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Incomplete covariates in the Cox model with applications to biological marker data

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A common occurrence in clinical trials with a survival end point is missing covariate data. With ignorably missing covariate data, Lipsitz and Ibrahim proposed a set of estimating equations to estimate the parameters of Cox's proportional hazards model. They proposed to obtain parameter estimates via a Monte Carlo EM algorithm. We extend those results to non-ignorably missing covariate data. We present a clinical trials example with three partially observed laboratory markers which are used as covariates to predict survival.
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Keywords: EM algorithm; Monte Carlo methods; Non-ignorably missing data

Document Type: Original Article

Affiliations: 1: Dana-Farber Cancer Institute, Boston, Emory University, Atlanta, USA, 2: Dana-Farber Cancer Institute, Boston, Medical University of South Carolina, Charleston, Harvard School of Public Health, Boston, USA, 3: Dana-Farber Cancer Institute, Boston USA, Harvard School of Public Health, Boston, USA

Publication date: 01 January 2001

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