Parametric mixture models to evaluate and summarize hazard ratios in the presence of competing risks with time-dependent hazards and delayed entry

Bryan Lau, Stephen R. Cole, Stephen J. Gange

Research output: Contribution to journalArticle

Abstract

In the analysis of survival data, there are often competing events that preclude an event of interest from occurring. Regression analysis with competing risks is typically undertaken using a cause-specific proportional hazards model. However, modern alternative methods exist for the analysis of the subdistribution hazard with a corresponding subdistribution proportional hazards model. In this paper, we introduce a flexible parametric mixture model as a unifying method to obtain estimates of the cause-specific and subdistribution hazards and hazard-ratio functions. We describe how these estimates can be summarized over time to give a single number comparable to the hazard ratio that is obtained from a corresponding cause-specific or subdistribution proportional hazards model. An application to the Women's Interagency HIV Study is provided to investigate injection drug use and the time to either the initiation of effective antiretroviral therapy, or clinical disease progression as a competing event.

Original languageEnglish (US)
Pages (from-to)654-665
Number of pages12
JournalStatistics in Medicine
Volume30
Issue number6
DOIs
StatePublished - Mar 15 2011

Keywords

  • Cause-specific hazards
  • Competing risks
  • Hazard ratio
  • Mixture model
  • Subdistribution
  • Subdistribution hazards
  • Survival analysis

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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