Modeling Censored Lifetime Data Using a Mixture of Gammas Baseline

We propose a Bayesian semiparametric accelerated failure time (AFT) model in which the baseline survival distribution is modeled as a Dirichlet process mixture of gamma densities. The model is highly flexible and readily captures features such as multimodality in predictive survival densities. The approach can be used in a "black-box" manner in that the prior information needed to fit the model can be quite vague, and we recommend a particular prior in the absence of information on the baseline survival distribution. The resulting posterior baseline distribution has mass only on the positive reals, a desirable feature in a failure-time model. The formulae needed to fit the model are available in closed-form and the model is relatively easy to code and implement. We provide both simulated and real data examples, including data on the cosmetic effects of cancer therapy.