Generate oracle prediction function estimates using doubly-robust pseudo-outcome regression with SuperLearner

Usage

DR_pseudo_outcome_regression(
  time,
  event,
  X,
  newX,
  approx_times,
  S_hat,
  G_hat,
  newtimes,
  outcome,
  SL.library,
  V
)

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
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.
approx_times: Numeric vector of length J2 giving times at which to approximate integral appearing in the pseudo-outcomes
S_hat: n x J2 matrix of conditional event time survival function estimates
G_hat: n x J2 matrix of conditional censoring time survival function estimates
newtimes: Numeric vector of times at which to generate oracle prediction function estimates
outcome: Outcome type, either "survival_probability" or "restricted_survival_time"
SL.library: Super Learner library
V: Number of cross-validation folds, to be passed to SuperLearner

Matrix of predictions.