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Maximum likelihood methods for cure rate models with missing covariates
Authors:Chen M H  Ibrahim J G
Institution:Department of Mathematical Sciences, Worcester Polytechnic Institute, Massachusetts 01609, USA.
Abstract:We propose maximum likelihood methods for parameter estimation for a novel class of semiparametric 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 B 44, 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.
Keywords:Cure rate model  EM algorithm  Gibbs sampling  Latent variables  Missing data
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