Related Experiment Video
Updated: Jul 23, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
A general Bayesian bootstrap for censored data based on the beta-Stacy process
1Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center 485 Lexington Ave, 2nd floor New York, NY 10017, United States.
Abstract:
We introduce a novel procedure to perform Bayesian non-parametric inference with right-censored data, the beta-Stacy bootstrap. This approximates the posterior law of summaries of the survival distribution (e.g. the mean survival time). More precisely, our procedure approximates the joint posterior law of functionals of the beta-Stacy process, a non-parametric process prior that generalizes the Dirichlet process and that is widely used in survival analysis. The beta-Stacy bootstrap generalizes and 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.
More Related Videos
Related Concept Videos
Censoring Survival Data
Bootstrapping
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Kaplan-Meier Approach
Assumptions of Survival Analysis
Survival Tree
Building a Survival Tree
Constructing a...

