Maximum likelihood methods for cure rate models with missing covariates.

M H Chen; J G Ibrahim

doi:10.1111/j.0006-341x.2001.00043.x

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Maximum likelihood methods for cure rate models with missing covariates.

M H Chen·J G Ibrahim

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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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Algorithms Biometry Clinical Trials, Phase III as Topic Data Interpretation, Statistical Humans Interferons Likelihood Functions Melanoma Models, Statistical Monte Carlo Method Randomized Controlled Trials as Topic Survival Analysis Survival Rate Journal Article

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M H Chen, J G Ibrahim. 2001. Maximum likelihood methods for cure rate models with missing covariates.. https://doi.org/10.1111/j.0006-341x.2001.00043.x

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