Bayesian leveraging of historical control data for a clinical trial with time-to-event endpoint

TL;DR

This paper introduces a Bayesian hierarchical model for incorporating historical control data into time-to-event clinical trials, enhancing efficiency and flexibility in oncology and cardiovascular research.

Contribution

It presents a novel Bayesian meta-analytic approach that handles heterogeneity and combines individual and aggregate data for time-to-event endpoints.

Findings

Effective integration of historical data improves trial analysis.

Flexible modeling accommodates trial heterogeneity.

Application to cancer trials demonstrates practical utility.

Abstract

The recent 21st Century Cures Act propagates innovations to accelerate the discovery, development, and delivery of 21st century cures. It includes the broader application of Bayesian statistics and the use of evidence from clinical expertise. An example of the latter is the use of trial-external (or historical) data, which promises more efficient or ethical trial designs. We propose a Bayesian meta-analytic approach to leveraging historical data for time-to-event endpoints, which are common in oncology and cardiovascular diseases. The approach is based on a robust hierarchical model for piecewise exponential data. It allows for various degrees of between trial-heterogeneity and for leveraging individual as well as aggregate data. An ovarian carcinoma trial and a non-small-cell cancer trial illustrate methodological and practical aspects of leveraging historical data for the analysis and design of time-to-event trials.