Nothing
R/BuyseTest.R
In BuyseTest: Generalized Pairwise Comparisons
Defines functions
calcSample
.BuyseTest
BuyseTest
Documented in
BuyseTest
calcSample
## * Documentation - BuyseTest
#' @name BuyseTest
#' @title Generalized Pairwise Comparisons (GPC)
#'
#' @description Performs Generalized Pairwise Comparisons for binary, continuous and time-to-event endpoints.
#'
#' @param formula [formula] a symbolic description of the GPC model,
#' typically \code{treatment ~ type1(endpoint1) + type2(endpoint2, threshold2) + strata}.
#' See Details, section "Specification of the GPC model".
#' @param treatment,endpoint,type,threshold,status,operator,censoring,restriction,strata Alternative to \code{formula} for describing the GPC model.
#' See Details, section "Specification of the GPC model".
#' @param data [data.frame] dataset.
#' @param scoring.rule [character] method used to compare the observations of a pair in presence of right censoring (i.e. \code{"timeToEvent"} endpoints).
#' Can be \code{"Gehan"} or \code{"Peron"}.
#' See Details, section "Handling missing values".
#' @param pool.strata [character] weights used to combine estimates across strata. Can be
#' \code{"Buyse"} to weight proportionnally to the number of pairs in the strata,
#' \code{"CMH"} to weight proportionnally to the ratio between the number of pairs in the strata and the number of observations in the strata.
#' \code{"equal"} to weight equally each strata,
#' or \code{"var-netBenefit"} to weight each strata proportionally to the precision of its estimated net benefit (similar syntax for the win ratio: \code{"var-winRatio"})
#' @param correction.uninf [integer] should a correction be applied to remove the bias due to the presence of uninformative pairs?
#' 0 indicates no correction, 1 impute the average score of the informative pairs, and 2 performs IPCW.
#' See Details, section "Handling missing values".
#' @param model.tte [list] optional survival models relative to each time to each time to event endpoint.
#' Models must \code{prodlim} objects and stratified on the treatment and strata variable. When used, the uncertainty from the estimates of these survival models is ignored.
#' @param method.inference [character] method used to compute confidence intervals and p-values.
#' Can be \code{"none"}, \code{"u-statistic"}, \code{"permutation"}, \code{"studentized permutation"}, \code{"bootstrap"}, \code{"studentized bootstrap"}.
#' See Details, section "Statistical inference".
#' @param n.resampling [integer] the number of permutations/samples used for computing the confidence intervals and the p.values.
#' See Details, section "Statistical inference".
#' @param strata.resampling [character] the variable on which the permutation/sampling should be stratified.
#' See Details, section "Statistical inference".
#' @param hierarchical [logical] should only the uninformative pairs be analyzed at the lower priority endpoints (hierarchical GPC)?
#' Otherwise all pairs will be compaired for all endpoint (full GPC).
#' @param weightEndpoint [numeric vector] weights used to cumulating the pairwise scores over the endpoints.
#' Only used when \code{hierarchical=FALSE}. Disregarded if the argument \code{formula} is defined.
#' @param weightObs [character or numeric vector] weights or variable in the dataset containing the weight associated to each observation.
#' These weights are only considered when performing GPC (but not when fitting surival models).
#' @param neutral.as.uninf [logical vector] should paired classified as neutral be re-analyzed using endpoints of lower priority (as it is done for uninformative pairs).
#' See Details, section "Handling missing values".
#' @param add.halfNeutral [logical] should half of the neutral score be added to the favorable and unfavorable scores?
#' @param keep.pairScore [logical] should the result of each pairwise comparison be kept?
#' @param seed [integer, >0] the seed to consider when performing resampling.
#' If \code{NULL} no seed is set.
#' @param cpus [integer, >0] the number of CPU to use.
#' Only the permutation test can use parallel computation.
#' See Details, section "Statistical inference".
#' @param trace [integer] should the execution of the function be traced ? \code{0} remains silent
#' and \code{1}-\code{3} correspond to a more and more verbose output in the console.
#'
#' @details
#'
#' \bold{Specification of the GPC model}: \cr
#' There are two way to specify the GPC model in \code{BuyseTest}.
#' A \emph{Formula interface} via the argument \code{formula} where the response variable should be a binary variable defining the treatment arms.
#' The rest of the formula should indicate the endpoints by order of priority and the strata variables (if any).
#' A \emph{Vector interface} using the following arguments \itemize{
#' \item \code{treatment}: [character] name of the treatment variable identifying the control and the experimental group.
#' Must have only two levels (e.g. \code{0} and \code{1}).
#' \item \code{endpoint}: [character vector] the name of the endpoint variable(s).
#' \item \code{threshold}: [numeric vector] critical values used to compare the pairs (threshold of minimal important difference).
#' A pair will be classified as neutral if the difference in endpoint is strictly below this threshold.
#' There must be one threshold for each endpoint variable; it must be \code{NA} for binary endpoints and positive for continuous or time to event endpoints.
#' \item \code{status}: [character vector] the name of the binary variable(s) indicating whether the endpoint was observed or censored.
#' Must value \code{NA} when the endpoint is not a time to event.
#' \item \code{operator}: [character vector] the sign defining a favorable endpoint.
#' \code{">0"} indicates that higher values are favorable while "<0" indicates the opposite.
#' \item \code{type}: [character vector] indicates whether it is
#' a binary outcome (\code{"b"}, \code{"bin"}, or \code{"binary"}),
#' a continuous outcome (\code{"c"}, \code{"cont"}, or \code{"continuous"}),
#' or a time to event outcome (\code{"t"}, \code{"tte"}, \code{"time"}, or \code{"timetoevent"})
#' \item \code{censoring}: [character vector] is the endpoint subject to right or left censoring (\code{"left"} or \code{"right"}). The default is right-censoring.
#' \item \code{restriction}: [numeric vector] value above which any difference is classified as neutral.
#' \item \code{strata}: [character vector] if not \code{NULL}, the GPC will be applied within each group of patient defined by the strata variable(s).
#' }
#' The formula interface can be more concise, especially when considering few outcomes, but may be more difficult to apprehend for new users.
#' Note that arguments \code{endpoint}, \code{threshold}, \code{status}, \code{operator}, \code{type}, and \code{censoring} must have the same length. \cr \cr \cr
#'
#'
#' \bold{GPC procedure} \cr
#' The GPC procedure form all pairs of observations, one belonging to the experimental group and the other to the control group, and class them in 4 categories: \itemize{
#' \item \emph{Favorable pair}: the endpoint is better for the observation in the experimental group.
#' \item \emph{Unfavorable pair}: the endpoint is better for the observation in the control group.
#' \item \emph{Neutral pair}: the difference between the endpoints of the two observations is (in absolute value) below the threshold. When \code{threshold=0}, neutral pairs correspond to pairs with equal endpoint. Lower-priority outcomes (if any) are then used to classified the pair into favorable/unfavorable.
#' \item \emph{Uninformative pair}: censoring/missingness prevents from classifying into favorable, unfavorable or neutral.
#' }
#' With complete data, pairs can be decidely classified as favorable/unfavorable/neutral.
#' In presence of missing values, the GPC procedure uses the scoring rule (argument \code{scoring.rule}) and the correction for uninformative pairs (argument \code{correction.uninf}) to classify the pairs.
#' The classification may not be 0,1, e.g. the probability that the pair is favorable/unfavorable/neutral with the Peron's scoring rule.
#' To export the classification of each pair set the argument code{keep.pairScore} to \code{TRUE} and call the function \code{getPairScore} on the result of the \code{BuyseTest} function. \cr \cr \cr
#'
#'
#' \bold{Handling missing values}
#' \itemize{
#' \item \code{scoring.rule}: indicates how to handle right-censoring in time to event endpoints using information from the survival curves.
#' The Gehan's scoring rule (argument \code{scoring.rule="Gehan"}) only scores pairs that can be decidedly classified as favorable, unfavorable, or neutral
#' while the "Peron"'s scoring rule (argument \code{scoring.rule="Peron"}) uses the empirical survival curves of each group to also score the pairs that cannot be decidedly classified.
#' The Peron's scoring rule is the recommanded scoring rule but only handles right-censoring.
#' \item \code{correction.uninf}: indicates how to handle missing values that could not be classified by the scoring rule. \describe{
#' \item{\code{correction.uninf=0}}{ treat them as uninformative: this is an equivalent to complete case analysis when \code{neutral.as.uninf=FALSE}, while when \code{neutral.as.uninf=TRUE}, uninformative pairs are treated as neutral, i.e., analyzed at the following endpoint (if any). This approach will (generally) lead to biased estimates for the proportion of favorable, unfavorable, or neutral pairs.}
#' \item{\code{correction.uninf=1}}{ imputes to the uninformative pairs the average score of the informative pairs, i.e. assumes that uninformative pairs would on average behave like informative pairs. This is therefore the recommanded approach when this assumption is resonnable, typically when the the tail of the survival function estimated by the Kaplan–Meier method is close to 0.}
#' \item{\code{correction.uninf=2}}{ uses inverse probability of censoring weights (IPCW), i.e. up-weight informative pairs to represent uninformative pairs. It also assumes that uninformative pairs would on average behave like informative pairs and is only recommanded when the analysis is stopped after the first endpoint with uninformative pairs.}
#' }
#' Note that both corrections will convert the whole proportion of uninformative pairs of a given endpoint into favorable, unfavorable, or neutral pairs. See Peron et al (2021) for further details and recommandations \cr \cr
#' }
#'
#'
#' \bold{Statistical inference} \cr
#' The argument \code{method.inference} defines how to approximate the distribution of the GPC estimators and so how standard errors, confidence intervals, and p-values are computed.
#' Available methods are:
#' \itemize{
#' \item argument \code{method.inference="none"}: only the point estimate is computed which makes the execution of the \code{BuyseTest} faster than with the other methods.
#' \item argument \code{method.inference="u-statistic"}: uses a Gaussian approximation to obtain the distribution of the GPC estimators.
#' The U-statistic theory indicates that this approximation is asymptotically exact.
#' The variance is computed using a H-projection of order 1 (default option), which is a consistent but downward biased estimator.
#' An unbiased estimator can be obtained using a H-projection of order 2 (only available for the uncorrected Gehan's scoring rule, see \code{BuyseTest.options}).
#' \bold{WARNING}: the current implementation of the H-projection is not valid when using corrections for uninformative pairs (\code{correction.uninf=1}, or \code{correction.uninf=2}).
#' \item argument \code{method.inference="permutation"}: perform a permutation test, estimating in each sample the summary statistics (net benefit, win ratio).
#' \item argument \code{method.inference="studentized permutation"}: perform a permutation test, estimating in each sample the summary statistics (net benefit, win ratio) and the variance-covariance matrix of the estimate.
#' \item argument \code{method.inference="bootstrap"}: perform a non-parametric boostrap, estimating in each sample the summary statistics (net benefit, win ratio).
#' \item argument \code{method.inference=" studentized bootstrap"}: perform a non-parametric boostrap, estimating in each sample the summary statistics (net benefit, win ratio) and the variance-covariance matrix of the estimator.
#' }
#' Additional arguments for permutation and bootstrap resampling:
#' \itemize{
#' \item \code{strata.resampling} If \code{NA} or of length 0, the permutation/non-parametric boostrap will be performed by resampling in the whole sample.
#' Otherwise, the permutation/non-parametric boostrap will be performed separately for each level that the variable defined in \code{strata.resampling} take.
#' \item \code{n.resampling} set the number of permutations/samples used.
#' A large number of permutations (e.g. \code{n.resampling=10000}) are needed to obtain accurate CI and p.value. See (Buyse et al., 2010) for more details.
#' \item \code{cpus} indicates whether the resampling procedure can be splitted on several cpus to save time. Can be set to \code{"all"} to use all available cpus.
#' The detection of the number of cpus relies on the \code{detectCores} function from the \emph{parallel} package. \cr \cr
#' }
#'
#' \bold{Pooling results across strata} the relative contribution of each strata to the global estimator is decided by \cr
#' \itemize{
#' \item \code{"CMH"} weights: Cochran-Mantel-Haenszel type weights which are optimal if the odds ratios are constant across strata.
#' \item \code{"Buyse"} weights: optimal if the risk difference is constant across strata. \cr \cr
#' }
#'
#' \bold{Default values} \cr
#' The default of the arguments
#' \code{scoring.rule}, \code{correction.uninf}, \code{method.inference}, \code{n.resampling},
#' \code{hierarchical}, \code{neutral.as.uninf}, \code{keep.pairScore}, \code{strata.resampling},
#' \code{cpus}, \code{trace} is read from \code{BuyseTest.options()}. \cr
#' Additional (hidden) arguments are \itemize{
#' \item \code{alternative} [character] the alternative hypothesis. Must be one of "two.sided", "greater" or "less" (used by \code{confint}).
#' \item \code{conf.level} [numeric] level for the confidence intervals (used by \code{confint}).
#' \item \code{keep.survival} [logical] export the survival values used by the Peron's scoring rule.
#' \item \code{order.Hprojection} [1 or 2] the order of the H-projection used to compute the variance when \code{method.inference="u-statistic"}.
#' }
#'
#' @return An \R object of class \code{\linkS4class{S4BuyseTest}}.
#'
#' @references
#' On the GPC procedure: Marc Buyse (2010). \bold{Generalized pairwise comparisons of prioritized endpoints in the two-sample problem}. \emph{Statistics in Medicine} 29:3245-3257 \cr
#' On the win ratio: D. Wang, S. Pocock (2016). \bold{A win ratio approach to comparing continuous non-normal outcomes in clinical trials}. \emph{Pharmaceutical Statistics} 15:238-245 \cr
#' On the Peron's scoring rule: J. Peron, M. Buyse, B. Ozenne, L. Roche and P. Roy (2018). \bold{An extension of generalized pairwise comparisons for prioritized outcomes in the presence of censoring}. \emph{Statistical Methods in Medical Research} 27: 1230-1239. \cr
#' On the Gehan's scoring rule: Gehan EA (1965). \bold{A generalized two-sample Wilcoxon test for doubly censored data}. \emph{Biometrika} 52(3):650-653 \cr
#' On inference in GPC using the U-statistic theory: Ozenne B, Budtz-Jorgensen E, Peron J (2021). \bold{The asymptotic distribution of the Net Benefit estimator in presence of right-censoring}. \emph{Statistical Methods in Medical Research} 2021 doi:10.1177/09622802211037067 \cr
#' On how to handle right-censoring: J. Peron, M. Idlhaj, D. Maucort-Boulch, et al. (2021) \bold{Correcting the bias of the net benefit estimator due to right-censored observations}. \emph{Biometrical Journal} 63: 893–906.
#'
#' @seealso
#' \code{\link{S4BuyseTest-summary}} for a summary of the results of generalized pairwise comparison. \cr
#' \code{\link{S4BuyseTest-confint}} for exporting estimates with confidence intervals and p-values. \cr
#' \code{\link{S4BuyseTest-class}} for a presentation of the \code{S4BuyseTest} object. \cr
#' \code{\link{S4BuyseTest-sensitivity}} for performing a sensitivity analysis on the choice of the threshold(s). \cr
#' \code{\link{constStrata}} to create a strata variable from several clinical variables. \cr
#' @keywords function BuyseTest
#' @author Brice Ozenne
## * BuyseTest (example)
#' @rdname BuyseTest
#' @examples
#' library(data.table)
#'
#' #### simulate some data ####
#' set.seed(10)
#' df.data <- simBuyseTest(1e2, n.strata = 2)
#'
#' ## display
#' if(require(prodlim)){
#' resKM_tempo <- prodlim(Hist(eventtime,status)~treatment, data = df.data)
#' plot(resKM_tempo)
#' }
#'
#' #### one time to event endpoint ####
#' BT <- BuyseTest(treatment ~ TTE(eventtime, status = status), data= df.data)
#'
#' summary(BT) # net benefit
#' summary(BT, percentage = FALSE)
#' summary(BT, statistic = "winRatio") # win Ratio
#'
#' ## permutation instead of asymptotics to compute the p-value
#' \dontrun{
#' BT <- BuyseTest(treatment ~ TTE(eventtime, status = status), data=df.data,
#' method.inference = "permutation", n.resampling = 1e3)
#' }
#' \dontshow{
#' BT <- BuyseTest(treatment ~ TTE(eventtime, status = status), data=df.data,
#' method.inference = "permutation", n.resampling = 1e1, trace = 0)
#' }
#' summary(BT, statistic = "netBenefit") ## default
#' summary(BT, statistic = "winRatio")
#'
#' ## parallel permutation
#' \dontrun{
#' BT <- BuyseTest(treatment ~ TTE(eventtime, status = status), data=df.data,
#' method.inference = "permutation", n.resampling = 1e3, cpus = 2)
#' summary(BT)
#' }
#'
#' ## method Gehan is much faster but does not optimally handle censored observations
#' BT <- BuyseTest(treatment ~ TTE(eventtime, status = status), data=df.data,
#' scoring.rule = "Gehan", trace = 0)
#' summary(BT)
#'
#' #### one time to event endpoint: only differences in survival over 1 unit ####
#' BT <- BuyseTest(treatment ~ TTE(eventtime, threshold = 1, status = status), data=df.data)
#' summary(BT)
#'
#' #### one time to event endpoint with a strata variable
#' BT <- BuyseTest(treatment ~ strata + TTE(eventtime, status = status), data=df.data)
#' summary(BT)
#'
#' #### several endpoints with a strata variable
#' f <- treatment ~ strata + T(eventtime, status, 1) + B(toxicity)
#' f <- update(f,
#' ~. + T(eventtime, status, 0.5) + C(score, 1) + T(eventtime, status, 0.25))
#'
#' BT <- BuyseTest(f, data=df.data)
#' summary(BT)
#'
#' #### real example : veteran dataset of the survival package ####
#' ## Only one endpoint. Type = Time-to-event. Thresold = 0. Stratfication by histological subtype
#' ## scoring.rule = "Gehan"
#'
#' if(require(survival)){
#' \dontrun{
#' library(survival) ## import veteran
#'
#' ## scoring.rule = "Gehan"
#' BT_Gehan <- BuyseTest(trt ~ celltype + TTE(time,threshold=0,status=status),
#' data=veteran, scoring.rule="Gehan")
#'
#' summary_Gehan <- summary(BT_Gehan)
#' summary_Gehan <- summary(BT_Gehan, statistic = "winRatio")
#'
#' ## scoring.rule = "Peron"
#' BT_Peron <- BuyseTest(trt ~ celltype + TTE(time,threshold=0,status=status),
#' data=veteran, scoring.rule="Peron")
#'
#' class(BT_Peron)
#' summary(BT_Peron)
#' }
#' }
## * BuyseTest (code)
##' @export
BuyseTest <- function(formula,
data,
scoring.rule = NULL,
pool.strata = NULL,
correction.uninf = NULL,
model.tte = NULL,
method.inference = NULL,
n.resampling = NULL,
strata.resampling = NULL,
hierarchical = NULL,
weightEndpoint = NULL,
weightObs = NULL,
neutral.as.uninf = NULL,
add.halfNeutral = NULL,
keep.pairScore = NULL,
seed = NULL,
cpus = NULL,
trace = NULL,
treatment = NULL,
endpoint = NULL,
type = NULL,
threshold = NULL,
status = NULL,
operator = NULL,
censoring = NULL,
restriction = NULL,
strata = NULL){
mycall <- match.call()
name.call <- names(mycall)
option <- BuyseTest.options()
## ** compatibility with previous version
if(!is.null(method.inference) && (method.inference=="asymptotic")){
stop("Value \"asymptotic\" for argument \'method.inference\' is obsolete. \n",
"Use \"u-statistic\" instead \n")
}
## ** initialize arguments (all expect data that is just converted to data.table)
## initialized arguments are stored in outArgs
outArgs <- initializeArgs(status = status,
correction.uninf = correction.uninf,
cpus = cpus,
data = data,
endpoint = endpoint,
formula = formula,
hierarchical = hierarchical,
keep.pairScore = keep.pairScore,
method.inference = method.inference,
scoring.rule = scoring.rule,
pool.strata = pool.strata,
model.tte = model.tte,
n.resampling = n.resampling,
strata.resampling = strata.resampling,
name.call = name.call,
neutral.as.uninf = neutral.as.uninf,
add.halfNeutral = add.halfNeutral,
operator = operator,
censoring = censoring,
option = option,
seed = seed,
strata = strata,
threshold = threshold,
restriction = restriction,
trace = trace,
treatment = treatment,
type = type,
weightEndpoint = weightEndpoint,
weightObs = weightObs,
envir = parent.frame())
## ** test arguments
if(option$check){
outTest <- do.call(testArgs, args = outArgs)
}
## ** initialization data
## WARNING when updating code: names in the c() must precisely match output of initializeData, in the same order
out.name <- c("data","M.endpoint","M.status",
"index.C","index.T","weightObs","index.strata",
"level.treatment","level.strata", "method.score",
"n.strata","n.obs","n.obsStrata","n.obsStrataResampling","cumn.obsStrataResampling","skeletonPeron",
"scoring.rule", "iidNuisance", "nUTTE.analyzedPeron_M1", "endpoint.UTTE", "status.UTTE", "D.UTTE","index.UTTE","keep.pairScore")
outArgs[out.name] <- initializeData(data = outArgs$data,
type = outArgs$type,
endpoint = outArgs$endpoint,
Uendpoint = outArgs$Uendpoint,
D = outArgs$D,
scoring.rule = outArgs$scoring.rule,
status = outArgs$status,
Ustatus = outArgs$Ustatus,
method.inference = outArgs$method.inference,
censoring = outArgs$censoring,
strata = outArgs$strata,
treatment = outArgs$treatment,
hierarchical = outArgs$hierarchical,
copy = TRUE,
keep.pairScore = outArgs$keep.pairScore,
endpoint.TTE = outArgs$endpoint.TTE,
status.TTE = outArgs$status.TTE,
iidNuisance = outArgs$iidNuisance,
weightEndpoint = outArgs$weightEndpoint,
weightObs = outArgs$weightObs)
if(option$check){
if(outArgs$iidNuisance && any(outArgs$method.score == "CRPeron")){
warning("Inference via the asymptotic theory for competing risks when using the Peron's scoring rule has not been validating \n",
"Consider setting \'method.inference\' to \"none\", \"bootstrap\", or \"permutation\" \n")
}
## if(outArgs$precompute && any(outArgs$method.score == "CRPeron")){
## stop("Option \'precompute\' is not available for the Peron scoring rule in the competing risk case \n")
## }
if(outArgs$precompute && any(outArgs$method.score == "CRPeron")){
outArgs$precompute <- FALSE
}
}
## ** Display
if (outArgs$trace > 1) {
cat("\n Generalized Pairwise Comparisons\n\n")
do.call(printGeneral, args = outArgs)
cat("\n")
}
## ** define environment
envirBT <- environment()
envirBT$.BuyseTest <- .BuyseTest
envirBT$initializeData <- initializeData
envirBT$calcPeron <- calcPeron
envirBT$outArgs$args.model.tte <- option$args.model.tte
## ** Point estimation
if (outArgs$trace > 1) {
if(outArgs$iid){
cat("Point estimation and calculation of the iid decomposition")
}else{
cat("Point estimation")
}
}
outPoint <- .BuyseTest(envir = envirBT,
iid = outArgs$iid,
method.inference = "none", ## do not use outArgs$method.inference as when it is equal to "bootstrap" or "permutation" we need the point estimate first.
pointEstimation = TRUE)
if (outArgs$trace > 1) {
cat("\n\n")
}
## check number of pairs
if(option$check){
vec.nPair <- (outPoint$count_favorable + outPoint$count_unfavorable + outPoint$count_neutral + outPoint$count_uninf )[,1]
if(any(abs(outPoint$n_pairs - vec.nPair) > 0.01)){
warning("Incorrect estimation of the number of pairs \n",
"Something probably went wrong - contact the package maintainer\n")
}
}
## convert from a list of vector (output of C++) to a list of data.table
if(outArgs$keep.pairScore){
## needed for inference with bebu
outPoint$tableScore <- pairScore2dt(outPoint$tableScore,
level.treatment = outArgs$level.treatment,
level.strata = outArgs$level.strata,
n.strata = outArgs$n.strata,
endpoint = outArgs$endpoint,
threshold = outArgs$threshold,
restriction = outArgs$restriction)
}
## ** Inference
if((outArgs$method.inference != "none") && (outArgs$trace > 1)){
do.call(printInference, args = outArgs)
}
outResampling <- NULL
if(outArgs$method.inference == "u-statistic"){
## done in the C++ code
}else if(outArgs$method.inference == "u-statistic-bebu"){
if(outArgs$keep.pairScore == FALSE){
stop("Argument \'keep.pairScore\' needs to be TRUE when argument \'method.inference\' is \"u-statistic-bebu\" \n")
}
## direct computation of the variance
outCovariance <- inferenceUstatisticBebu(tablePairScore = outPoint$tableScore,
order = option$order.Hprojection,
weightEndpoint = outArgs$weightEndpoint,
n.pairs = outPoint$n_pairs,
n.C = length(envirBT$outArgs$index.C),
n.T = length(envirBT$outArgs$index.T),
level.strata = outArgs$level.strata,
n.strata = outArgs$n.strata,
endpoint = outArgs$endpoint)
outPoint$covariance <- outCovariance$Sigma
attr(outArgs$method.inference,"Hprojection") <- option$order.Hprojection
}else if(grepl("bootstrap|permutation",outArgs$method.inference)){
outResampling <- inferenceResampling(envirBT)
}
if((outArgs$method.inference != "none") && (outArgs$trace > 1)){
cat("\n")
}
## ** Gather results into a S4BuyseTest object
if(outArgs$trace > 1){
cat("Gather the results in a S4BuyseTest object \n")
}
keep.args <- c("index.T", "index.C", "index.strata", "type","endpoint","level.strata","level.treatment","scoring.rule","hierarchical","neutral.as.uninf","add.halfNeutral",
"correction.uninf","method.inference","method.score","strata","threshold","restriction","weightObs","weightEndpoint","pool.strata","n.resampling")
mycall2 <- setNames(as.list(mycall),names(mycall))
if(!missing(formula)){
mycall2$formula <- formula ## change name of the variable into actual value
}
mycall2$data <- data ## change name of the variable into actual value
BuyseTest.object <- do.call("S4BuyseTest", args = c(list(call = mycall2),
outPoint, outArgs[keep.args], outResampling))
if(outArgs$trace > 1){
cat("\n")
}
## ** export
return(BuyseTest.object)
}
## * .BuyseTest (code)
.BuyseTest <- function(envir,
iid,
method.inference,
pointEstimation){
## ** Resampling
outSample <- calcSample(envir = envir, method.inference = method.inference)
if(is.null(outSample)){return(NULL)}
## ** Estimate survival curves with its iid
if(envir$outArgs$scoring.rule == 0){ ## Gehan
outSurv <- envir$outArgs$skeletonPeron
}else{ ## Peron
outSurv <- calcPeron(data = outSample$data,
model.tte = envir$outArgs$model.tte,
method.score = envir$outArgs$method.score,
treatment = envir$outArgs$treatment,
level.treatment = envir$outArgs$level.treatment,
endpoint = envir$outArgs$endpoint,
endpoint.TTE = envir$outArgs$endpoint.TTE,
endpoint.UTTE = envir$outArgs$endpoint.UTTE,
status = envir$outArgs$status,
status.TTE = envir$outArgs$status.TTE,
status.UTTE = envir$outArgs$status.UTTE,
D.TTE = envir$outArgs$D.TTE,
D.UTTE = envir$outArgs$D.UTTE,
threshold = envir$outArgs$threshold,
restriction = envir$outArgs$restriction,
level.strata = envir$outArgs$level.strata,
n.strata = envir$outArgs$n.strata,
strata = envir$outArgs$strata,
precompute = envir$outArgs$precompute,
iidNuisance = envir$outArgs$iidNuisance * iid,
out = envir$outArgs$skeletonPeron,
fitter = envir$outArgs$fitter.model.tte,
args = envir$outArgs$args.model.tte)
index.test <- which(envir$outArgs$method.score == "SurvPeron")
if(!grepl("permutation|bootstrap",method.inference) && envir$outArgs$correction.uninf>0 && length(index.test)>0 && all(is.na(envir$outArgs$restriction))){
maxLastSurv <- setNames(sapply(outSurv$lastSurv[index.test],max),envir$outArgs$endpoint[index.test])[!duplicated(envir$outArgs$endpoint[index.test])]
Wtau <- BuyseTest.options("warning.correction")
if(any(maxLastSurv>Wtau)){
warning("Some of the survival curves for endpoint(s) \"",paste(names(which(maxLastSurv>Wtau)),collapse = "\", \""),"\" are unknown beyond a survival of ",Wtau,".\n",
"The correction of uninformative pairs assume that uninformative pairs would on average behave like informative pairs. \n",
"This can be a strong assumption and have substantial impact when the tail of the survival curve is unknown. \n")
}
}
}
## ** Restriction
if(any(!is.na(envir$outArgs$restriction))){
## index restricted endpoint
index.rendpoint <- setdiff(which(!is.na(envir$outArgs$restriction)), ## non-NA value
which(duplicated(envir$outArgs$index.endpoint))) ## not already visitied
for(iE in index.rendpoint){ ## iE <- 1
iRestriction <- envir$outArgs$restriction[iE]
if(envir$outArgs$operator[iE]==1){ ## ">0"
if(envir$outArgs$method.score[iE] %in% c("TTEgehan","SurvPeron","CRPeron")){ ## right censoring
envir$outArgs$M.status[envir$outArgs$M.endpoint[,iE]>iRestriction,iE] <- 1/2
}
envir$outArgs$M.endpoint[envir$outArgs$M.endpoint[,iE]>iRestriction,iE] <- iRestriction
}else if(envir$outArgs$operator[iE]==-1){ ## "<0"
if(envir$outArgs$method.score[iE] %in% c("TTEgehan2")){ ## left censoring
envir$outArgs$M.status[envir$outArgs$M.endpoint[,iE]<iRestriction,iE] <- 1/2
}
envir$outArgs$M.endpoint[envir$outArgs$M.endpoint[,iE]<iRestriction,iE] <- iRestriction
}
}
}
## ** Perform GPC
resBT <- do.call(envir$outArgs$engine,
args = list(endpoint = envir$outArgs$M.endpoint,
status = envir$outArgs$M.status,
indexC = outSample$ls.indexC,
posC = outSample$ls.posC,
indexT = outSample$ls.indexT,
posT = outSample$ls.posT,
threshold = envir$outArgs$threshold,
restriction = envir$outArgs$restriction,
weightEndpoint = envir$outArgs$weightEndpoint,
weightObs = envir$outArgs$weightObs,
method = sapply(envir$outArgs$method.score, switch, "continuous" = 1, "gaussian" = 2, "TTEgehan" = 3, "TTEgehan2" = 4, "SurvPeron" = 5, "CRPeron" = 6),
pool = envir$outArgs$pool.strata,
op = envir$outArgs$operator,
D = envir$outArgs$D,
D_UTTE = envir$outArgs$D.UTTE,
n_strata = envir$outArgs$n.strata,
nUTTE_analyzedPeron_M1 = envir$outArgs$nUTTE.analyzedPeron_M1,
index_endpoint = envir$outArgs$index.endpoint,
index_status = envir$outArgs$index.status,
index_UTTE = envir$outArgs$index.UTTE,
list_survTimeC = outSurv$survTimeC,
list_survTimeT = outSurv$survTimeT,
list_survJumpC = outSurv$survJumpC,
list_survJumpT = outSurv$survJumpT,
list_lastSurv = outSurv$lastSurv,
p_C = outSurv$p.C,
p_T = outSurv$p.T,
iid_survJumpC = outSurv$iid$survJumpC,
iid_survJumpT = outSurv$iid$survJumpT,
zeroPlus = 1e-8,
correctionUninf = envir$outArgs$correction.uninf,
hierarchical = envir$outArgs$hierarchical,
hprojection = envir$outArgs$order.Hprojection,
neutralAsUninf = envir$outArgs$neutral.as.uninf,
addHalfNeutral = envir$outArgs$add.halfNeutral,
keepScore = (pointEstimation && envir$outArgs$keep.pairScore),
precompute = envir$outArgs$precompute,
returnIID = c(iid,envir$outArgs$iidNuisance),
debug = envir$outArgs$debug
))
## ** export
if(pointEstimation){
if(envir$outArgs$keep.survival){ ## useful to test initSurvival
resBT$tableSurvival <- outSurv
}
return(resBT)
}else{
## index <- 5
## resBT$Delta[,index]
## sum(resBT$delta[,,index][,1] * resBT$weightStrata)
return(list(delta = resBT$delta,
Delta = resBT$Delta,
weightStrata = resBT$weightStrata,
covariance = resBT$covariance))
}
}
## * calcSample
#' @rdname internal-initialization
calcSample <- function(envir, method.inference){
## ** initialization
out <- list(## rows in M.endpoint/M.status corresponding to observations from the control/treatment group (not unique when boostraping)
ls.indexC = vector(mode = "list", length = envir$outArgs$n.strata),
ls.indexT = vector(mode = "list", length = envir$outArgs$n.strata),
## identifier for each observation from the control/treatment group (unique even when boostrap)
ls.posC = vector(mode = "list", length = envir$outArgs$n.strata),
ls.posT = vector(mode = "list", length = envir$outArgs$n.strata),
## dataset
data = data.table::data.table()
)
if(method.inference %in% c("none","u-statistic")){
## ** no resampling
if(envir$outArgs$n.strata==1){
out$ls.indexC[[1]] <- envir$outArgs$index.C - 1
out$ls.indexT[[1]] <- envir$outArgs$index.T - 1
}else{
for(iStrata in 1:envir$outArgs$n.strata){ ## iStrata <- 1
out$ls.indexC[[iStrata]] <- intersect(envir$outArgs$index.C, envir$outArgs$index.strata[[iStrata]]) - 1
out$ls.indexT[[iStrata]] <- intersect(envir$outArgs$index.T, envir$outArgs$index.strata[[iStrata]]) - 1
}
}
out$ls.posC <- out$ls.indexC
out$ls.posT <- out$ls.indexT
if(envir$outArgs$scoring.rule>0){
out$data <- data.table::data.table(envir$outArgs$data,
envir$outArgs$M.endpoint,
envir$outArgs$M.status)
}
}else{
## ** stratified resampling
n.strataResampling <- length(envir$outArgs$n.obsStrataResampling)
index.resampling <- NULL
for (iSR in 1:n.strataResampling) {
index.resampling <- c(index.resampling,
envir$outArgs$cumn.obsStrataResampling[iSR] + sample.int(envir$outArgs$n.obsStrataResampling[iSR], replace = grepl("bootstrap",method.inference)))
}
## ** reconstruct groups
## index: index of the new observations in the old dataset by treatment group
## pos: unique identifier for each observation
if(envir$outArgs$n.strata==1){ ## no strata
if(grepl("permutation",method.inference)){
out$ls.indexC[[1]] <- which(index.resampling %in% envir$outArgs$index.C) - 1
out$ls.indexT[[1]] <- which(index.resampling %in% envir$outArgs$index.T) - 1
out$ls.posC[[1]] <- out$ls.indexC[[1]]
out$ls.posT[[1]] <- out$ls.indexT[[1]]
}else if(grepl("bootstrap",method.inference)){
out$ls.posC[[1]] <- which(index.resampling %in% envir$outArgs$index.C) - 1
out$ls.posT[[1]] <- which(index.resampling %in% envir$outArgs$index.T) - 1
out$ls.indexC[[1]] <- index.resampling[out$ls.posC[[1]] + 1] - 1
out$ls.indexT[[1]] <- index.resampling[out$ls.posT[[1]] + 1] - 1
}
## check that each group has at least one observation
if(length(out$ls.indexC[[1]])==0 || length(out$ls.indexT[[1]])==0){return(NULL)}
## out$data[treatment == 0,eventtime1] - envir$outArgs$M.endpoint[out$ls.indexC[[1]]+1,1]
## out$data[treatment == 1,eventtime1] - envir$outArgs$M.endpoint[out$ls.indexT[[1]]+1,1]
}else{ ## strata
if (grepl("permutation",method.inference)) {
index.C <- which(index.resampling %in% envir$outArgs$index.C)
index.T <- which(index.resampling %in% envir$outArgs$index.T)
}
for(iStrata in 1:envir$outArgs$n.strata){ ## iStrata <- 1
## index of the new observation in the old dataset by treatment group
if(grepl("permutation",method.inference)){
out$ls.indexC[[iStrata]] <- intersect(index.C, envir$outArgs$index.strata[[iStrata]]) - 1
out$ls.indexT[[iStrata]] <- intersect(index.T, envir$outArgs$index.strata[[iStrata]]) - 1
out$ls.posC[[iStrata]] <- out$ls.indexC[[iStrata]]
out$ls.posT[[iStrata]] <- out$ls.indexT[[iStrata]]
}else if(grepl("bootstrap",method.inference)){
out$ls.posC[[iStrata]] <- which(index.resampling %in% intersect(envir$outArgs$index.C, envir$outArgs$index.strata[[iStrata]])) - 1
out$ls.posT[[iStrata]] <- which(index.resampling %in% intersect(envir$outArgs$index.T, envir$outArgs$index.strata[[iStrata]])) - 1
out$ls.indexC[[iStrata]] <- index.resampling[out$ls.posC[[iStrata]] + 1] - 1
out$ls.indexT[[iStrata]] <- index.resampling[out$ls.posT[[iStrata]] + 1] - 1
}
## check that each group has at least one observation
if(length(out$ls.indexC[[iStrata]])==0 || length(out$ls.indexT[[iStrata]])==0){return(NULL)}
}
}
## ** rebuild dataset
if(envir$outArgs$scoring.rule>0){
if(grepl("permutation",method.inference)){
out$data <- data.table::data.table(envir$outArgs$data[[envir$outArgs$treatment]][index.resampling],
"..strata.." = envir$outArgs$data[["..strata.."]],
envir$outArgs$M.endpoint,envir$outArgs$M.status)
data.table::setnames(out$data, old = names(out$data)[1], new = envir$outArgs$treatment)
}else{
out$data <- data.table::data.table(envir$outArgs$data[,.SD,.SDcols = c(envir$outArgs$treatment,"..strata..")],
envir$outArgs$M.endpoint,
envir$outArgs$M.status)[index.resampling]
}
}
}
return(out)
}
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Defines functions calcSample .BuyseTest BuyseTest
Documented in BuyseTest calcSample
## * Documentation - BuyseTest
#' @name BuyseTest
#' @title Generalized Pairwise Comparisons (GPC)
#'
#' @description Performs Generalized Pairwise Comparisons for binary, continuous and time-to-event endpoints.
#'
#' @param formula [formula] a symbolic description of the GPC model,
#' typically \code{treatment ~ type1(endpoint1) + type2(endpoint2, threshold2) + strata}.
#' See Details, section "Specification of the GPC model".
#' @param treatment,endpoint,type,threshold,status,operator,censoring,restriction,strata Alternative to \code{formula} for describing the GPC model.
#' See Details, section "Specification of the GPC model".
#' @param data [data.frame] dataset.
#' @param scoring.rule [character] method used to compare the observations of a pair in presence of right censoring (i.e. \code{"timeToEvent"} endpoints).
#' Can be \code{"Gehan"} or \code{"Peron"}.
#' See Details, section "Handling missing values".
#' @param pool.strata [character] weights used to combine estimates across strata. Can be
#' \code{"Buyse"} to weight proportionnally to the number of pairs in the strata,
#' \code{"CMH"} to weight proportionnally to the ratio between the number of pairs in the strata and the number of observations in the strata.
#' \code{"equal"} to weight equally each strata,
#' or \code{"var-netBenefit"} to weight each strata proportionally to the precision of its estimated net benefit (similar syntax for the win ratio: \code{"var-winRatio"})
#' @param correction.uninf [integer] should a correction be applied to remove the bias due to the presence of uninformative pairs?
#' 0 indicates no correction, 1 impute the average score of the informative pairs, and 2 performs IPCW.
#' See Details, section "Handling missing values".
#' @param model.tte [list] optional survival models relative to each time to each time to event endpoint.
#' Models must \code{prodlim} objects and stratified on the treatment and strata variable. When used, the uncertainty from the estimates of these survival models is ignored.
#' @param method.inference [character] method used to compute confidence intervals and p-values.
#' Can be \code{"none"}, \code{"u-statistic"}, \code{"permutation"}, \code{"studentized permutation"}, \code{"bootstrap"}, \code{"studentized bootstrap"}.
#' See Details, section "Statistical inference".
#' @param n.resampling [integer] the number of permutations/samples used for computing the confidence intervals and the p.values.
#' See Details, section "Statistical inference".
#' @param strata.resampling [character] the variable on which the permutation/sampling should be stratified.
#' See Details, section "Statistical inference".
#' @param hierarchical [logical] should only the uninformative pairs be analyzed at the lower priority endpoints (hierarchical GPC)?
#' Otherwise all pairs will be compaired for all endpoint (full GPC).
#' @param weightEndpoint [numeric vector] weights used to cumulating the pairwise scores over the endpoints.
#' Only used when \code{hierarchical=FALSE}. Disregarded if the argument \code{formula} is defined.
#' @param weightObs [character or numeric vector] weights or variable in the dataset containing the weight associated to each observation.
#' These weights are only considered when performing GPC (but not when fitting surival models).
#' @param neutral.as.uninf [logical vector] should paired classified as neutral be re-analyzed using endpoints of lower priority (as it is done for uninformative pairs).
#' See Details, section "Handling missing values".
#' @param add.halfNeutral [logical] should half of the neutral score be added to the favorable and unfavorable scores?
#' @param keep.pairScore [logical] should the result of each pairwise comparison be kept?
#' @param seed [integer, >0] the seed to consider when performing resampling.
#' If \code{NULL} no seed is set.
#' @param cpus [integer, >0] the number of CPU to use.
#' Only the permutation test can use parallel computation.
#' See Details, section "Statistical inference".
#' @param trace [integer] should the execution of the function be traced ? \code{0} remains silent
#' and \code{1}-\code{3} correspond to a more and more verbose output in the console.
#'
#' @details
#'
#' \bold{Specification of the GPC model}: \cr
#' There are two way to specify the GPC model in \code{BuyseTest}.
#' A \emph{Formula interface} via the argument \code{formula} where the response variable should be a binary variable defining the treatment arms.
#' The rest of the formula should indicate the endpoints by order of priority and the strata variables (if any).
#' A \emph{Vector interface} using the following arguments \itemize{
#' \item \code{treatment}: [character] name of the treatment variable identifying the control and the experimental group.
#' Must have only two levels (e.g. \code{0} and \code{1}).
#' \item \code{endpoint}: [character vector] the name of the endpoint variable(s).
#' \item \code{threshold}: [numeric vector] critical values used to compare the pairs (threshold of minimal important difference).
#' A pair will be classified as neutral if the difference in endpoint is strictly below this threshold.
#' There must be one threshold for each endpoint variable; it must be \code{NA} for binary endpoints and positive for continuous or time to event endpoints.
#' \item \code{status}: [character vector] the name of the binary variable(s) indicating whether the endpoint was observed or censored.
#' Must value \code{NA} when the endpoint is not a time to event.
#' \item \code{operator}: [character vector] the sign defining a favorable endpoint.
#' \code{">0"} indicates that higher values are favorable while "<0" indicates the opposite.
#' \item \code{type}: [character vector] indicates whether it is
#' a binary outcome (\code{"b"}, \code{"bin"}, or \code{"binary"}),
#' a continuous outcome (\code{"c"}, \code{"cont"}, or \code{"continuous"}),
#' or a time to event outcome (\code{"t"}, \code{"tte"}, \code{"time"}, or \code{"timetoevent"})
#' \item \code{censoring}: [character vector] is the endpoint subject to right or left censoring (\code{"left"} or \code{"right"}). The default is right-censoring.
#' \item \code{restriction}: [numeric vector] value above which any difference is classified as neutral.
#' \item \code{strata}: [character vector] if not \code{NULL}, the GPC will be applied within each group of patient defined by the strata variable(s).
#' }
#' The formula interface can be more concise, especially when considering few outcomes, but may be more difficult to apprehend for new users.
#' Note that arguments \code{endpoint}, \code{threshold}, \code{status}, \code{operator}, \code{type}, and \code{censoring} must have the same length. \cr \cr \cr
#'
#'
#' \bold{GPC procedure} \cr
#' The GPC procedure form all pairs of observations, one belonging to the experimental group and the other to the control group, and class them in 4 categories: \itemize{
#' \item \emph{Favorable pair}: the endpoint is better for the observation in the experimental group.
#' \item \emph{Unfavorable pair}: the endpoint is better for the observation in the control group.
#' \item \emph{Neutral pair}: the difference between the endpoints of the two observations is (in absolute value) below the threshold. When \code{threshold=0}, neutral pairs correspond to pairs with equal endpoint. Lower-priority outcomes (if any) are then used to classified the pair into favorable/unfavorable.
#' \item \emph{Uninformative pair}: censoring/missingness prevents from classifying into favorable, unfavorable or neutral.
#' }
#' With complete data, pairs can be decidely classified as favorable/unfavorable/neutral.
#' In presence of missing values, the GPC procedure uses the scoring rule (argument \code{scoring.rule}) and the correction for uninformative pairs (argument \code{correction.uninf}) to classify the pairs.
#' The classification may not be 0,1, e.g. the probability that the pair is favorable/unfavorable/neutral with the Peron's scoring rule.
#' To export the classification of each pair set the argument code{keep.pairScore} to \code{TRUE} and call the function \code{getPairScore} on the result of the \code{BuyseTest} function. \cr \cr \cr
#'
#'
#' \bold{Handling missing values}
#' \itemize{
#' \item \code{scoring.rule}: indicates how to handle right-censoring in time to event endpoints using information from the survival curves.
#' The Gehan's scoring rule (argument \code{scoring.rule="Gehan"}) only scores pairs that can be decidedly classified as favorable, unfavorable, or neutral
#' while the "Peron"'s scoring rule (argument \code{scoring.rule="Peron"}) uses the empirical survival curves of each group to also score the pairs that cannot be decidedly classified.
#' The Peron's scoring rule is the recommanded scoring rule but only handles right-censoring.
#' \item \code{correction.uninf}: indicates how to handle missing values that could not be classified by the scoring rule. \describe{
#' \item{\code{correction.uninf=0}}{ treat them as uninformative: this is an equivalent to complete case analysis when \code{neutral.as.uninf=FALSE}, while when \code{neutral.as.uninf=TRUE}, uninformative pairs are treated as neutral, i.e., analyzed at the following endpoint (if any). This approach will (generally) lead to biased estimates for the proportion of favorable, unfavorable, or neutral pairs.}
#' \item{\code{correction.uninf=1}}{ imputes to the uninformative pairs the average score of the informative pairs, i.e. assumes that uninformative pairs would on average behave like informative pairs. This is therefore the recommanded approach when this assumption is resonnable, typically when the the tail of the survival function estimated by the Kaplan–Meier method is close to 0.}
#' \item{\code{correction.uninf=2}}{ uses inverse probability of censoring weights (IPCW), i.e. up-weight informative pairs to represent uninformative pairs. It also assumes that uninformative pairs would on average behave like informative pairs and is only recommanded when the analysis is stopped after the first endpoint with uninformative pairs.}
#' }
#' Note that both corrections will convert the whole proportion of uninformative pairs of a given endpoint into favorable, unfavorable, or neutral pairs. See Peron et al (2021) for further details and recommandations \cr \cr
#' }
#'
#'
#' \bold{Statistical inference} \cr
#' The argument \code{method.inference} defines how to approximate the distribution of the GPC estimators and so how standard errors, confidence intervals, and p-values are computed.
#' Available methods are:
#' \itemize{
#' \item argument \code{method.inference="none"}: only the point estimate is computed which makes the execution of the \code{BuyseTest} faster than with the other methods.
#' \item argument \code{method.inference="u-statistic"}: uses a Gaussian approximation to obtain the distribution of the GPC estimators.
#' The U-statistic theory indicates that this approximation is asymptotically exact.
#' The variance is computed using a H-projection of order 1 (default option), which is a consistent but downward biased estimator.
#' An unbiased estimator can be obtained using a H-projection of order 2 (only available for the uncorrected Gehan's scoring rule, see \code{BuyseTest.options}).
#' \bold{WARNING}: the current implementation of the H-projection is not valid when using corrections for uninformative pairs (\code{correction.uninf=1}, or \code{correction.uninf=2}).
#' \item argument \code{method.inference="permutation"}: perform a permutation test, estimating in each sample the summary statistics (net benefit, win ratio).
#' \item argument \code{method.inference="studentized permutation"}: perform a permutation test, estimating in each sample the summary statistics (net benefit, win ratio) and the variance-covariance matrix of the estimate.
#' \item argument \code{method.inference="bootstrap"}: perform a non-parametric boostrap, estimating in each sample the summary statistics (net benefit, win ratio).
#' \item argument \code{method.inference=" studentized bootstrap"}: perform a non-parametric boostrap, estimating in each sample the summary statistics (net benefit, win ratio) and the variance-covariance matrix of the estimator.
#' }
#' Additional arguments for permutation and bootstrap resampling:
#' \itemize{
#' \item \code{strata.resampling} If \code{NA} or of length 0, the permutation/non-parametric boostrap will be performed by resampling in the whole sample.
#' Otherwise, the permutation/non-parametric boostrap will be performed separately for each level that the variable defined in \code{strata.resampling} take.
#' \item \code{n.resampling} set the number of permutations/samples used.
#' A large number of permutations (e.g. \code{n.resampling=10000}) are needed to obtain accurate CI and p.value. See (Buyse et al., 2010) for more details.
#' \item \code{cpus} indicates whether the resampling procedure can be splitted on several cpus to save time. Can be set to \code{"all"} to use all available cpus.
#' The detection of the number of cpus relies on the \code{detectCores} function from the \emph{parallel} package. \cr \cr
#' }
#'
#' \bold{Pooling results across strata} the relative contribution of each strata to the global estimator is decided by \cr
#' \itemize{
#' \item \code{"CMH"} weights: Cochran-Mantel-Haenszel type weights which are optimal if the odds ratios are constant across strata.
#' \item \code{"Buyse"} weights: optimal if the risk difference is constant across strata. \cr \cr
#' }
#'
#' \bold{Default values} \cr
#' The default of the arguments
#' \code{scoring.rule}, \code{correction.uninf}, \code{method.inference}, \code{n.resampling},
#' \code{hierarchical}, \code{neutral.as.uninf}, \code{keep.pairScore}, \code{strata.resampling},
#' \code{cpus}, \code{trace} is read from \code{BuyseTest.options()}. \cr
#' Additional (hidden) arguments are \itemize{
#' \item \code{alternative} [character] the alternative hypothesis. Must be one of "two.sided", "greater" or "less" (used by \code{confint}).
#' \item \code{conf.level} [numeric] level for the confidence intervals (used by \code{confint}).
#' \item \code{keep.survival} [logical] export the survival values used by the Peron's scoring rule.
#' \item \code{order.Hprojection} [1 or 2] the order of the H-projection used to compute the variance when \code{method.inference="u-statistic"}.
#' }
#'
#' @return An \R object of class \code{\linkS4class{S4BuyseTest}}.
#'
#' @references
#' On the GPC procedure: Marc Buyse (2010). \bold{Generalized pairwise comparisons of prioritized endpoints in the two-sample problem}. \emph{Statistics in Medicine} 29:3245-3257 \cr
#' On the win ratio: D. Wang, S. Pocock (2016). \bold{A win ratio approach to comparing continuous non-normal outcomes in clinical trials}. \emph{Pharmaceutical Statistics} 15:238-245 \cr
#' On the Peron's scoring rule: J. Peron, M. Buyse, B. Ozenne, L. Roche and P. Roy (2018). \bold{An extension of generalized pairwise comparisons for prioritized outcomes in the presence of censoring}. \emph{Statistical Methods in Medical Research} 27: 1230-1239. \cr
#' On the Gehan's scoring rule: Gehan EA (1965). \bold{A generalized two-sample Wilcoxon test for doubly censored data}. \emph{Biometrika} 52(3):650-653 \cr
#' On inference in GPC using the U-statistic theory: Ozenne B, Budtz-Jorgensen E, Peron J (2021). \bold{The asymptotic distribution of the Net Benefit estimator in presence of right-censoring}. \emph{Statistical Methods in Medical Research} 2021 doi:10.1177/09622802211037067 \cr
#' On how to handle right-censoring: J. Peron, M. Idlhaj, D. Maucort-Boulch, et al. (2021) \bold{Correcting the bias of the net benefit estimator due to right-censored observations}. \emph{Biometrical Journal} 63: 893–906.
#'
#' @seealso
#' \code{\link{S4BuyseTest-summary}} for a summary of the results of generalized pairwise comparison. \cr
#' \code{\link{S4BuyseTest-confint}} for exporting estimates with confidence intervals and p-values. \cr
#' \code{\link{S4BuyseTest-class}} for a presentation of the \code{S4BuyseTest} object. \cr
#' \code{\link{S4BuyseTest-sensitivity}} for performing a sensitivity analysis on the choice of the threshold(s). \cr
#' \code{\link{constStrata}} to create a strata variable from several clinical variables. \cr
#' @keywords function BuyseTest
#' @author Brice Ozenne
## * BuyseTest (example)
#' @rdname BuyseTest
#' @examples
#' library(data.table)
#'
#' #### simulate some data ####
#' set.seed(10)
#' df.data <- simBuyseTest(1e2, n.strata = 2)
#'
#' ## display
#' if(require(prodlim)){
#' resKM_tempo <- prodlim(Hist(eventtime,status)~treatment, data = df.data)
#' plot(resKM_tempo)
#' }
#'
#' #### one time to event endpoint ####
#' BT <- BuyseTest(treatment ~ TTE(eventtime, status = status), data= df.data)
#'
#' summary(BT) # net benefit
#' summary(BT, percentage = FALSE)
#' summary(BT, statistic = "winRatio") # win Ratio
#'
#' ## permutation instead of asymptotics to compute the p-value
#' \dontrun{
#' BT <- BuyseTest(treatment ~ TTE(eventtime, status = status), data=df.data,
#' method.inference = "permutation", n.resampling = 1e3)
#' }
#' \dontshow{
#' BT <- BuyseTest(treatment ~ TTE(eventtime, status = status), data=df.data,
#' method.inference = "permutation", n.resampling = 1e1, trace = 0)
#' }
#' summary(BT, statistic = "netBenefit") ## default
#' summary(BT, statistic = "winRatio")
#'
#' ## parallel permutation
#' \dontrun{
#' BT <- BuyseTest(treatment ~ TTE(eventtime, status = status), data=df.data,
#' method.inference = "permutation", n.resampling = 1e3, cpus = 2)
#' summary(BT)
#' }
#'
#' ## method Gehan is much faster but does not optimally handle censored observations
#' BT <- BuyseTest(treatment ~ TTE(eventtime, status = status), data=df.data,
#' scoring.rule = "Gehan", trace = 0)
#' summary(BT)
#'
#' #### one time to event endpoint: only differences in survival over 1 unit ####
#' BT <- BuyseTest(treatment ~ TTE(eventtime, threshold = 1, status = status), data=df.data)
#' summary(BT)
#'
#' #### one time to event endpoint with a strata variable
#' BT <- BuyseTest(treatment ~ strata + TTE(eventtime, status = status), data=df.data)
#' summary(BT)
#'
#' #### several endpoints with a strata variable
#' f <- treatment ~ strata + T(eventtime, status, 1) + B(toxicity)
#' f <- update(f,
#' ~. + T(eventtime, status, 0.5) + C(score, 1) + T(eventtime, status, 0.25))
#'
#' BT <- BuyseTest(f, data=df.data)
#' summary(BT)
#'
#' #### real example : veteran dataset of the survival package ####
#' ## Only one endpoint. Type = Time-to-event. Thresold = 0. Stratfication by histological subtype
#' ## scoring.rule = "Gehan"
#'
#' if(require(survival)){
#' \dontrun{
#' library(survival) ## import veteran
#'
#' ## scoring.rule = "Gehan"
#' BT_Gehan <- BuyseTest(trt ~ celltype + TTE(time,threshold=0,status=status),
#' data=veteran, scoring.rule="Gehan")
#'
#' summary_Gehan <- summary(BT_Gehan)
#' summary_Gehan <- summary(BT_Gehan, statistic = "winRatio")
#'
#' ## scoring.rule = "Peron"
#' BT_Peron <- BuyseTest(trt ~ celltype + TTE(time,threshold=0,status=status),
#' data=veteran, scoring.rule="Peron")
#'
#' class(BT_Peron)
#' summary(BT_Peron)
#' }
#' }
## * BuyseTest (code)
##' @export
BuyseTest <- function(formula,
data,
scoring.rule = NULL,
pool.strata = NULL,
correction.uninf = NULL,
model.tte = NULL,
method.inference = NULL,
n.resampling = NULL,
strata.resampling = NULL,
hierarchical = NULL,
weightEndpoint = NULL,
weightObs = NULL,
neutral.as.uninf = NULL,
add.halfNeutral = NULL,
keep.pairScore = NULL,
seed = NULL,
cpus = NULL,
trace = NULL,
treatment = NULL,
endpoint = NULL,
type = NULL,
threshold = NULL,
status = NULL,
operator = NULL,
censoring = NULL,
restriction = NULL,
strata = NULL){
mycall <- match.call()
name.call <- names(mycall)
option <- BuyseTest.options()
## ** compatibility with previous version
if(!is.null(method.inference) && (method.inference=="asymptotic")){
stop("Value \"asymptotic\" for argument \'method.inference\' is obsolete. \n",
"Use \"u-statistic\" instead \n")
}
## ** initialize arguments (all expect data that is just converted to data.table)
## initialized arguments are stored in outArgs
outArgs <- initializeArgs(status = status,
correction.uninf = correction.uninf,
cpus = cpus,
data = data,
endpoint = endpoint,
formula = formula,
hierarchical = hierarchical,
keep.pairScore = keep.pairScore,
method.inference = method.inference,
scoring.rule = scoring.rule,
pool.strata = pool.strata,
model.tte = model.tte,
n.resampling = n.resampling,
strata.resampling = strata.resampling,
name.call = name.call,
neutral.as.uninf = neutral.as.uninf,
add.halfNeutral = add.halfNeutral,
operator = operator,
censoring = censoring,
option = option,
seed = seed,
strata = strata,
threshold = threshold,
restriction = restriction,
trace = trace,
treatment = treatment,
type = type,
weightEndpoint = weightEndpoint,
weightObs = weightObs,
envir = parent.frame())
## ** test arguments
if(option$check){
outTest <- do.call(testArgs, args = outArgs)
}
## ** initialization data
## WARNING when updating code: names in the c() must precisely match output of initializeData, in the same order
out.name <- c("data","M.endpoint","M.status",
"index.C","index.T","weightObs","index.strata",
"level.treatment","level.strata", "method.score",
"n.strata","n.obs","n.obsStrata","n.obsStrataResampling","cumn.obsStrataResampling","skeletonPeron",
"scoring.rule", "iidNuisance", "nUTTE.analyzedPeron_M1", "endpoint.UTTE", "status.UTTE", "D.UTTE","index.UTTE","keep.pairScore")
outArgs[out.name] <- initializeData(data = outArgs$data,
type = outArgs$type,
endpoint = outArgs$endpoint,
Uendpoint = outArgs$Uendpoint,
D = outArgs$D,
scoring.rule = outArgs$scoring.rule,
status = outArgs$status,
Ustatus = outArgs$Ustatus,
method.inference = outArgs$method.inference,
censoring = outArgs$censoring,
strata = outArgs$strata,
treatment = outArgs$treatment,
hierarchical = outArgs$hierarchical,
copy = TRUE,
keep.pairScore = outArgs$keep.pairScore,
endpoint.TTE = outArgs$endpoint.TTE,
status.TTE = outArgs$status.TTE,
iidNuisance = outArgs$iidNuisance,
weightEndpoint = outArgs$weightEndpoint,
weightObs = outArgs$weightObs)
if(option$check){
if(outArgs$iidNuisance && any(outArgs$method.score == "CRPeron")){
warning("Inference via the asymptotic theory for competing risks when using the Peron's scoring rule has not been validating \n",
"Consider setting \'method.inference\' to \"none\", \"bootstrap\", or \"permutation\" \n")
}
## if(outArgs$precompute && any(outArgs$method.score == "CRPeron")){
## stop("Option \'precompute\' is not available for the Peron scoring rule in the competing risk case \n")
## }
if(outArgs$precompute && any(outArgs$method.score == "CRPeron")){
outArgs$precompute <- FALSE
}
}
## ** Display
if (outArgs$trace > 1) {
cat("\n Generalized Pairwise Comparisons\n\n")
do.call(printGeneral, args = outArgs)
cat("\n")
}
## ** define environment
envirBT <- environment()
envirBT$.BuyseTest <- .BuyseTest
envirBT$initializeData <- initializeData
envirBT$calcPeron <- calcPeron
envirBT$outArgs$args.model.tte <- option$args.model.tte
## ** Point estimation
if (outArgs$trace > 1) {
if(outArgs$iid){
cat("Point estimation and calculation of the iid decomposition")
}else{
cat("Point estimation")
}
}
outPoint <- .BuyseTest(envir = envirBT,
iid = outArgs$iid,
method.inference = "none", ## do not use outArgs$method.inference as when it is equal to "bootstrap" or "permutation" we need the point estimate first.
pointEstimation = TRUE)
if (outArgs$trace > 1) {
cat("\n\n")
}
## check number of pairs
if(option$check){
vec.nPair <- (outPoint$count_favorable + outPoint$count_unfavorable + outPoint$count_neutral + outPoint$count_uninf )[,1]
if(any(abs(outPoint$n_pairs - vec.nPair) > 0.01)){
warning("Incorrect estimation of the number of pairs \n",
"Something probably went wrong - contact the package maintainer\n")
}
}
## convert from a list of vector (output of C++) to a list of data.table
if(outArgs$keep.pairScore){
## needed for inference with bebu
outPoint$tableScore <- pairScore2dt(outPoint$tableScore,
level.treatment = outArgs$level.treatment,
level.strata = outArgs$level.strata,
n.strata = outArgs$n.strata,
endpoint = outArgs$endpoint,
threshold = outArgs$threshold,
restriction = outArgs$restriction)
}
## ** Inference
if((outArgs$method.inference != "none") && (outArgs$trace > 1)){
do.call(printInference, args = outArgs)
}
outResampling <- NULL
if(outArgs$method.inference == "u-statistic"){
## done in the C++ code
}else if(outArgs$method.inference == "u-statistic-bebu"){
if(outArgs$keep.pairScore == FALSE){
stop("Argument \'keep.pairScore\' needs to be TRUE when argument \'method.inference\' is \"u-statistic-bebu\" \n")
}
## direct computation of the variance
outCovariance <- inferenceUstatisticBebu(tablePairScore = outPoint$tableScore,
order = option$order.Hprojection,
weightEndpoint = outArgs$weightEndpoint,
n.pairs = outPoint$n_pairs,
n.C = length(envirBT$outArgs$index.C),
n.T = length(envirBT$outArgs$index.T),
level.strata = outArgs$level.strata,
n.strata = outArgs$n.strata,
endpoint = outArgs$endpoint)
outPoint$covariance <- outCovariance$Sigma
attr(outArgs$method.inference,"Hprojection") <- option$order.Hprojection
}else if(grepl("bootstrap|permutation",outArgs$method.inference)){
outResampling <- inferenceResampling(envirBT)
}
if((outArgs$method.inference != "none") && (outArgs$trace > 1)){
cat("\n")
}
## ** Gather results into a S4BuyseTest object
if(outArgs$trace > 1){
cat("Gather the results in a S4BuyseTest object \n")
}
keep.args <- c("index.T", "index.C", "index.strata", "type","endpoint","level.strata","level.treatment","scoring.rule","hierarchical","neutral.as.uninf","add.halfNeutral",
"correction.uninf","method.inference","method.score","strata","threshold","restriction","weightObs","weightEndpoint","pool.strata","n.resampling")
mycall2 <- setNames(as.list(mycall),names(mycall))
if(!missing(formula)){
mycall2$formula <- formula ## change name of the variable into actual value
}
mycall2$data <- data ## change name of the variable into actual value
BuyseTest.object <- do.call("S4BuyseTest", args = c(list(call = mycall2),
outPoint, outArgs[keep.args], outResampling))
if(outArgs$trace > 1){
cat("\n")
}
## ** export
return(BuyseTest.object)
}
## * .BuyseTest (code)
.BuyseTest <- function(envir,
iid,
method.inference,
pointEstimation){
## ** Resampling
outSample <- calcSample(envir = envir, method.inference = method.inference)
if(is.null(outSample)){return(NULL)}
## ** Estimate survival curves with its iid
if(envir$outArgs$scoring.rule == 0){ ## Gehan
outSurv <- envir$outArgs$skeletonPeron
}else{ ## Peron
outSurv <- calcPeron(data = outSample$data,
model.tte = envir$outArgs$model.tte,
method.score = envir$outArgs$method.score,
treatment = envir$outArgs$treatment,
level.treatment = envir$outArgs$level.treatment,
endpoint = envir$outArgs$endpoint,
endpoint.TTE = envir$outArgs$endpoint.TTE,
endpoint.UTTE = envir$outArgs$endpoint.UTTE,
status = envir$outArgs$status,
status.TTE = envir$outArgs$status.TTE,
status.UTTE = envir$outArgs$status.UTTE,
D.TTE = envir$outArgs$D.TTE,
D.UTTE = envir$outArgs$D.UTTE,
threshold = envir$outArgs$threshold,
restriction = envir$outArgs$restriction,
level.strata = envir$outArgs$level.strata,
n.strata = envir$outArgs$n.strata,
strata = envir$outArgs$strata,
precompute = envir$outArgs$precompute,
iidNuisance = envir$outArgs$iidNuisance * iid,
out = envir$outArgs$skeletonPeron,
fitter = envir$outArgs$fitter.model.tte,
args = envir$outArgs$args.model.tte)
index.test <- which(envir$outArgs$method.score == "SurvPeron")
if(!grepl("permutation|bootstrap",method.inference) && envir$outArgs$correction.uninf>0 && length(index.test)>0 && all(is.na(envir$outArgs$restriction))){
maxLastSurv <- setNames(sapply(outSurv$lastSurv[index.test],max),envir$outArgs$endpoint[index.test])[!duplicated(envir$outArgs$endpoint[index.test])]
Wtau <- BuyseTest.options("warning.correction")
if(any(maxLastSurv>Wtau)){
warning("Some of the survival curves for endpoint(s) \"",paste(names(which(maxLastSurv>Wtau)),collapse = "\", \""),"\" are unknown beyond a survival of ",Wtau,".\n",
"The correction of uninformative pairs assume that uninformative pairs would on average behave like informative pairs. \n",
"This can be a strong assumption and have substantial impact when the tail of the survival curve is unknown. \n")
}
}
}
## ** Restriction
if(any(!is.na(envir$outArgs$restriction))){
## index restricted endpoint
index.rendpoint <- setdiff(which(!is.na(envir$outArgs$restriction)), ## non-NA value
which(duplicated(envir$outArgs$index.endpoint))) ## not already visitied
for(iE in index.rendpoint){ ## iE <- 1
iRestriction <- envir$outArgs$restriction[iE]
if(envir$outArgs$operator[iE]==1){ ## ">0"
if(envir$outArgs$method.score[iE] %in% c("TTEgehan","SurvPeron","CRPeron")){ ## right censoring
envir$outArgs$M.status[envir$outArgs$M.endpoint[,iE]>iRestriction,iE] <- 1/2
}
envir$outArgs$M.endpoint[envir$outArgs$M.endpoint[,iE]>iRestriction,iE] <- iRestriction
}else if(envir$outArgs$operator[iE]==-1){ ## "<0"
if(envir$outArgs$method.score[iE] %in% c("TTEgehan2")){ ## left censoring
envir$outArgs$M.status[envir$outArgs$M.endpoint[,iE]<iRestriction,iE] <- 1/2
}
envir$outArgs$M.endpoint[envir$outArgs$M.endpoint[,iE]<iRestriction,iE] <- iRestriction
}
}
}
## ** Perform GPC
resBT <- do.call(envir$outArgs$engine,
args = list(endpoint = envir$outArgs$M.endpoint,
status = envir$outArgs$M.status,
indexC = outSample$ls.indexC,
posC = outSample$ls.posC,
indexT = outSample$ls.indexT,
posT = outSample$ls.posT,
threshold = envir$outArgs$threshold,
restriction = envir$outArgs$restriction,
weightEndpoint = envir$outArgs$weightEndpoint,
weightObs = envir$outArgs$weightObs,
method = sapply(envir$outArgs$method.score, switch, "continuous" = 1, "gaussian" = 2, "TTEgehan" = 3, "TTEgehan2" = 4, "SurvPeron" = 5, "CRPeron" = 6),
pool = envir$outArgs$pool.strata,
op = envir$outArgs$operator,
D = envir$outArgs$D,
D_UTTE = envir$outArgs$D.UTTE,
n_strata = envir$outArgs$n.strata,
nUTTE_analyzedPeron_M1 = envir$outArgs$nUTTE.analyzedPeron_M1,
index_endpoint = envir$outArgs$index.endpoint,
index_status = envir$outArgs$index.status,
index_UTTE = envir$outArgs$index.UTTE,
list_survTimeC = outSurv$survTimeC,
list_survTimeT = outSurv$survTimeT,
list_survJumpC = outSurv$survJumpC,
list_survJumpT = outSurv$survJumpT,
list_lastSurv = outSurv$lastSurv,
p_C = outSurv$p.C,
p_T = outSurv$p.T,
iid_survJumpC = outSurv$iid$survJumpC,
iid_survJumpT = outSurv$iid$survJumpT,
zeroPlus = 1e-8,
correctionUninf = envir$outArgs$correction.uninf,
hierarchical = envir$outArgs$hierarchical,
hprojection = envir$outArgs$order.Hprojection,
neutralAsUninf = envir$outArgs$neutral.as.uninf,
addHalfNeutral = envir$outArgs$add.halfNeutral,
keepScore = (pointEstimation && envir$outArgs$keep.pairScore),
precompute = envir$outArgs$precompute,
returnIID = c(iid,envir$outArgs$iidNuisance),
debug = envir$outArgs$debug
))
## ** export
if(pointEstimation){
if(envir$outArgs$keep.survival){ ## useful to test initSurvival
resBT$tableSurvival <- outSurv
}
return(resBT)
}else{
## index <- 5
## resBT$Delta[,index]
## sum(resBT$delta[,,index][,1] * resBT$weightStrata)
return(list(delta = resBT$delta,
Delta = resBT$Delta,
weightStrata = resBT$weightStrata,
covariance = resBT$covariance))
}
}
## * calcSample
#' @rdname internal-initialization
calcSample <- function(envir, method.inference){
## ** initialization
out <- list(## rows in M.endpoint/M.status corresponding to observations from the control/treatment group (not unique when boostraping)
ls.indexC = vector(mode = "list", length = envir$outArgs$n.strata),
ls.indexT = vector(mode = "list", length = envir$outArgs$n.strata),
## identifier for each observation from the control/treatment group (unique even when boostrap)
ls.posC = vector(mode = "list", length = envir$outArgs$n.strata),
ls.posT = vector(mode = "list", length = envir$outArgs$n.strata),
## dataset
data = data.table::data.table()
)
if(method.inference %in% c("none","u-statistic")){
## ** no resampling
if(envir$outArgs$n.strata==1){
out$ls.indexC[[1]] <- envir$outArgs$index.C - 1
out$ls.indexT[[1]] <- envir$outArgs$index.T - 1
}else{
for(iStrata in 1:envir$outArgs$n.strata){ ## iStrata <- 1
out$ls.indexC[[iStrata]] <- intersect(envir$outArgs$index.C, envir$outArgs$index.strata[[iStrata]]) - 1
out$ls.indexT[[iStrata]] <- intersect(envir$outArgs$index.T, envir$outArgs$index.strata[[iStrata]]) - 1
}
}
out$ls.posC <- out$ls.indexC
out$ls.posT <- out$ls.indexT
if(envir$outArgs$scoring.rule>0){
out$data <- data.table::data.table(envir$outArgs$data,
envir$outArgs$M.endpoint,
envir$outArgs$M.status)
}
}else{
## ** stratified resampling
n.strataResampling <- length(envir$outArgs$n.obsStrataResampling)
index.resampling <- NULL
for (iSR in 1:n.strataResampling) {
index.resampling <- c(index.resampling,
envir$outArgs$cumn.obsStrataResampling[iSR] + sample.int(envir$outArgs$n.obsStrataResampling[iSR], replace = grepl("bootstrap",method.inference)))
}
## ** reconstruct groups
## index: index of the new observations in the old dataset by treatment group
## pos: unique identifier for each observation
if(envir$outArgs$n.strata==1){ ## no strata
if(grepl("permutation",method.inference)){
out$ls.indexC[[1]] <- which(index.resampling %in% envir$outArgs$index.C) - 1
out$ls.indexT[[1]] <- which(index.resampling %in% envir$outArgs$index.T) - 1
out$ls.posC[[1]] <- out$ls.indexC[[1]]
out$ls.posT[[1]] <- out$ls.indexT[[1]]
}else if(grepl("bootstrap",method.inference)){
out$ls.posC[[1]] <- which(index.resampling %in% envir$outArgs$index.C) - 1
out$ls.posT[[1]] <- which(index.resampling %in% envir$outArgs$index.T) - 1
out$ls.indexC[[1]] <- index.resampling[out$ls.posC[[1]] + 1] - 1
out$ls.indexT[[1]] <- index.resampling[out$ls.posT[[1]] + 1] - 1
}
## check that each group has at least one observation
if(length(out$ls.indexC[[1]])==0 || length(out$ls.indexT[[1]])==0){return(NULL)}
## out$data[treatment == 0,eventtime1] - envir$outArgs$M.endpoint[out$ls.indexC[[1]]+1,1]
## out$data[treatment == 1,eventtime1] - envir$outArgs$M.endpoint[out$ls.indexT[[1]]+1,1]
}else{ ## strata
if (grepl("permutation",method.inference)) {
index.C <- which(index.resampling %in% envir$outArgs$index.C)
index.T <- which(index.resampling %in% envir$outArgs$index.T)
}
for(iStrata in 1:envir$outArgs$n.strata){ ## iStrata <- 1
## index of the new observation in the old dataset by treatment group
if(grepl("permutation",method.inference)){
out$ls.indexC[[iStrata]] <- intersect(index.C, envir$outArgs$index.strata[[iStrata]]) - 1
out$ls.indexT[[iStrata]] <- intersect(index.T, envir$outArgs$index.strata[[iStrata]]) - 1
out$ls.posC[[iStrata]] <- out$ls.indexC[[iStrata]]
out$ls.posT[[iStrata]] <- out$ls.indexT[[iStrata]]
}else if(grepl("bootstrap",method.inference)){
out$ls.posC[[iStrata]] <- which(index.resampling %in% intersect(envir$outArgs$index.C, envir$outArgs$index.strata[[iStrata]])) - 1
out$ls.posT[[iStrata]] <- which(index.resampling %in% intersect(envir$outArgs$index.T, envir$outArgs$index.strata[[iStrata]])) - 1
out$ls.indexC[[iStrata]] <- index.resampling[out$ls.posC[[iStrata]] + 1] - 1
out$ls.indexT[[iStrata]] <- index.resampling[out$ls.posT[[iStrata]] + 1] - 1
}
## check that each group has at least one observation
if(length(out$ls.indexC[[iStrata]])==0 || length(out$ls.indexT[[iStrata]])==0){return(NULL)}
}
}
## ** rebuild dataset
if(envir$outArgs$scoring.rule>0){
if(grepl("permutation",method.inference)){
out$data <- data.table::data.table(envir$outArgs$data[[envir$outArgs$treatment]][index.resampling],
"..strata.." = envir$outArgs$data[["..strata.."]],
envir$outArgs$M.endpoint,envir$outArgs$M.status)
data.table::setnames(out$data, old = names(out$data)[1], new = envir$outArgs$treatment)
}else{
out$data <- data.table::data.table(envir$outArgs$data[,.SD,.SDcols = c(envir$outArgs$treatment,"..strata..")],
envir$outArgs$M.endpoint,
envir$outArgs$M.status)[index.resampling]
}
}
}
return(out)
}
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