Abstract
This paper presents a brief overview of the recent literature on adaptive design of clinical trials from a Bayesian perspective for statistically not so sophisticated readers. Adaptive designs are attracting a keen interest in several disciplines, from a theoretical viewpoint and also—potentially—from a practical one, and Bayesian adaptive designs, in particular, have raised high expectations in clinical trials. The main conceptual tools are highlighted here, with a mention of several trial designs proposed in the literature that use these methods, including some of the registered Bayesian adaptive trials to this date. This review aims at complementing the existing ones on this topic, pointing at further interesting reading material.
Highlights
This may be thanks to the popularization by Donald Berry [7,8,9,10,11] and the efforts made by statisticians working in the pharmaceutical industry, one of the main players in the design of clinical trials, to incorporate Bayesian methods
The need to randomize treatments to patients is mandatory in all clinical trials, and is used in each Phase whenever possible, it tends to be studied with particular reference to Phase III: these are multicenter case-control studies on large patient groups
Monitoring can be based on the posterior probabilities of the hypotheses of interest, like the posterior probability that the treatment benefit lies above or below some boundary, or based on the predictive probabilities of the consequences of continuing the study; the predictive power, namely the expectation of the power function with respect to the distribution of the true underlying effect size, is often relevant when deciding on whether to stop a clinical trial for futility
Summary
A search carried out in PubMed in August 2020 has returned nearly 300 publications (half of them published in the last decade) which either propose or use Bayesian adaptive methods in the design of a clinical trial. This may be thanks to the popularization by Donald Berry [7,8,9,10,11] and the efforts made by statisticians working in the pharmaceutical industry, one of the main players in the design of clinical trials, to incorporate Bayesian methods.
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