basesurv {geecure}

Estimation of the baseline survival function

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.