R/CIF_ini.R

In survidm: Inference and Prediction in an Illness-Death Model

CIF_ini <- function (object, t, s, conf = FALSE, n.boot = 199, conf.level = 0.95,
                     cluster = FALSE, ncores = NULL, presmooth = FALSE)
{
  if (missing(object))
    stop("Argument 'object' is missing, with no default")
  if (missing(t)) {
    t <- object[[1]]$time1
    ptimes <- which(object[[1]]$event1 == 1)
    t <- t[ptimes]
  }
  if (missing(s))
    s <- mean(object[[1]]$time1)
  if (any(t <= 0))
    stop("The values of 't' must be positive")
  t <- sort(unique(t))
  n <- length(t)
  if (length(t) == 0)
    stop("Invalid values for 't'.")
  t <- c(0, t)
  n <- length(t)
  cif <- rep(NA, length(t))
  condcif <- rep(NA, length(t))

  if(presmooth == FALSE){
    kmw <- KMW(object[[1]]$time1, object[[1]]$event1)
    p0 <- which(object[[1]]$time1 > s)
    kmw1 <- KMW(object[[1]]$time1[p0], object[[1]]$event1[p0])
  }

  else{
    kmw <- PKMW(object[[1]]$time1, object[[1]]$event1)
    p0 <- which(object[[1]]$time1 > s)
    kmw1 <- PKMW(object[[1]]$time1[p0], object[[1]]$event1[p0])
  }

  for (k in 1:length(t)) {
    p <- which(object[[1]]$time1 < object[[1]]$Stime & object[[1]]$time1 <
                 t[k])
    q <- which(object[[1]]$time1[p0] < object[[1]]$Stime[p0] &
                 object[[1]]$time1[p0] < t[k])
    cif[k] <- sum(kmw[p])
    condcif[k] <- sum(kmw1[q])
  }
  resu <- data.frame(cbind(t, cif, condcif))
  names(resu) <- c("t", "CIF", "CIF(t|Y(s)=0)")
  cif.ci <- matrix(NA, length(t), 2)
  condcif.ci <- matrix(NA, length(t), 2)
  if (conf == TRUE) {
    cif.ci0 <- matrix(NA, nrow = length(t), ncol = n.boot)
    condcif.ci0 <- matrix(NA, nrow = length(t), ncol = n.boot)
    n <- dim(object[[1]])[1]
    for (j in 1:n.boot) {
      xx <- sample.int(n, size = n, replace = TRUE)
      ndata <- object[[1]][xx, ]
      p0 <- which(ndata$time1 > s)
      if(presmooth == FALSE){
        kmw <- KMW(ndata$time1, ndata$event1)
        kmw1 <- KMW(ndata$time1[p0], ndata$event1[p0])
      }
      else {
        kmw <- PKMW(ndata$time1, ndata$event1)
        kmw1 <- PKMW(ndata$time1[p0], ndata$event1[p0])
      }

      for (k in 1:length(t)) {
        p <- which(ndata$time1 < ndata$Stime & ndata$time1 <
                     t[k])
        q <- which(ndata$time1[p0] < ndata$Stime[p0] &
                     ndata$time1[p0] < t[k])
        cif.ci0[k, j] <- sum(kmw[p])
        condcif.ci0[k, j] <- sum(kmw1[q])
      }
    }
    for (k in 1:length(t)) {
      cif.ci[k, 1] <- quantile(cif.ci0[k, ], (1 - conf.level)/2)
      cif.ci[k, 2] <- quantile(cif.ci0[k, ], 1 - (1 - conf.level)/2)
      condcif.ci[k, 1] <- quantile(condcif.ci0[k, ], (1 -
                                                        conf.level)/2)
      condcif.ci[k, 2] <- quantile(condcif.ci0[k, ], 1 -
                                     (1 - conf.level)/2)
    }
  }
  if (conf == TRUE) {
    ci <- cbind(cif.ci, condcif.ci)
    ci <- data.frame(ci)
    names(ci) <- c("cif.li.ci", "cif.ls.ci", "condcif.li.ci",
                   "condcif.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("CIF")
  return(invisible(result))
}