Maximum likelihood methods for cure rate models with missing covariates |
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Authors: | Chen M H Ibrahim J G |
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Affiliation: | Department of Mathematical Sciences, Worcester Polytechnic Institute, Massachusetts 01609, USA. |
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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. |
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Keywords: | Cure rate model EM algorithm Gibbs sampling Latent variables Missing data |
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