Kaplan-Meier Prediction Algorithm — surv.km • SuperSurv

This prediction algorithm ignores all covariates and computes the marginal Kaplan-Meier survival estimator using the survfit function.

Usage

surv.km(time, event, X, newdata, new.times, obsWeights, id, ...)

Arguments

time: Numeric vector of observed follow-up times.
event: Numeric vector of event indicators (1 = event, 0 = censored).
X: Training covariate data.frame (Ignored by KM).
newdata: Test covariate data.frame to use for prediction.
new.times: Numeric vector of times at which to predict survival.
obsWeights: Numeric vector of observation weights.
id: Optional vector indicating subject/cluster identities.
...: Additional ignored arguments.

Value

A list containing:

fit: A list containing the fitted survfit object.
pred: A numeric matrix of cross-validated survival predictions evaluated at new.times.

Examples

data("metabric", package = "SuperSurv")
dat <- metabric[1:30, ]
x_cols <- grep("^x", names(dat))[1:3]
X <- dat[, x_cols, drop = FALSE]
newX <- X[1:5, , drop = FALSE]
times <- seq(50, 150, by = 50)

fit <- surv.km(
  time = dat$duration,
  event = dat$event,
  X = X,
  newdata = newX,
  new.times = times,
  obsWeights = rep(1, nrow(dat)),
  id = NULL
)

dim(fit$pred)
#> [1] 5 3