A general Bayesian bootstrap for censored data based on the beta-Stacy process

We introduce a novel procedure to perform Bayesian non-parametric inference with right-censored data, the \emph{beta-Stacy bootstrap}. This approximates the posterior law of summaries of the survival distribution (e.g. the mean survival time), which is often difficult in the non-parametric case. More precisely, our procedure approximates the joint posterior law of functionals of the beta-Stacy process, a non-parametric process prior widely used in survival analysis. It also represents the missing link that unifies other common Bayesian bootstraps for complete or censored data based on non-parametric priors. It is defined by an exact sampling algorithm that does not require tuning of Markov Chain Monte Carlo steps. We illustrate the beta-Stacy bootstrap by analyzing survival data from a real clinical trial.