Bayesian sample sizes for exploratory clinical trials comparing multiple experimental treatments with a control

TL;DR

This paper introduces a Bayesian method for determining sample sizes in exploratory clinical trials that compare multiple treatments to a control, balancing prior beliefs and statistical power to efficiently identify promising treatments.

Contribution

It develops a Bayesian sample size calculation approach for multi-treatment comparisons, incorporating prior information and providing a direct link to conventional power concepts.

Findings

Bayesian approach reduces required sample sizes compared to traditional methods.

Method ensures at least one promising treatment is identified with high confidence.

Illustrations demonstrate practical application and advantages over frequentist techniques.

Abstract

In this paper, a Bayesian approach is developed for simultaneously comparing multiple experimental treatments with a common control treatment in an exploratory clinical trial. The sample size is set to ensure that, at the end of the study, there will be at least one treatment for which the investigators have a strong belief that it is better than control, or else they have a strong belief that none of the experimental treatments are substantially better than control. This criterion bears a direct relationship with conventional frequentist power requirements, while allowing prior opinion to feature in the analysis with a consequent reduction in sample size. If it is concluded that at least one of the experimental treatments shows promise, then it is envisaged that one or more of these promising treatments will be developed further in a definitive phase III trial. The approach is developed in the context of normally distributed responses sharing a common standard deviation regardless of treatment. To begin with, the standard deviation will be assumed known when the sample size is calculated. The final analysis will not rely upon this assumption, although the intended properties of the design may not be achieved if the anticipated standard deviation turns out to be inappropriate. Methods that formally allow for uncertainty about the standard deviation, expressed in the form of a Bayesian prior, are then explored. Illustrations of the sample sizes computed from the new method are presented, and comparisons are made with frequentist methods devised for the same situation.