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

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  • Traci Leong
  • Stuart R. Lipsitz
  • Joseph G. Ibrahim

Abstract

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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  • Traci Leong & Stuart R. Lipsitz & Joseph G. Ibrahim, 2001. "Incomplete covariates in the Cox model with applications to biological marker data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(4), pages 467-484.
    • Handle: RePEc:bla:jorssc:v:50:y:2001:i:4:p:467-484
    DOI: 10.1111/1467-9876.00248
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    Cited by:

    1. Chen, Ming-Hui & Ibrahim, Joseph G. & Shao, Qi-Man, 2009. "Maximum likelihood inference for the Cox regression model with applications to missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2018-2030, October.

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