A latent class model for competing risks

M. Rowley, H. Garmo, M. Vanhemelrijck, W. Wulaningsih, B. Grundmark, B. Zethelius, N. Hammar, G. Walldius, M. Inoue, L. Holmberg, A. C.C. Coolen

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)2100-2119
Number of pages20
JournalStatistics in Medicine
Volume36
Issue number13
DOIs
Publication statusPublished - 2017 Jun 15

Keywords

  • competing risks
  • heterogeneity
  • informative censoring
  • survival analysis

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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    Rowley, M., Garmo, H., Vanhemelrijck, M., Wulaningsih, W., Grundmark, B., Zethelius, B., Hammar, N., Walldius, G., Inoue, M., Holmberg, L., & Coolen, A. C. C. (2017). A latent class model for competing risks. Statistics in Medicine, 36(13), 2100-2119. https://doi.org/10.1002/sim.7246