A latent class model for competing risks

Stat Med. 2017 Jun 15;36(13):2100-2119. doi: 10.1002/sim.7246. Epub 2017 Feb 24.

Abstract

Survival data analysis becomes complex when the proportional hazards assumption is violated at population level or when crude hazard rates are no longer estimators of marginal ones. We develop a Bayesian survival analysis method to deal with these situations, on the basis of assuming that the complexities are induced by latent cohort or disease heterogeneity that is not captured by covariates and that proportional hazards hold at the level of individuals. This leads to a description from which risk-specific marginal hazard rates and survival functions are fully accessible, 'decontaminated' of the effects of informative censoring, and which includes Cox, random effects and latent class models as special cases. Simulated data confirm that our approach can map a cohort's substructure and remove heterogeneity-induced informative censoring effects. Application to data from the Uppsala Longitudinal Study of Adult Men cohort leads to plausible alternative explanations for previous counter-intuitive inferences on prostate cancer. The importance of managing cardiovascular disease as a comorbidity in women diagnosed with breast cancer is suggested on application to data from the Swedish Apolipoprotein Mortality Risk Study. Copyright © 2017 John Wiley & Sons, Ltd.

Keywords: competing risks; heterogeneity; informative censoring; survival analysis.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Apolipoproteins / blood
  • Bayes Theorem
  • Breast Neoplasms / complications
  • Cardiovascular Diseases / blood
  • Cardiovascular Diseases / complications
  • Female
  • Humans
  • Longitudinal Studies
  • Male
  • Models, Statistical*
  • Proportional Hazards Models
  • Prostatic Neoplasms / epidemiology
  • Prostatic Neoplasms / etiology
  • Risk Assessment* / methods
  • Risk Assessment* / statistics & numerical data
  • Risk Factors
  • Survival Analysis
  • Sweden / epidemiology

Substances

  • Apolipoproteins