Bayesian adaptive clinical trial designs for respiratory medicine

Abstract

The use of Bayesian adaptive designs for clinical trials has increased in recent years, particularly during the COVID-19 pandemic. Bayesian adaptive designs offer a flexible and efficient framework for conducting clinical trials and may provide results that are more useful and natural to interpret for clinicians, compared to traditional approaches. In this review, we provide an introduction to Bayesian adaptive designs and discuss its use in recent clinical trials conducted in respiratory medicine. We illustrate this approach by constructing a Bayesian adaptive design for a multi-arm trial that compares two non-invasive ventilation treatments to standard oxygen therapy for patients with acute cardiogenic pulmonary oedema. We highlight the benefits and some of the challenges involved in designing and implementing Bayesian adaptive trials.

Keywords: Bayesian adaptive design; Bayesian methods; adaptive trial; clinical trials; interim analysis; monitoring.

FIGURE 1

Boxplots showing the distribution of allocations (number of patients) for each treatment arm across the 10,000 simulated trials for each scenario (represented in separate plots) for the Bayesian adaptive design case study