title: A latent class model for competing risks.
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.
authors.affiliation: Institute for Mathematical and Molecular Biomedicine, King's College London, London, U.K.|Saddle Point Science, London, U.K.
authors.forename: M
authors.identifier: 0000-0002-0530-923X
authors.initials: M
authors.lastname: Rowley
authors.affiliation: Cancer Epidemiology Group, King's College London, Guy's Hospital, London, U.K.
authors.forename: H
authors.initials: H
authors.lastname: Garmo
authors.affiliation: Cancer Epidemiology Group, King's College London, Guy's Hospital, London, U.K.
authors.forename: M
authors.initials: M
authors.lastname: Van Hemelrijck
authors.affiliation: Cancer Epidemiology Group, King's College London, Guy's Hospital, London, U.K.
authors.forename: W
authors.initials: W
authors.lastname: Wulaningsih
authors.affiliation: Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.|Medical Products Agency, Uppsala, Sweden.
authors.forename: B
authors.initials: B
authors.lastname: Grundmark
authors.affiliation: Medical Products Agency, Uppsala, Sweden.|Department of Public Health and Caring Sciences/Geriatrics, Uppsala University, Uppsala, Sweden.
authors.forename: B
authors.initials: B
authors.lastname: Zethelius
authors.affiliation: Department of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.|AstraZeneca Sverige, Södertalje, Sweden.
authors.forename: N
authors.initials: N
authors.lastname: Hammar
authors.affiliation: Department of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
authors.forename: G
authors.initials: G
authors.lastname: Walldius
authors.affiliation: Department of Electrical Engineering and Bioscience, Waseda University, Tokyo, Japan.
authors.forename: M
authors.initials: M
authors.lastname: Inoue
authors.affiliation: Cancer Epidemiology Group, King's College London, Guy's Hospital, London, U.K.
authors.forename: L
authors.initials: L
authors.lastname: Holmberg
authors.affiliation: Institute for Mathematical and Molecular Biomedicine, King's College London, London, U.K.
authors.forename: A C C
authors.initials: ACC
authors.lastname: Coolen
mesh_terms: D001053:Apolipoproteins / Q000097:blood; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000150:complications; D002318:Cardiovascular Diseases / Q000097:blood / Q000150:complications; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D015233:Models, Statistical; D016016:Proportional Hazards Models; D011471:Prostatic Neoplasms / Q000453:epidemiology / Q000209:etiology; D018570:Risk Assessment / Q000379:methods / Q000706:statistics & numerical data; D012307:Risk Factors; D016019:Survival Analysis; D013548:Sweden / Q000453:epidemiology
chemical_list: D001053:Apolipoproteins
keywords: competing risks; heterogeneity; informative censoring; survival analysis