Bayesian semiparametric models for survival data with a cure fraction

We propose methods for Bayesian inference for a new class of semiparametric survival models with a cure fraction. Specifically we propose a semiparame tric cure rate model with a smoothing parameter that controls the degree of parametricity in the right tail of the survival distribution. We show that such a parameter is crucial for these kinds of models and can have an impa ct on the posterior estimates. Several novel properties of tile proposed mo del are derived. In addition, we propose a class of improper noninformative priors based on this model and examine the properties of the implied poste rior. Also, a class of informative priors based on historical data is propo sed and its theoretical properties are investigated. A case study involving a melanoma clinical trial is discussed in detail to demonstrate the propos ed methodology.