A Bayesian adaptive feasibility design for rare diseases

Abstract

It is important for researchers to carefully assess the feasibility of a clinical trial prior to the launch of the study. One feasibility aspect that needs to be considered includes whether investigators can expect to successfully achieve the sample size needed for their trial. In this manuscript, we present a Bayesian design in which data collected during a pilot study is used to predict the feasibility of a planned phase III trial. Specifically, we outline a model that predicts a target sample size obtained from the Gamma-Poisson distribution. In a simulation study, we showcase the utility of the proposed design by applying it to a phase III trial designed to assess the efficacy of mycophenolate mofetil in individuals with mild systemic sclerosis. We demonstrate that the predictive nature of the proposed design is particularly useful for rare disease clinical trials and has the potential to greatly increase their efficiency.