Bayesian Analysis of Competing Risks

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DMS-0306416Sanjib BasuBayesian analysis of competing risksAbstractThe investigator develops new statistical methodology and significantly advances currently available methodology for analyzing censored survival data with competing risks where several potential "risks" or causes of failure are operating on a subject or a unit. The failure time data arising in such contexts are often masked where the cause of failure is not exactly known, but can only be narrowed down to a subset of all potential causes. The investigator (a) develops a Bayesian framework for masked competing risks data using the cause-specific model and compares it to the latent approach, (b) develops flexible semiparametric models for such data and compares them to the parametric models, and (c) develops Bayesian model selection methodology for both parametric and semiparametric models and uses it to compare both model fit and predictive power. The investigator studies inferential methods for estimation of overall and cause-specific survival probabilities from the partially masked data; for estimation of the diagnostic probability of failure from a specific cause, given a masked subset of causes; and for incorporating covariate information through regressors in the setting of masked survival data.Masked competing risks data arise frequently in biomedical settings, clinical trials, and engineering applications. For example, the competing risks acting on a patient diagnosed with prostate cancer could be prostate cancer; other diseases like hypertension cardiovascular disease, and diabetes; or simply old age (since prostate cancer is a slow-growing disease). In an engineering setting, the failure of a system (such as a computer) could be due to failure of a specific component which may not be exactly identified. The goal of this research is to develop new statistical methodology and significantly advance currently available methodology for analyzing such data. The developed computational methods will be made publicly available so that anyone interested in the analysis of such competing risks data can use these methods.