survSL.coxph: Wrapper function for Cox proportional hazards regression...

In tedwestling/survSuperLearner: Super Learning for Conditional Survival Functions with Right-Censored Data

Wrapper function for Cox proportional hazards regression prediction algorithm

Description

This prediciton algorithm uses the partial maximum likelihood estimator of the coefficients and Breslow estimator of the baseline cumulative hazard in the Cox proportional hazards model using the coxph and survfit functions.

Usage

survSL.coxph(time, event, X, newX, new.times, obsWeights, ...)

Arguments

time	Observed follow-up time; i.e. minimum of the event and censoring times.
event	Observed event indicator; i.e, whether the follow-up time corresponds to an event or censoring.
X	Training covariate data.frame.
newX	Test covariate data.frame to use for prediction. Should have the same variable names and structure as X.
new.times	Times at which to obtain to obtain the predicted survivals.
obsWeights	Observation weights.
...	Additional ignored arguments.

Value

pred	Matrix of predictions, with the same number of rows as newX and number of columns equal to the length of new.times. Rows index new observations, and columns index new times at which the survival was computed.
fit	One-element list including object, the fitted coxph object.

tedwestling/survSuperLearner documentation built on Dec. 12, 2024, 4:16 p.m.