Proportional hazard model estimation under dependent censoring using copulas and penalized likelihood

Jin Xu, Jun Ma, Michael H. Connors, Henry Brodaty

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

This paper considers Cox proportional hazard models estimation under informative right censored data using maximum penalized likelihood, where dependence between censoring and event times are modelled by a copula function and a roughness penalty function is used to restrain the baseline hazard as a smooth function. Since the baseline hazard is nonnegative, we propose a special algorithm where each iteration involves updating regression coefficients by the Newton algorithm and baseline hazard by the multiplicative iterative algorithm. The asymptotic properties for both regression coefficients and baseline hazard estimates are developed. The simulation study investigates the performance of our method and also compares it with an existing maximum likelihood method. We apply the proposed method to a dementia patients dataset.
Original languageEnglish
Pages (from-to)2238-2251
Number of pages14
JournalStatistics in Medicine
Volume37
Issue number14
DOIs
Publication statusPublished - 30 Jun 2018

Keywords

  • constrained optimization
  • copulas
  • dependent censoring
  • maximum penalized likelihood
  • sensitivity analysis

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