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.
basesurv(Time, Status, X, beta, Lambda, w, model)right censored data which is the follow up time.
the censoring indicator, normally 1 = event of interest happens, and 0 = censoring.
a matrix of covariates corresponding to the latency part.
initial beta from the GEE for the latency part.
initial cumulative baseline hazard function from the GEE with independence working corrlation matrix.
conditional probability of a patient remains uncured at the mth iteration. We use Status as initial value.
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.
Kalbfleisch, J. D. and Prentice, R. L. (2002) The Statistical Analysis of Failure Time Data. John Wiley & Sons, New York, 2nd edition.