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Estimation under Cox proportional hazards model with covariates missing not at random

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  • Lisha Guo
  • X. Joan Hu
  • Yanyan Liu

Abstract

This paper considers likelihood-based estimation under the Cox proportional hazards model in the situations where some covariate entries are missing not at random. Assuming the conditional distribution of the missing entries is known, we demonstrate the existence of the semiparametric maximum likelihood estimator of the model parameters, establish the consistency and weak convergence. By simulation, we examine the finite-sample performance of the estimation procedure, and compare the SPMLE with the one resulted from using an estimated conditional distribution of the missing entries. We analyze the data from a tuberculosis (TB) study applying the proposed approach for illustration.

Suggested Citation

  • Lisha Guo & X. Joan Hu & Yanyan Liu, 2017. "Estimation under Cox proportional hazards model with covariates missing not at random," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(18), pages 8952-8972, September.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:18:p:8952-8972
    DOI: 10.1080/03610926.2016.1197252
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