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Estimate conditional survival function nuisance parameters using survival stacking

Source: R/prebuilt_generators.R
generate_nuisance_predictions_stackG.Rd

Estimate conditional survival function nuisance parameters using survival stacking

Usage

generate_nuisance_predictions_stackG(
  time,
  event,
  X,
  X_holdout,
  newtimes,
  SL.library = c("SL.mean", "SL.glm", "SL.earth", "SL.gam", "SL.ranger"),
  V = 5,
  bin_size = 0.05,
  approx_times
)

Arguments

time

n x 1 numeric vector of observed follow-up times. If there is censoring, these are the minimum of the event and censoring times.

event

n x 1 numeric vector of status indicators of whether an event was observed.

X

n x p data.frame of observed covariate values

X_holdout

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.

SL.library

Super Learner library

V

Number of cross-validation folds, to be passed to SuperLearner

bin_size

Size of time bin on which to discretize for estimation of cumulative probability functions. Can be a number between 0 and 1, indicating the size of quantile grid (e.g. 0.1 estimates the cumulative probability functions on a grid based on deciles of observed times). If NULL, creates a grid of all observed times. See stackG documentation.

approx_times

Numeric vector of times at which to approximate product integral or cumulative hazard interval. See stackG documentation.

Value

A list containing elements S_hat (conditional event survival function, corresponding to X_holdout and newtimes), S_hat_train (conditional event survival function, corresponding to X and newtimes), G_hat (conditional censoring survival function, corresponding to X_holdout and newtimes), and G_hat_train (conditional censoring survival function, corresponding to X and newtimes)

See also

stackG