reading aggregate data by Excel
-#' target_pop <- read.csv(
-#' system.file("extdata", "aggregate_data_example_1.csv", package = "maicplus", mustWork = TRUE)
-#' )
-#' agd <- process_agd(target_pop)
-#'
-#' # Alternatively, you can specify aggregate data manually in data frame
 #' data(agd)
-#' ipd_centered <- center_ipd(ipd = adsl, agd = agd)
-#'
+#' agd <- process_agd(agd)
+#' ipd_centered <- center_ipd(ipd = adsl_sat, agd = agd)
 #' @return centered ipd using aggregate level data averages
 #' @export
 
@@ -259,6 +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

@@ -7,18 +7,15 @@
 #' @param time_scale a character string, 'years', 'months', 'weeks' or 'days', time unit of median survival time
 #'
 #' @examples
+#' data(adtte_sat)
+#' data(pseudo_ipd_sat)
 #' library(survival)
-#' load(system.file("extdata", "combined_data_tte.rda", package = "maicplus", mustWork = TRUE))
-#' kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data_tte, conf.type = "log-log")
-#' kmobj_adj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data_tte,
-#' weights = combined_data_tte$weights, conf.type = "log-log"
-#' )
+#' 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_adj <- medSurv_makeup(kmobj_adj, legend = "after matching", time_scale = "day")
-#' medSurv_out <- rbind(medSurv, medSurv_adj)
-#' medSurv_out
+#' medSurv
 #' @return a data frame with a index column 'type', median survival time and confidence interval
 #' @export
 
@@ -47,11 +44,12 @@ medSurv_makeup <- function(km_fit, legend = "before matching", time_scale) {
 #' @param single_trt_name name of treatment if no strata are specified in `km_fit`
 #'
 #' @examples
-#' \dontrun{
-#' load(system.file("extdata", "combined_data_tte.rda", package = "maicplus", mustWork = TRUE))
-#' kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data_tte, conf.type = "log-log")
+#' 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)
-#' }
 #' @return a list of data frames of variables from [survival::survfit()]. Data frame is divided by treatment.
 #' @export
 

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",
@@ -68,7 +67,9 @@ get_time_conversion <- function(factor = c("days", "weeks", "months", "years"))
 #' @param as A time scale to convert to. One of "days", "weeks", "months", "years"
 #'
 #' @return Returns a numeric vector calculated from `times / get_time_conversion(factor = as)`
-
+#' @export
+#' @examples
+#' get_time_as(50, as = "months")
 get_time_as <- function(times, as = NULL) {
  if (is.null(as)) as <- settings_env$default_time_scale
  if (!is.numeric(times)) stop("times arguments must be numeric")

data-raw/dummy_anchored.R

@@ -1,8 +1,8 @@
 #### create anchored example datasets ####
 
-devtools::load_all()
+# devtools::load_all()
 library(flexsurv)
-set.seed(2024)
+set.seed(1234)
 
 # create adsl_twt
 adsl <- read.csv(system.file("extdata", "adsl.csv",
@@ -67,6 +67,7 @@ pseudo_ipd2$Time <- tmp$time_1
 pseudo_ipd2$Event <- tmp$event_1
 
 pseudo_ipd_twt <- rbind(pseudo_ipd, pseudo_ipd2)
+colnames(pseudo_ipd_twt) <- c("TIME", "EVENT", "ARM")
 
 # create centered adsl_twt
 agd <- process_agd(agd)

data-raw/dummy_unanchored.R

@@ -1,7 +1,7 @@
 #### create datasets for unanchored case ####
 ## adsl_sat, adtte_sat, adrs_sat, agd (AgD of effect modifiers), pseudo_ipd_sat (AgD, tte data)
 
-devtools::load_all()
+# devtools::load_all()
 # Read in relevant ADaM data and rename variables of interest
 adsl_sat <- read.csv(system.file("extdata", "adsl.csv",
  package = "maicplus",
@@ -31,6 +31,7 @@ pseudo_ipd_sat <- read.csv(system.file("extdata", "psuedo_IPD.csv",
  mustWork = TRUE
 ))
 pseudo_ipd_sat$ARM <- "B"
+colnames(pseudo_ipd_sat) <- c("TIME", "EVENT", "ARM")
 
 ### Centered IPD
 agd_sat <- process_agd(agd)

inst/examples/basic_kmplot2_ex.R

@@ -0,0 +1,18 @@
+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)

inst/examples/basic_kmplot_ex.R

@@ -1,6 +1,9 @@
 library(survival)
-load(system.file("extdata", "combined_data_tte.rda", package = "maicplus", mustWork = TRUE))
-kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data_tte, conf.type = "log-log")
+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)