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##' @title Time-dependent AUC for joint models
##' @name AUCJMMLSM
##' @aliases AUCJMMLSM
##' @param object object of class 'JMMLSM'.
##' @param seed a numeric value of seed to be specified for cross validation.
##' @param landmark.time a numeric value of time for which dynamic prediction starts..
##' @param horizon.time a numeric vector of future times for which predicted probabilities are to be computed.
##' @param obs.time a character string of specifying a longitudinal time variable.
##' @param method estimation method for predicted probabilities. If \code{Laplace}, then the empirical empirical
##' estimates of random effects is used. If \code{GH}, then the pseudo-adaptive Gauss-Hermite quadrature is used.
##' @param quadpoint the number of pseudo-adaptive Gauss-Hermite quadrature points if \code{method = "GH"}.
##' @param maxiter the maximum number of iterations of the EM algorithm that the
##' function will perform. Default is 10000.
##' @param n.cv number of folds for cross validation. Default is 3.
##' @param survinitial Fit a Cox model to obtain initial values of the parameter estimates. Default is TRUE.
##' @param ... Further arguments passed to or from other methods.
##' @return a list of matrices with conditional probabilities for subjects.
##' @author Shanpeng Li \email{lishanpeng0913@ucla.edu}
##' @seealso \code{\link{JMMLSM}, \link{survfitJMMLSM}}
##' @export
##'
AUCJMMLSM <- function(object, seed = 100, landmark.time = NULL, horizon.time = NULL,
obs.time = NULL, method = c("Laplace", "GH"),
quadpoint = NULL, maxiter = 1000, n.cv = 3, survinitial = TRUE, ...) {
if (!inherits(object, "JMMLSM"))
stop("Use only with 'JMMLSM' xs.\n")
if (is.null(landmark.time))
stop("Please specify the landmark.time for dynamic prediction.")
if (!method %in% c("Laplace", "GH"))
stop("Please specify a method for probability approximation: Laplace or GH.")
if (!is.vector(horizon.time))
stop("horizon.time must be vector typed.")
if (is.null(quadpoint)) {
quadpoint <- object$quadpoint
}
if (is.null(obs.time)) {
stop("Please specify a vector that represents the time variable from ydatanew.")
} else {
if (!obs.time %in% colnames(object$ydata)) {
stop(paste0(obs.time, " is not found in ynewdata."))
}
}
CompetingRisk <- object$CompetingRisk
set.seed(seed)
cdata <- object$cdata
ydata <- object$ydata
long.formula <- object$LongitudinalSubmodelmean
surv.formula <- object$SurvivalSubmodel
surv.var <- all.vars(surv.formula)
New.surv.formula.out <- paste0("survival::Surv(", surv.var[1], ",",
surv.var[2], "==0)")
New.surv.formula <- as.formula(paste(New.surv.formula.out, 1, sep = "~"))
variance.formula <- as.formula(paste("", object$LongitudinalSubmodelvariance[3], sep = "~"))
random <- all.vars(object$random)
ID <- random[length(random)]
folds <- caret::groupKFold(c(1:nrow(cdata)), k = n.cv)
AUC.cv <- list()
for (t in 1:n.cv) {
train.cdata <- cdata[folds[[t]], ]
train.ydata <- ydata[ydata[, ID] %in% train.cdata[, ID], ]
fit <- try(JMMLSM(cdata = train.cdata, ydata = train.ydata,
long.formula = long.formula,
surv.formula = surv.formula,
variance.formula = variance.formula,
quadpoint = quadpoint, random = object$random,
survinitial = survinitial, maxiter = maxiter), silent = TRUE)
if ('try-error' %in% class(fit)) {
writeLines(paste0("Error occured in the ", t, " th training!"))
AUC.cv[[t]] <- NULL
} else if (fit$iter == maxiter) {
AUC.cv[[t]] <- NULL
} else {
val.cdata <- cdata[-folds[[t]], ]
val.ydata <- ydata[ydata[, ID] %in% val.cdata[, ID], ]
val.cdata <- val.cdata[val.cdata[, surv.var[1]] > landmark.time, ]
val.ydata <- val.ydata[val.ydata[, ID] %in% val.cdata[, ID], ]
val.ydata <- val.ydata[val.ydata[, obs.time] <= landmark.time, ]
NewyID <- unique(val.ydata[, ID])
val.cdata <- val.cdata[val.cdata[, ID] %in% NewyID, ]
survfit <- try(survfitJMMLSM(fit, ynewdata = val.ydata, cnewdata = val.cdata,
u = horizon.time, method = method,
Last.time = rep(landmark.time, nrow(val.cdata)),
obs.time = obs.time, quadpoint = quadpoint), silent = TRUE)
if ('try-error' %in% class(survfit)) {
writeLines(paste0("Error occured in the ", t, " th validation!"))
AUC.cv[[t]] <- NULL
} else {
if (CompetingRisk) {
CIF <- as.data.frame(matrix(0, nrow = nrow(val.cdata), ncol = 3))
colnames(CIF) <- c("ID", "CIF1", "CIF2")
CIF$ID <- val.cdata[, ID]
CIF$time <- val.cdata[, surv.var[1]]
CIF$status <- val.cdata[, surv.var[2]]
mean.AUC <- matrix(NA, nrow = length(horizon.time), ncol = 2)
for (j in 1:length(horizon.time)) {
## extract estimated CIF
for (k in 1:nrow(CIF)) {
CIF[k, 2] <- survfit$Pred[[k]][j, 2]
CIF[k, 3] <- survfit$Pred[[k]][j, 3]
}
ROC <- timeROC::timeROC(T = CIF$time, delta = CIF$status,
weighting = "marginal",
marker = CIF$CIF1, cause = 1,
times = horizon.time[j])
mean.AUC[j, 1] <- ROC$AUC_1[2]
CIF$status2 <- ifelse(CIF$status == 2, 1,
ifelse(CIF$status == 1, 2, 0)
)
ROC <- timeROC::timeROC(T = CIF$time, delta = CIF$status2,
weighting = "marginal",
marker = CIF$CIF2, cause = 1,
times = horizon.time[j])
mean.AUC[j, 2] <- ROC$AUC_1[2]
}
AUC.cv[[t]] <- mean.AUC
} else {
Surv <- as.data.frame(matrix(0, nrow = nrow(val.cdata), ncol = 2))
colnames(Surv) <- c("ID", "Surv")
Surv$ID <- val.cdata[, ID]
Surv$time <- val.cdata[, surv.var[1]]
Surv$status <- val.cdata[, surv.var[2]]
mean.AUC <- matrix(NA, nrow = length(horizon.time), ncol = 1)
## extract estimated Survival probability
for (j in 1:length(horizon.time)) {
for (k in 1:nrow(Surv)) {
Surv[k, 2] <- survfit$Pred[[k]][j, 2]
}
ROC <- timeROC::timeROC(T = Surv$time, delta = Surv$status,
weighting = "marginal",
marker = -Surv$Surv, cause = 1,
times = horizon.time[j])
mean.AUC[j, 1] <- ROC$AUC[2]
}
AUC.cv[[t]] <- mean.AUC
}
writeLines(paste0("The ", t, " th validation is done!"))
}
}
}
result <- list(n.cv = n.cv, AUC.cv = AUC.cv, landmark.time = landmark.time,
horizon.time = horizon.time, method = method, quadpoint = quadpoint,
CompetingRisk = CompetingRisk, seed = seed)
class(result) <- "AUCJMMLSM"
return(result)
}
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