Practical Bayesian adaptive randomisation in clinical trials

Abstract

While randomisation is the established method for obtaining scientifically valid treatment comparisons in clinical trials, it sometimes is at odds with what physicians feel is good medical practice. If a physician favours one treatment over another based on personal experience or published data, it may be more appropriate ethically for that physician to use the favoured treatment, rather than enrolling patients on a randomised trial. Still, the randomised trial may later show the physician's favoured treatment to be inferior. This paper reviews a statistical method, Bayesian adaptive randomisation, that provides a practical compromise between the scientific ideal of conventional randomisation and choosing each patient's treatment based on a personal preference that may prove to be incorrect. The method will first be illustrated by a simple hypothetical example, then by a recent trial in which patients with unresectable soft tissue sarcoma were adaptively randomised between two chemotherapy regimens.

Fig. 1

Illustrations of Bayesian priors and posteriors using beta distributions.

Fig. 2

Illustration of the priors and posteriors of response probabilities θ_A and θ_B after 50, 100, 150 and 200 patients for a hypothetical trial conducted using BAR(n/2N).

Fig. 3

Illustration of the adaptive randomization probabilities over the course of a hypothetical trial conducted using BAR(n/2N).