Bo Cai

Department of Epidemiology and Biostatistics

University of South Carolina

Bayesian Semiparametric Modeling using Mixtures for Stratified Survival Data

Survival analysis based on mixture models have certain advantages over classical parametric approaches because mixture models provide a convenient and flexible semiparametric framework to model unknown distributions shapes. We describe a generalized mixture model based on B-splines for modeling monotone function such as the integrated baseline hazard function and covariate link in a proportional hazard model, which includes beta mixtures by Diaconis and Ylvisaker (1985, Bayesian Statistics 2, 133-156) and triangular mixtures by Perron and Mengersen (2001, Biometrics 57, 518-528). A Bayesian hierarchical semiparametric proportional hazard model is developed by using such mixtures for fitting stratified survival data. Data from a multicenter AIDS clinical trial are used for illustration and comparison of hierarchical proportional hazards regression models based on different mixtures.

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