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Obtain predicted conditional survival function from a local survival stacking object

Source: R/stackL.R
predict.stackL.Rd

Obtain predicted conditional survival function from a local survival stacking object

Usage

# S3 method for class 'stackL'
predict(object, newX, newtimes, ...)

Arguments

object

Object of class stackL

newX

m x p data.frame of new observed covariate values at which to obtain m predictions for the estimated algorithm. Must have the same names and structure as X.

newtimes

k x 1 numeric vector of times at which to obtain k predicted conditional survivals.

...

Further arguments passed to or from other methods.

Value

A named list with the following components:

S_T_preds

An m x k matrix of estimated event time survival probabilities at the m covariate vector values and k times provided by the user in newX and newtimes, respectively.

See also

stackL

Examples


# This is a small simulation example
set.seed(123)
n <- 500
X <- data.frame(X1 = rnorm(n), X2 = rbinom(n, size = 1, prob = 0.5))

S0 <- function(t, x){
  pexp(t, rate = exp(-2 + x[,1] - x[,2] + .5 * x[,1] * x[,2]), lower.tail = FALSE)
}
T <- rexp(n, rate = exp(-2 + X[,1] - X[,2] + .5 *  X[,1] * X[,2]))

G0 <- function(t, x) {
  as.numeric(t < 15) *.9*pexp(t,
                              rate = exp(-2 -.5*x[,1]-.25*x[,2]+.5*x[,1]*x[,2]),
                              lower.tail=FALSE)
}
C <- rexp(n, exp(-2 -.5 * X[,1] - .25 * X[,2] + .5 * X[,1] * X[,2]))
C[C > 15] <- 15

entry <- runif(n, 0, 15)

time <- pmin(T, C)
event <- as.numeric(T <= C)

sampled <- which(time >= entry)
X <- X[sampled,]
time <- time[sampled]
event <- event[sampled]
entry <- entry[sampled]

# Note that this a very small Super Learner library, for computational purposes.
SL.library <- c("SL.mean", "SL.glm")

fit <- stackL(time = time,
               event = event,
               entry = entry,
               X = X,
               newX = X,
               newtimes = seq(0, 15, .1),
               direction = "prospective",
               bin_size = 0.1,
               time_basis = "continuous",
               SL_control = list(SL.library = SL.library,
                                 V = 5))

preds <- predict(object = fit,
                 newX = X,
                 newtimes = seq(0, 15, 0.1))

plot(preds$S_T_preds[1,], S0(t =  seq(0, 15, .1), X[1,]))
abline(0,1,col='red')