Joint modeling approach for semicompeting risks data with missing nonterminal event status

Chen Hu, Alex Tsodikov

Research output: Contribution to journalArticlepeer-review

Abstract

Semicompeting risks data, where a subject may experience sequential non-terminal and terminal events, and the terminal event may censor the non-terminal event but not vice versa, are widely available in many biomedical studies. We consider the situation when a proportion of subjects’ non-terminal events is missing, such that the observed data become a mixture of “true” semicompeting risks data and partially observed terminal event only data. An illness–death multistate model with proportional hazards assumptions is proposed to study the relationship between non-terminal and terminal events, and provide covariate-specific global and local association measures. Maximum likelihood estimation based on semiparametric regression analysis is used for statistical inference, and asymptotic properties of proposed estimators are studied using empirical process and martingale arguments. We illustrate the proposed method with simulation studies and data analysis of a follicular cell lymphoma study.

Original languageEnglish (US)
Pages (from-to)563-583
Number of pages21
JournalLifetime Data Analysis
Volume20
Issue number4
DOIs
StatePublished - Oct 2014
Externally publishedYes

Keywords

  • Dependent censoring
  • Missing nonterminal event
  • Multistate model
  • Proportional hazards
  • Semicompeting risks data
  • Survival analysis

ASJC Scopus subject areas

  • Applied Mathematics

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