On shared gamma-frailty conditional Markov model for semicompeting risks data

Jing Li, Ying Zhang, Giorgos Bakoyannis, Sujuan Gao

Research output: Contribution to journalArticle

Abstract

Semicompeting risks data are a mixture of competing risks data and progressive state data. This type of data occurs when a nonterminal event is subject to truncation by a well-defined terminal event, but not vice versa. The shared gamma-frailty conditional Markov model (GFCMM) has been used to analyze semicompeting risks data because of its flexibility. There are two versions of this model: the restricted and the unrestricted model. Maximum likelihood estimation methodology has been proposed in the literature. However, we found through numerical experiments that the unrestricted model sometimes yields nonparametrically biased estimation. In this article, we provide a practical guideline for using the GFCMM in the analysis of semicompeting risk data that includes: (a) a score test to assess if the restricted model, which does not exhibit estimation problems, is reasonable under a proportional hazards assumption, and (b) a graphical illustration to justify whether the unrestricted model yields nonparametric estimation with substantial bias for cases where the test provides a statistical significant result against the restricted model. This guideline was applied to the Indianapolis-Ibadan Dementia Project data as an illustration to explore how dementia occurrence changes mortality risk.

Original languageEnglish (US)
JournalStatistics in Medicine
DOIs
StateAccepted/In press - 2020

Keywords

  • dementia
  • EM-algorithm
  • frailty
  • illness-death model
  • Markov model
  • nonparametric
  • semicompeting risks

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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