The efficiencies of pilot feasibility trials in rare diseases using Bayesian methods

TL;DR

This paper explores how Bayesian methods can effectively incorporate pilot study data to improve the design and analysis of subsequent rare disease trials, enhancing efficiency and ethical considerations.

Contribution

It demonstrates the use of robust Bayesian priors to integrate pilot data into definitive trials, providing practical guidance for rare disease research.

Findings

Bayesian methods improve trial efficiency and ethical outcomes.

Simulation shows reduced sample sizes and trial durations.

Robust priors enhance decision-making in rare disease trials.

Abstract

Pilot feasibility studies play a pivotal role in the development of clinical trials for rare diseases, where small populations and slow recruitment often threaten trial viability. While such studies are commonly used to assess operational parameters, they also offer a valuable opportunity to inform the design and analysis of subsequent definitive trials-particularly through the use of Bayesian methods. In this paper, we demonstrate how data from a single, protocol-aligned pilot study can be incorporated into a definitive trial using robust meta-analytic-predictive priors. We focus on the case of a binary efficacy outcome, motivated by a feasibility trial of intravenous immunoglobulin tapering in autoimmune inflammatory myopathies. Through simulation studies, we evaluate the operating characteristics of trials informed by pilot data, including sample size, expected trial duration, and the probability of meeting recruitment targets. Our findings highlight the operational and ethical advantages of leveraging pilot data via robust Bayesian priors, and offer practical guidance for their application in rare disease settings.