title: Bayesian semiparametric models for survival data with a cure fraction.
abstract: We propose methods for Bayesian inference for a new class of semiparametric survival models with a cure fraction. Specifically, we propose a semiparametric 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 impact on the posterior estimates. Several novel properties of the proposed model are derived. In addition, we propose a class of improper noninformative priors based on this model and examine the properties of the implied posterior. Also, a class of informative priors based on historical data is proposed and its theoretical properties are investigated. A case study involving a melanoma clinical trial is discussed in detail to demonstrate the proposed methodology.
authors: Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA. ibrahim@jimmy.harvard.edu
authors: J G
authors: JG
authors: Ibrahim
authors: M H
authors: MH
authors: Chen
authors: D
authors: D
authors: Sinha
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D016013:Likelihood Functions; D008545:Melanoma / Q000401:mortality / Q000556:secondary / Q000628:therapy; D015233:Models, Statistical; D009362:Neoplasm Metastasis; D009369:Neoplasms / Q000401:mortality / Q000628:therapy; D011237:Predictive Value of Tests; D016019:Survival Analysis; D017211:Treatment Failure; D016896:Treatment Outcome