Joint control of consensus and evidence in Bayesian design of clinical trials

In Bayesian inference, prior distributions formalize preexperimental information and uncertainty on model parameters. Sometimes different sources of knowledge are available, possibly leading to divergent posterior distributions and inferences. Research has been recently devoted to the development of sample size criteria that guarantee agreement of posterior information in terms of credible intervals when multiple priors are available. In these articles, the goals of reaching consensus and evidence are typically kept separated. Adopting a Bayesian performance-based approach, the present article proposes new sample size criteria for superiority trials that jointly control the achievement of both minimal evidence and consensus, measured by appropriate functions of the posterior distributions. We develop both an average criterion and a more stringent criterion that accounts for the entire predictive distributions of the selected measures of minimal evidence and consensus. Methods are developed and illustrated via simulation for trials involving binary outcomes. A real clinical trial example on Covid-19 vaccine data is presented.

Automated summary

This article focuses on developing new sample size criteria for superiority trials in Bayesian inference to ensure consistency of posterior information when multiple priors are available. The study aims to control the achievement of both minimal evidence and consensus by appropriate functions of the posterior distributions, with methods developed and illustrated for trials involving binary outcomes and a real clinical trial example presented on Covid-19 vaccine data.