Maximum Likelihood Methods for Cure Rate Models with Missing Covariates

Ming‐Hui Chen and Joseph G. Ibrahim

Biometrics, 2001, vol. 57, issue 1, 43-52

Abstract: Summary. We propose maximum likelihood methods for parameter estimation for a novel class of semi‐parametric survival models with a cure fraction, in which the covariates are allowed to be missing. We allow the covariates to be either categorical or continuous and specify a parametric distribution for the covariates that is written as a sequence of one‐dimensional conditional distributions. We propose a novel EM algorithm for maximum likelihood estimation and derive standard errors by using Louis's formula (Louis, 1982, Journal of the Royal Statistical Society, Series B44, 226–233). Computational techniques using the Monte Carlo EM algorithm are discussed and implemented. A real data set involving a melanoma cancer clinical trial is examined in detail to demonstrate the methodology.

Date: 2001
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