Design of Bayesian Clinical Trials with Clustered Data

TL;DR

This paper introduces a computationally efficient method for evaluating the operating characteristics of Bayesian clinical trials with clustered data, reducing the need for extensive simulations across multiple configurations.

Contribution

It develops a theoretical framework that models posterior probabilities as functions of cluster counts, enabling rapid assessment of trial design parameters.

Findings

The method accurately estimates operating characteristics using minimal simulations.

Theoretical results link posterior probabilities to the number of clusters.

Application demonstrated on a real cluster-randomized Bayesian trial.

Abstract

In the design of clinical trials, it is essential to assess the design operating characteristics (e.g., power and the type I error rate). Common practice for the evaluation of operating characteristics in Bayesian clinical trials relies on estimating the sampling distribution of posterior summaries via Monte Carlo simulation. It is computationally intensive to repeat this estimation process for each design configuration considered, particularly for clustered data that are analyzed using complex, high-dimensional models. In this paper, we propose an efficient method to assess operating characteristics and determine sample sizes for Bayesian trials with clustered data. We prove theoretical results that enable posterior probabilities to be modeled as a function of the number of clusters. Using these functions, we assess operating characteristics at a range of sample sizes given simulations conducted at only two cluster counts. These theoretical results are also leveraged to quantify the impact of simulation variability on our sample size recommendations. The applicability of our methodology is illustrated using an example cluster-randomized Bayesian clinical trial.