Bayesian design and analysis of two-arm cluster randomised trials using assurance: extension to binary outcomes and comparison of MCMC and INLA

TL;DR

This paper develops Bayesian methods for designing and analyzing two-arm cluster RCTs with binary and continuous outcomes, comparing MCMC and INLA for inference, and applies these methods to a stroke trial case study.

Contribution

It introduces a Bayesian framework using assurance for sample size determination in cluster RCTs with binary outcomes and compares MCMC and INLA methods for inference.

Findings

INLA provides faster inference with comparable accuracy to MCMC in certain scenarios.

The Bayesian assurance approach effectively guides sample size selection for cluster RCTs.

Recommendations for choosing between MCMC and INLA based on computational efficiency and accuracy.

Abstract

The paper considers two different designs; a two-arm superiority cluster randomised controlled trial (RCT) with a continuous outcome, and a twoarm superiority cluster RCT with a binary outcome. From a Bayesian perspective, for the analysis of the trial we use a (generalised) linear mixed effects model. We summarise the inference for the treatment effect for a cluster RCT based on the posterior distribution. Based on this inference we use assurance to choose the sample size. We consider and compare two different methods for the inference: Markov Chain Monte Carlo (MCMC) and Integrated Nested Laplace Approximations (INLA), and consider their implications for the assurance. We consider the Specialist Pre-hospital redirection for ischemic stroke thrombectomy (SPEEDY) trial, an RCT which has co-primary outcomes of thrombectomy rate and time to thrombectomy, as a case study for the developed Bayesian RCT designs. We demonstrate our novel approach to the sample size calculation using assurance on the SPEEDY trial, based on the results of a formal prior elicitation exercise with two clinical experts. The paper considers a range of different scenarios for cluster RCTs to evaluate INLA and MCMC, to determine when each inference scheme should be used, balancing the computational cost in terms of speed and accuracy. We make recommendations for when each should be used.