Nothing
aucJM.JMbayes <- function (object, newdata, Tstart, Thoriz = NULL, Dt = NULL, idVar = "id",
simulate = FALSE, M = 100, ...) {
if (!inherits(object, "JMbayes"))
stop("Use only with 'JMbayes' objects.\n")
if (!is.data.frame(newdata) || nrow(newdata) == 0)
stop("'newdata' must be a data.frame with more than one rows.\n")
if (is.null(newdata[[idVar]]))
stop("'idVar' not in 'newdata'.\n")
if (is.null(Thoriz) && is.null(Dt))
stop("either 'Thoriz' or 'Dt' must be non null.\n")
if (!is.null(Thoriz) && Thoriz <= Tstart)
stop("'Thoriz' must be larger than 'Tstart'.")
if (is.null(Thoriz))
Thoriz <- Tstart + Dt
Thoriz <- Thoriz + 1e-07
id <- newdata[[idVar]]
id <- match(id, unique(id))
TermsT <- object$Terms$termsT
SurvT <- model.response(model.frame(TermsT, newdata))
is_counting <- attr(SurvT, "type") == "counting"
Time <- if (is_counting) {
ave(SurvT[, 2], id, FUN = function (x) tail(x, 1))
} else {
SurvT[, 1]
}
timeVar <- object$timeVar
ordTime <- order(Time)
newdata2 <- newdata[ordTime, ]
newdata2 <- newdata2[Time[ordTime] > Tstart, ]
newdata2 <- newdata2[newdata2[[timeVar]] <= Tstart, ]
pi.u.t <- if (is_counting) {
survfitJM(object, newdata = newdata2, idVar = idVar, survTimes = Thoriz,
simulate = simulate, M = M, LeftTrunc_var = all.vars(TermsT)[1L])
} else {
survfitJM(object, newdata = newdata2, idVar = idVar, survTimes = Thoriz,
simulate = simulate, M = M)
}
pi.u.t <- sapply(pi.u.t$summaries, "[", 1, 2)
# find comparable subjects
id <- newdata2[[idVar]]
SurvT <- model.response(model.frame(TermsT, newdata2))
if (is_counting) {
f <- factor(id, levels = unique(id))
Time <- tapply(SurvT[, 2], f, tail, 1)
event <- tapply(SurvT[, 3], f, tail, 1)
} else{
Time <- SurvT[!duplicated(id), 1]
event <- SurvT[!duplicated(id), 2]
}
names(Time) <- names(event) <- as.character(unique(id))
if (any(dupl <- duplicated(Time))) {
Time[dupl] <- Time[dupl] + 1e-07
}
if (!all(names(pi.u.t) == names(Time)))
stop("mismatch between 'Time' variable names and survival probabilities names.")
auc <- if (length(Time) > 1) {
pairs <- combn(as.character(unique(id)), 2)
Ti <- Time[pairs[1, ]]
Tj <- Time[pairs[2, ]]
di <- event[pairs[1, ]]
dj <- event[pairs[2, ]]
pi.u.t.i <- pi.u.t[pairs[1, ]]
pi.u.t.j <- pi.u.t[pairs[2, ]]
ind1 <- (Ti <= Thoriz & di == 1) & Tj > Thoriz
ind2 <- (Ti <= Thoriz & di == 0) & Tj > Thoriz
ind3 <- (Ti <= Thoriz & di == 1) & (Tj <= Thoriz & dj == 0)
ind4 <- (Ti <= Thoriz & di == 0) & (Tj <= Thoriz & dj == 0)
names(ind1) <- names(ind2) <- names(ind3) <- names(ind4) <- paste(names(Ti), names(Tj), sep = "_")
ind <- ind1 | ind2 | ind3 | ind4
##### Generate future predictions for ind2, 3, 4 where some events occur after dThoriz
# Create indexes to organise and unpack predictions
# Note unlist prevents sapply retunrning a list when any index is zero
nams_pi2i <- unlist( sapply( strsplit(names(ind2[ind2]), "_"), "[", 1) )
nams_pi3j <- unlist( sapply( strsplit(names(ind3[ind3]), "_"), "[", 2) )
nams_pi4i <- unlist( sapply( strsplit(names(ind4[ind4]), "_"), "[", 1) )
nams_pi4j <- unlist( sapply( strsplit(names(ind4[ind4]), "_"), "[", 2) )
nams_to_pred <- unique( c(nams_pi2i, nams_pi3j, nams_pi4i, nams_pi4j))
# Predictions conditional observed events
if(length(nams_to_pred) > 0){
cond_preds <- if (is_counting) {
survfitJM(object, newdata = newdata2[id %in% nams_to_pred, ], idVar = idVar,
last.time = Time[nams_to_pred], survTimes = Thoriz,
simulate = simulate, M = M, LeftTrunc_var = all.vars(TermsT)[1L])
} else {
survfitJM(object, newdata = newdata2[id %in% nams_to_pred, ], idVar = idVar,
last.time = Time[nams_to_pred], survTimes = Thoriz,
simulate = simulate, M = M)
}
}
if (any(ind2)) {
# Extract ind2 predictions
pi2 <- cond_preds$summaries[ match(nams_pi2i, names(cond_preds$summaries)) ]
pi2 <- 1 - sapply(pi2, "[", 1, 2)
ind[ind2] <- ind[ind2] * pi2
}
if (any(ind3)) {
# Extract ind3 predictions
pi3 <- cond_preds$summaries[ match(nams_pi3j, names(cond_preds$summaries)) ]
pi3 <- sapply(pi3, "[", 1, 2)
ind[ind3] <- ind[ind3] * pi3
}
if (any(ind4)) {
# Extract ind4 predictions
pi4_i <- cond_preds$summaries[ match(nams_pi4i, names(cond_preds$summaries)) ]
pi4_i <- 1 - sapply(pi4_i, "[", 1, 2)
pi4_j <- cond_preds$summaries[ match(nams_pi4j, names(cond_preds$summaries)) ]
pi4_j <- sapply(pi4_j, "[", 1, 2)
ind[ind4] <- ind[ind4] * pi4_i * pi4_j
}
sum((pi.u.t.i < pi.u.t.j) * c(ind), na.rm = TRUE) / sum(ind, na.rm = TRUE)
} else {
NA
}
out <- list(auc = auc, Tstart = Tstart, Thoriz = Thoriz, nr = length(unique(id)),
classObject = class(object), nameObject = deparse(substitute(object)))
class(out) <- "aucJM"
out
}
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