ES algorithm is an estension of the EM algorithm where the M-step of the EM algorithm is replaced by a step requiring the solution of a series of generalised estimating equations. We use the ES algorithm for the analysis of survival cure data with potential correlation.
es(Time, Status, X, Z, id, model, corstr, stdz, esmax, eps)right censored data which is the follow up time.
the censoring indicator, normally 0 = event of interest happens, and 0 = censoring.
a matrix of covariates corresponding to the latency part.
a matrix of covariates corresponding to the incidence part.
a vector which identifies the clusters. The length of id should be the same as the number of observations.
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.
a character string specifying the correlation structure. The following are permitted: independence and exchangeable.
If it is TRUE, all the covariates in the formula and cureform are standardized. By default, stdz = FALSE.
specifies the maximum iteration number. If the convergence criterion is not met, the ES iteration will be stopped after esmax iterations and the estimates will be based on the last ES iteration. The default esmax = 100.
tolerance for convergence. The default is eps = 1e-6. Iteration stops once the relative change in deviance is less than eps.