Estimating Design Operating Characteristics in Bayesian Adaptive Clinical Trials

TL;DR

This paper introduces efficient methods for estimating and quantifying uncertainty in the design operating characteristics of Bayesian adaptive clinical trials, especially useful in time-sensitive situations like a pandemic.

Contribution

It proposes a novel approach to model sampling distributions of Bayesian decision metrics, facilitating easier estimation of trial operating characteristics.

Findings

Effective in COVID-19 trial design with ordinal endpoints

Reduces computational burden of simulation studies

Applicable to various Bayesian adaptive trial designs

Abstract

Bayesian adaptive designs have gained popularity in all phases of clinical trials with numerous new developments in the past few decades. During the COVID-19 pandemic, the need to establish evidence for the effectiveness of vaccines, therapeutic treatments and policies that could resolve or control the crisis emphasized the advantages offered by efficient and flexible clinical trial designs. In many COVID-19 clinical trials, due to the high level of uncertainty, Bayesian adaptive designs were considered advantageous. Designing Bayesian adaptive trials, however, requires extensive simulation studies that are generally considered challenging, particularly in time-sensitive settings such as a pandemic. In this article, we propose a set of methods for efficient estimation and uncertainty quantification for design operating characteristics of Bayesian adaptive trials. Specifically, we model the sampling distribution of Bayesian probability statements that are commonly used as the basis of decision making. To showcase the implementation and performance of the proposed approach, we use a clinical trial design with an ordinal disease-progression scale endpoint that was popular among COVID-19 trial. However, the proposed methodology may be applied generally in clinical trial context where design operating characteristics cannot be obtained analytically.