116 lines
4.4 KiB
R
116 lines
4.4 KiB
R
#'
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#' RCT format for Virtual Twins
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#'
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#' \code{formatRCTDataset} returns dataset that Virtual Twins is able to
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#' analyze.
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#'
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#' This function check these differents topic: Outcome must be binary and a
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#' factor. If numeric with two distincts values, outcome becomes a factor where
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#' the favorable reponse is the second level. Also, outcome is moved on the
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#' first column of \code{dataset}.
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#'
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#' Treatment must have two distinct numeric values, 0 : no treatment, 1 :
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#' treatment. Treatment is moved to the second column.
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#'
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#' Qualitatives variables must be factor. If it has more than two levels, if
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#' running VirtualTwins with interaction, it creates dummy variables.
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#'
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#' @param dataset data.frame representing RCT's
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#' @param outcome.field name of the outcome's field in \code{dataset}
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#' @param treatment.field name of the treatment's field in \code{dataset}
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#' @param interactions logical. If running VirtualTwins with treatment's
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#' interactions, set to TRUE (default value)
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#'
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#' @return return data.frame with good format (explained in details section) to run VirtualTwins
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#'
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#' @examples
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#' \dontrun{
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#' data.format <- formatRCTDataset(data, "outcome", "treatment", TRUE)
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#' }
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#' data(sepsis)
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#' data.format <- formatRCTDataset(sepsis, "survival", "THERAPY", T)
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#'
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#'
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#'
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#'
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#' @export
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formatRCTDataset <- function(dataset, outcome.field, treatment.field, interactions = TRUE){
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if(!is.data.frame(dataset)) stop("Dataset parameter must be data.frame")
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if(!is.character(outcome.field)) stop(sprintf("%s, outcome.field parameter must be a string", outcome.field))
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if(!is.character(treatment.field)) stop(sprintf("%s, treatment.field parameter must be a string", treatment.field))
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if(!outcome.field %in% colnames(dataset)) stop(sprintf("%s must be in data.frame colnames", outcome.field))
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outcome.field.which <- which(outcome.field == colnames(dataset))
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if(!treatment.field %in% colnames(dataset)) stop(sprintf("%s must be in data.frame colnames", treatment.field))
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treatment.field.which <- which(treatment.field == colnames(dataset))
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d <- dataset
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outcome <- d[, outcome.field.which]
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if(!is.factor(outcome)) outcome <- as.factor(outcome)
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if(!length(levels(outcome)) == 2L) stop(sprintf("outcome %s must be binary", outcome.field))
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cat(sprintf("\"%s\" will be the favorable outcome \n", levels(outcome)[2]))
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d[, outcome.field.which] <- outcome
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treatment <- d[, treatment.field.which]
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if(!is.numeric(treatment) & !is.integer(treatment)) treatment <- as.numeric(treatment)
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if(!( length(unique(treatment)) == 2L & all(c(0,1) %in% unique(treatment)) ))
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stop(sprintf("%s, response must be numeric:\n 0 = no treatment \n 1 = treatment \n", treatment.field))
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d[, treatment.field.which] <- treatment
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predictors <- colnames(dataset)[-c(outcome.field.which, treatment.field.which)]
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predictors.next <- vector()
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iter = 1
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for(i in predictors){
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iter <- length(predictors.next)+1
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var <- d[, i]
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if(is.numeric(var) | is.integer(var)){
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predictors.next[iter] <- i
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}
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if(is.character(var)){
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var <- as.factor(var)
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}
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if(is.factor(var)){
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if(length(levels(var))>2){
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if(isTRUE(interactions)){
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cat(sprintf("Creation of dummy variables for %s \n", i))
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for(l in levels(var)){
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n <- paste(i, l, sep = "_")
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d[, n] <- ifelse(var == l, 1, 0)
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predictors.next[iter] <- n
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cat(sprintf("Dummy variable %s created \n", n))
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iter <- iter + 1
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}
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# d <- d[, predictors != i]
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} else {
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if(length(levels(var))>32){
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stop(cat(sprintf("%s has too many levels (superior to 32)", i)))
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}else{
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warning(sprintf("%s has more than 2 levels. Virtual Twins won't be able to run with interactions.", i))
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predictors.next[iter] <- i
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}
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}
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}else if(length(levels(var)) == 2){
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cat(sprintf("%s is two-level factor. It has to be transformed into numeric value : \n", i))
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cat(sprintf("%s becomes 0 \n", levels(var)[1]))
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cat(sprintf("%s becomes 1 \n", levels(var)[2]))
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d[, i] <- ifelse(var == levels(var)[1], 0, 1)
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predictors.next[iter] <- i
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}else{
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cat(sprintf("%s is deleted because only one level", i))
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}
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}
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}
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colnames.order <- c(outcome.field, treatment.field, predictors.next)
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d <- d[, colnames.order]
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return(invisible(d))
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}
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