#' Update TMLEs for Hazard to Cumulative Incidence
#'
#' @description A helper function that maps hazard estimates into estimates of
#' cumulative incidence and updates the "clever covariates" used by the
#' targeted minimum loss-based estimation fluctuation step.
#'
#' @param dataList A list of \code{data.frame} objects.
#' @param allJ Numeric vector indicating the labels of all causes of failure.
#' @param ofInterestJ Numeric vector indicating \code{ftypeOfInterest} that was
#' passed to \code{\link{hazard_tmle}}.
#' @param nJ The number of unique failure types.
#' @param uniqtrt The values of \code{trtOfInterest} passed to
#' \code{\link{mean_tmle}}.
#' @param att A \code{boolean} indicating whether to compute the ATT estimate,
#' instead of treatment specific survival curves. This option only works with
#' two levels of \code{trt} that are labeled with 0 and 1.
#' @param ntrt The number of \code{trt} values of interest.
#' @param t0 The timepoint at which \code{survtmle} was called to evaluate.
#' @param verbose A \code{logical} indicating whether the function should print
#' messages to indicate progress.
#' @param ... Other arguments. Not currently used.
#'
#' @return The function returns a list that is exactly the same as the input
#' \code{dataList}, but with updated columns corresponding with estimated
#' cumulative incidence at each time and estimated "clever covariates" at each
#' time.
updateVariables <- function(dataList, allJ, ofInterestJ,
nJ, uniqtrt, ntrt, t0, att,
verbose, ...) {
dataList[2:(ntrt + 1)] <- lapply(dataList[2:(ntrt + 1)], function(x, allJ) {
# total hazard
Q_dot <- rowSums(cbind(rep(0, nrow(x)), x[, paste0("Q", allJ, "Haz")]))
# survival at t
x$S.t <- unlist(by((1 - Q_dot), x$id, FUN = cumprod))
x$S.t[x$S.t == 0] <- .Machine$double.neg.eps
# survival at t-1
S.tminus1 <- c(1, x$S.t[1:(length(x$S.t) - 1)])
S.tminus1[x$t == 1] <- 1
for (j in ofInterestJ) {
# calculate CIF at time t
x[[paste0("F", j, ".t")]] <- unlist(by(x[, paste0("Q", j, "Haz")] *
S.tminus1, x$id, FUN = cumsum))
}
x
}, allJ = allJ)
# calculate CIF at time t0
for (j in ofInterestJ) {
# TO DO: change this to static memory allocation
Fj.t0.allZ <- vector(mode = "list", length = ntrt)
for (i in 1:ntrt) {
t0.mod <- dataList[[i + 1]]$ftime[1]
Fj.t0.allZ[[i]] <-
dataList[[i + 1]][[paste0("F", j, ".t")]][dataList[[i + 1]]$t == t0.mod]
}
dataList <- lapply(dataList, function(x, j, uniqtrt, Fj.t0.allZ) {
for (i in seq_along(uniqtrt)) {
# browser()
# ind <- tapply(X = x$id, INDEX = x$id, FUN = NULL)
x[[paste0("F", j, ".z", uniqtrt[i], ".t0")]] <- Fj.t0.allZ[[i]][x$id]
}
x
}, j = j, uniqtrt = uniqtrt, Fj.t0.allZ = Fj.t0.allZ)
}
# merge into dataList[[1]]
# indicators of S.t and Fj.t for dataList[[1]]
colInd <- which(colnames(dataList[[1]]) %in% c("S.t", paste0(
"F", ofInterestJ, ".t"
)))
if (ntrt == 1) {
merge_vars <- c("id", "t")
} else {
merge_vars <- c("id", "t", "trt")
}
# the first time it's called these columns won't exist
if (length(colInd) == 0) {
dataList[[1]]$row_id <- seq_len(dim(dataList[[1]])[1])
dataList[[1]] <- merge(
dataList[[1]],
Reduce(
rbind,
dataList[2:(ntrt + 1)]
)[, c(
"id", "t", "trt", "S.t",
paste0(
"F", ofInterestJ, ".t"
)
)],
by = merge_vars
)
dataList[[1]] <- dataList[[1]][order(dataList[[1]]$row_id), ]
dataList[[1]] <- dataList[[1]][,-which(colnames(dataList[[1]]) == "row_id")]
} else {
# the next times it's called those columns will exist but we want them
# replaced with the values from dataList[[>1]]
dataList[[1]]$row_id <- seq_len(dim(dataList[[1]])[1])
dataList[[1]] <- merge(
x = dataList[[1]][, -colInd],
y = Reduce(
rbind,
dataList[2:(ntrt + 1)]
)[, c(
"id", "t", "trt", "S.t",
paste0(
"F", ofInterestJ, ".t"
)
)],
by = merge_vars
)
dataList[[1]] <- dataList[[1]][order(dataList[[1]]$row_id), ]
dataList[[1]] <- dataList[[1]][,-which(colnames(dataList[[1]]) == "row_id")]
}
# drop merged trt columns
if (ntrt == 1) {
dataList[[1]]$trt <- dataList[[1]]$trt.x
if("trt.x" %in% colnames(dataList[[1]])){
dataList[[1]] <- dataList[[1]][, -which(colnames(dataList[[1]]) == "trt.x"), drop = FALSE]
}
if("trt.y" %in% colnames(dataList[[1]])){
dataList[[1]] <- dataList[[1]][, -which(colnames(dataList[[1]]) == "trt.y"), drop = FALSE] }
}
dataList <- lapply(dataList, function(x, allJ) {
for (j in allJ) {
if (length(allJ) > 1) {
x[[paste0("hazNot", j)]] <-
rowSums(cbind(rep(0, nrow(x)), x[, paste0("Q", allJ[allJ != j], "Haz")]))
x[[paste0("hazNot", j)]][x[[paste0("hazNot", j)]] == 1] <-
1 - .Machine$double.neg.eps
} else {
x[[paste0("hazNot", j)]] <- 0
}
}
x
}, allJ = allJ)
# set up clever covariates needed for fluctuation
dataList <- lapply(dataList, function(x, ofInterestJ, uniqtrt, att) {
for (z in uniqtrt) {
for (j in ofInterestJ) {
x[[paste0("H", j, ".jSelf.z", z)]] <-
(x$ftime >= x$t & x$trt == z) / (x[[paste0("g_", z)]] * x$G_dC) *
(1 - x[[paste0("hazNot", j)]]) * ((x$t < t0) * (1 - (x[[paste0("F", j, ".z", z, ".t0")]] -
x[[paste0("F", j, ".t")]]) / c(x$S.t)) + as.numeric(x$t == t0))
x[[paste0("H", j, ".jNotSelf.z", z)]] <-
-(x$ftime >= x$t & x$trt == z) / (x[[paste0("g_", z)]] * x$G_dC) *
(1 - x[[paste0("hazNot", j)]]) * ((x$t < t0) * (x[[paste0("F", j, ".z", z, ".t0")]] -
x[[paste0("F", j, ".t")]]) / c(x$S.t))
if(att){
x[[paste0("H", j, ".jSelf.z", z)]] <- x[[paste0("H", j, ".jSelf.z", z)]] * x$g_1
x[[paste0("H", j, ".jNotSelf.z", z)]] <- x[[paste0("H", j, ".jNotSelf.z", z)]] * x$g_1
}
}
}
x
}, ofInterestJ = ofInterestJ, uniqtrt = uniqtrt, att = att)
# } else {
# dataList <- lapply(dataList, function(x, ofInterestJ, uniqtrt) {
# # placebo match
# x$H1.jSelf.z0 <- 1 / x$F1.z0.t0 * (x$ftime >= x$t & x$trt == 0) /
# (x$g_0 * x$G_dC) * (1 - x$hazNot1) *
# ((x$t < t0) * (1 - (x$F1.z0.t0 - x$F1.t)/c(x$S.t)) + (x$t==t0))
# x$H1.jNotSelf.z0 <- 1 / x$F1.z0.t0 * - (x$ftime >= x$t & x$trt == 0) /
# (x$g_0 * x$G_dC) *(1 - x$hazNot1) * ((x$t < t0) *
# (x$F1.z0.t0 - x$F1.t) / c(x$S.t))
# # vaccine match
# x$H1.jSelf.z1 <- -1 / x$F1.z1.t0 * (x$ftime >= x$t & x$trt == 1) /
# (x$g_1 * x$G_dC) * (1 - x$hazNot1) *
# ((x$t < t0) * (1 - (x$F1.z1.t0 - x$F1.t) / c(x$S.t)) + (x$t == t0))
# x$H1.jNotSelf.z1 <- -1 / x$F1.z1.t0 * -(x$ftime >= x$t & x$trt == 1) /
# (x$g_1*x$G_dC) * (1 - x$hazNot1) *
# ((x$t < t0) * (x$F1.z1.t0 - x$F1.t) / c(x$S.t))
# # placebo mismatch
# x$H2.jSelf.z0 <- -1 / x$F2.z0.t0 * (x$ftime >= x$t & x$trt == 0) /
# (x$g_0 * x$G_dC) * (1 - x$hazNot2) *
# ((x$t < t0) * (1 - (x$F2.z0.t0 - x$F2.t) / c(x$S.t)) + (x$t == t0))
# x$H2.jNotSelf.z0 <- -1 / x$F2.z0.t0 * -(x$ftime >= x$t & x$trt == 0) /
# (x$g_0 * x$G_dC) *(1 - x$hazNot2) *
# ((x$t < t0)*(x$F2.z0.t0 - x$F2.t) / c(x$S.t))
# # vaccine mismatch
# x$H2.jSelf.z1 <- 1 / x$F2.z1.t0 * (x$ftime >= x$t & x$trt == 1) /
# (x$g_1 * x$G_dC) * (1 - x$hazNot2) *
# ((x$t < t0) * (1 - (x$F2.z1.t0 - x$F2.t)/c(x$S.t)) + (x$t == t0))
# x$H2.jNotSelf.z1 <- 1 / x$F2.z1.t0 * -(x$ftime >= x$t & x$trt == 1) /
# (x$g_1 * x$G_dC) * (1 - x$hazNot2) *
# ((x$t < t0) * (x$F2.z1.t0 - x$F2.t)/c(x$S.t))
# x
# })
# }
dataList
}
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