predict.stackG: Obtain predicted conditional survival and cumulative hazard...

In cwolock/survML: Tools for Flexible Survival Analysis Using Machine Learning

Obtain predicted conditional survival and cumulative hazard functions from a global survival stacking object

Description

Obtain predicted conditional survival and cumulative hazard functions from a global survival stacking object

Usage

## S3 method for class 'stackG'
predict(
  object,
  newX,
  newtimes,
  surv_form = object$surv_form,
  time_grid_approx = object$time_grid_approx,
  ...
)

Arguments

object	Object of class stackG
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.
surv_form	Mapping from hazard estimate to survival estimate. Can be either "PI" (product integral mapping) or "exp" (exponentiated cumulative hazard estimate). Defaults to the value saved in object.
time_grid_approx	Numeric vector of times at which to approximate product integral or cumulative hazard interval. Defaults to the value saved in object.
...	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.
S_C_preds	An m x k matrix of estimated censoring time survival probabilities at the m covariate vector values and k times provided by the user in newX and newtimes, respectively.
Lambda_T_preds	An m x k matrix of estimated event time cumulative hazard function values at the m covariate vector values and k times provided by the user in newX and newtimes, respectively.
Lambda_C_preds	An m x k matrix of estimated censoring time cumulative hazard function values at the m covariate vector values and k times provided by the user in newX and newtimes, respectively.
time_grid_approx	The approximation grid for the product integral or cumulative hazard integral, (user-specified).
surv_form	Exponential or product-integral form (user-specified).

See Also

stackG

Examples

# This is a small simulation example
set.seed(123)
n <- 250
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 <- stackG(time = time,
              event = event,
              entry = entry,
              X = X,
              newX = X,
              newtimes = seq(0, 15, .1),
              direction = "prospective",
              bin_size = 0.1,
              time_basis = "continuous",
              time_grid_approx = sort(unique(time)),
              surv_form = "exp",
              learner = "SuperLearner",
              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')

cwolock/survML documentation built on April 17, 2025, 5:17 p.m.