Adaptive designs in public health: Vaccine and cluster randomized trials go Bayesian

Abstract

Clinical trials in public health—particularly those conducted in low‐ and middle‐income countries—often involve communicable and non‐communicable diseases with high disease burden and unmet needs. Trials conducted in these regions often are faced with resource limitations, so improving the efficiencies of these trials is critical. Adaptive trial designs have the potential to save trial time and resources and reduce the number of patients receiving ineffective interventions. In this paper, we provide a detailed account of the implementation of vaccine and cluster randomized trials within the framework of Bayesian adaptive trials, with emphasis on computational efficiency and flexibility with regard to stopping rules and allocation ratios. We offer an educated approach to selecting prior distributions and a data‐driven empirical Bayes method for plug‐in estimates for nuisance parameters.