A latent class model for competing risks.
Stat Med
; 36(13): 2100-2119, 2017 06 15.
Article
en En
| MEDLINE
| ID: mdl-28233395
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.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Modelos Estadísticos
/
Medición de Riesgo
Tipo de estudio:
Etiology_studies
/
Observational_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Female
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Humans
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Male
País/Región como asunto:
Europa
Idioma:
En
Revista:
Stat Med
Año:
2017
Tipo del documento:
Article
País de afiliación:
Reino Unido