DR_pseudo_outcome_regression: Generate oracle prediction function estimates using...

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

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

Description

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
)

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
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

Value

Matrix of predictions.

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