Bayesian models for survival data of clinical trials: Comparison of implementations using R software

TL;DR

This paper compares different R software implementations of Bayesian survival analysis in clinical trials, demonstrating their consistency with traditional methods and highlighting their ability to provide probabilistic insights.

Contribution

It offers practical guidance on applying Bayesian models in R for survival data, comparing various packages and assumptions in a real clinical trial context.

Findings

Bayesian hazard ratio estimates closely match maximum likelihood results.

Different R packages yield consistent Bayesian posterior estimates.

Bayesian models offer unique probabilistic statements not available with traditional methods.

Abstract

Objective: To provide guidance for the use of the main functions available in R for performing post hoc Bayesian analysis of a randomized clinical trial with a survival endpoint using proportional hazard models. Study Design and Setting: Data derived from the ALLOZITHRO trial, conducted with 465 patients after allograft to prevent pulmonary complications and allocated between azithromycin and placebo; airflow decline-free survival at 2 years after randomization was the main endpoint. Results: Despite heterogeneity in modeling assumptions, in particular for the baseline hazard (parametric or nonparametric), and in estimation methods, Bayesian posterior mean hazard ratio (HR) estimates of azithromycin effect were close to those obtained by the maximum likelihood approach. Conclusion: Bayesian models can be implemented using various R packages, providing results in close agreement with the maximum likelihood estimates. These models provide probabilistic statements that could not be obtained otherwise.