On an Approach to Bayesian Sample Sizing in Clinical Trials

TL;DR

This paper proposes a Bayesian method for determining sample sizes in clinical trials, using posterior probabilities and marginal data distributions, to improve trial success criteria without mixing frequentist concepts.

Contribution

It introduces a proper Bayesian approach for sample size calculation in clinical trials, avoiding frequentist-Bayesian mixing and using posterior probability criteria.

Findings

Demonstrates the approach on a drug superiority trial

Provides a criterion based on posterior probability for trial success

Shows how to select sample sizes using the proposed Bayesian method

Abstract

This paper explores an approach to Bayesian sample size determination in clinical trials. The approach falls into the category of what is often called "proper Bayesian", in that it does not mix frequentist concepts with Bayesian ones. A criterion for a "successful trial" is defined in terms of a posterior probability, its probability is assessed using the marginal distribution of the data, and this probability forms the basis for choosing sample sizes. We illustrate with a standard problem in clinical trials, that of establishing superiority of a new drug over a control.