tpLIDA <-
function(object, s, conf = FALSE, n.boot = 199, conf.level = 0.95,
cluster = FALSE, ncores = NULL)
{
if (missing(object))
stop("Argument 'object' is missing, with no default")
# if (!inherits(object, "survIDM")) stop("'object' must be of class 'survIDM'")
if (missing(s))
s <- 0
ptimes <- which(object[[1]]$event1 == 1 | object[[1]]$event == 1)
if (s > max(object[[1]]$time1)) stop("The value of 's' is too large")
if (s < 0) stop("'s' must be nonnegative")
if (length(s) > 1) stop("Length of 's' must be 1")
t1 <- object[[1]]$time1[object[[1]]$event1 == 1]
t2 <- object[[1]]$Stime[object[[1]]$event == 1]
t <- c(s, t1, t2)
t <- t[t >= s]
t <- sort(unique(t))
n <- length(object[[1]]$Stime)
p0 <- which(object[[1]]$time1 <= s)
p1 <- which(object[[1]]$time1 <= s & object[[1]]$Stime > s)
p1new <- which(object[[1]]$time1 <= s & object[[1]]$Stime > s & object[[1]]$event == 1)
state1 <- length(p1new)
p00 <- rep(NA, length(t))
p01 <- rep(NA, length(t))
p02 <- rep(NA, length(t))
p11 <- rep(NA, length(t))
p12 <- rep(NA, length(t))
den <- KM(object[[1]]$time1, object[[1]]$event1, s)
den2 <- sum(KMW(object[[1]]$Stime, object[[1]]$event)[p1])
kmw1 <- KMW(object[[1]]$Stime, object[[1]]$event)
for (k in 1: length(t)) {
p2 <- which(object[[1]]$time1 > s & object[[1]]$time1 <= t[k] & object[[1]]$Stime > t[k])
p3 <- which(object[[1]]$time1 <= s & object[[1]]$Stime > t[k])
n.p2 <- length(p2)
n.p3 <- length(p3)
p00[k] <- KM(object[[1]]$time1, object[[1]]$event1, t[k]) / den
ifelse(n.p2 == 0, p01[k] <- 0, p01[k] <- sum(kmw1[p2]) / den)
p02[k] <- 1 - p00[k] - p01[k]
if(p01[k] > 1) p01[k] <- 1
if(p02[k] < 0) p02[k] <- 0
if (state1 > 0) {
ifelse(n.p3 == 0, p11[k] <- 0, p11[k] <- sum(kmw1[p3]) / den2)
p12[k] <- 1 - p11[k]
}
}
resu <- data.frame(cbind(t, p00, p01, p02, p11, p12))
names(resu) <- c("t", "p00", "p01", "p02", "p11", "p12")
p00.ci <- matrix(NA, length(t), 2)
p01.ci <- matrix(NA, length(t), 2)
p02.ci <- matrix(NA, length(t), 2)
p11.ci <- matrix(NA, length(t), 2)
p12.ci <- matrix(NA, length(t), 2)
p00.std <- matrix(NA, length(t), 2)
p01.std <- matrix(NA, length(t), 2)
p02.std <- matrix(NA, length(t), 2)
p11.std <- matrix(NA, length(t), 2)
p12.std <- matrix(NA, length(t), 2)
if (conf == TRUE) {
res.ci <- array(NA, dim=c(length(t), n.boot, 5))
for (j in 1:n.boot){
xx <- sample.int(n, size = n, replace = TRUE)
ndata <- object[[1]][xx,]
p0 <- which(ndata$time1 > s)
p1 <- which(ndata$time1 <= s & ndata$Stime > s)
den <- KM(ndata$time1, ndata$event1, s)
den2 <- sum(KMW(ndata$Stime, ndata$event)[p1])
kmw1 <- KMW(ndata$Stime, ndata$event)
for (k in 1: length(t)) {
p2 <- which(ndata$time1 > s & ndata$time1 <= t[k] & ndata$Stime > t[k])
p3 <- which(ndata$time1 <= s & ndata$Stime > t[k])
n.p2 <- length(p2)
n.p3 <- length(p3)
res.ci[k, j, 1] <- KM(ndata$time1, ndata$event1, t[k]) / den
ifelse(n.p2 == 0, res.ci[k, j, 2] <- 0, res.ci[k, j, 2] <- sum(kmw1[p2]) / den)
res.ci[k, j, 3] <- 1 - res.ci[k, j, 1] - res.ci[k, j, 2]
if(res.ci[k, j, 2] > 1) res.ci[k, j, 2] <- 1
if(res.ci[k, j, 3] < 0) res.ci[k, j, 3] <- 0
if (state1 > 0) {
ifelse(n.p3 == 0, res.ci[k, j, 4] <- 0, res.ci[k, j, 4] <- sum(kmw1[p3]) / den2)
res.ci[k, j, 5] <- 1 - res.ci[k, j, 4]
}
}
}
for (k in 1: length(t)) {
p00.ci[k,1] <- quantile(res.ci[k,,1], (1 - conf.level) / 2)
p00.ci[k,2] <- quantile(res.ci[k,,1], 1 - (1 - conf.level) / 2)
p01.ci[k,1] <- quantile(res.ci[k,,2], (1 - conf.level) / 2)
p01.ci[k,2] <- quantile(res.ci[k,,2], 1 - (1 - conf.level) / 2)
p02.ci[k,1] <- quantile(res.ci[k,,3], (1 - conf.level) / 2)
p02.ci[k,2] <- quantile(res.ci[k,,3], 1 - (1 - conf.level) / 2)
if (state1 > 0) {
p11.ci[k,1] <- quantile(res.ci[k,,4], (1 - conf.level) / 2)
p11.ci[k,2] <- quantile(res.ci[k,,4], 1 - (1 - conf.level) / 2)
p12.ci[k,1] <- quantile(res.ci[k,,5], (1 - conf.level) / 2)
p12.ci[k,2] <- quantile(res.ci[k,,5], 1 - (1 - conf.level) / 2)
}
}
}
if(conf == TRUE){
ci <- cbind(p00.ci, p01.ci, p02.ci, p11.ci, p12.ci)
ci <- data.frame(ci)
names(ci) <- c("p00.li.ci", "p00.ls.ci", "p01.li.ci", "p01.ls.ci", "p02.li.ci", "p02.ls.ci", "p11.li.ci", "p11.ls.ci", "p12.li.ci", "p12.ls.ci")
}
if(conf==TRUE) result <- list(est=resu, CI=ci, conf.level=conf.level, s=s, t=t, conf=conf)
else result <- list(est=resu, s=s, t=t, conf=conf)
class(result) <- c("tpLIDA")
return(invisible(result))
}
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