basesurv: Estimation of the baseline survival function

In geecure: Marginal Proportional Hazards Mixture Cure Models with Generalized Estimating Equations

Description

The estimated baseline survival function based on the product-limit estimator (Kalbfleisch and Prentice, 2002), which is uesd to update the E-step in the ES algorithm.

Usage

basesurv(Time, Status, X, beta, Lambda, w, model)

Arguments

Time	right censored data which is the follow up time.
Status	the censoring indicator, normally 1 = event of interest happens, and 0 = censoring.
X	a matrix of covariates corresponding to the latency part.
beta	initial beta from the GEE for the latency part.
Lambda	initial cumulative baseline hazard function from the GEE with independence working corrlation matrix.
w	conditional probability of a patient remains uncured at the mth iteration. We use Status as initial value.
model	specifies your model, it can be para which represents the parametric PHMC model with two-parameter Weibull baseline survival function, or semi which represents the semiparametric PHMC model.

References

Kalbfleisch, J. D. and Prentice, R. L. (2002) The Statistical Analysis of Failure Time Data. John Wiley & Sons, New York, 2nd edition.

geecure documentation built on May 2, 2019, 6:03 a.m.