title: A Bayesian approach to competing risks analysis with masked cause of death.
abstract: Cause-specific analyses under a competing risks framework have received considerable attention in the statistical literature. Such analyses are useful for comparing mortality patterns across racial and/or age groups. Earlier work in the statistical literature focused on the situation when the cause of death is known. A challenging twist to the problem arises when the cause of death is not known exactly, but can be narrowed down to a set of potential causes that do not necessarily act independently. This phenomenon, referred to as masking, is often the result of incomplete or partial information on death certificates and/or lack of routine autopsy on every patient. In this article we propose a semiparametric Bayesian approach for analyzing competing risks survival data with masked cause of death. The models proposed do not assume independence among the causes, and are valid for an arbitrary number of causes. Further, the Bayesian approach is flexible in allowing a general pattern of missingness for the cause of death. We illustrate our methodology using breast cancer data from the Detroit Surveillance, Epidemiology, and End Results registry.
authors.affiliation: Department of Statistics and Center for Statistical Consultation and Research, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Ananda
authors.initials: A
authors.lastname: Sen
authors.forename: Mousumi
authors.initials: M
authors.lastname: Banerjee
authors.forename: Yun
authors.initials: Y
authors.lastname: Li
authors.forename: Anne-Michelle
authors.initials: AM
authors.lastname: Noone
mesh_terms: D000367:Age Factors; D000465:Algorithms; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000401:mortality / Q000473:pathology; D002423:Cause of Death; D003198:Computer Simulation; D003644:Death Certificates; D005260:Female; D006801:Humans; D016015:Logistic Models; D008390:Markov Chains; D008824:Michigan / Q000453:epidemiology; D015233:Models, Statistical; D009010:Monte Carlo Method; D009367:Neoplasm Staging; D016016:Proportional Hazards Models; D044469:Racial Groups / Q000706:statistics & numerical data; D018570:Risk Assessment; D018426:SEER Program; D016019:Survival Analysis