BESS: A Bayesian Estimator of Sample Size

Abstract

We consider a Bayesian framework for estimating the sample size of a clinical trial. The new approach, called BESS, is built upon three pillars: Sample size of the trial, Evidence from the observed data, and Confidence of the final decision in the posterior inference. It uses a simple logic of "given the evidence from data, a specific sample size can achieve a degree of confidence in trial success." The key distinction between BESS and standard sample size estimation (SSE) is that SSE, typically based on Frequentist inference, specifies the true parameters values in its calculation to achieve properties under repeated sampling while BESS assumes possible outcome from the observed data to achieve high posterior probabilities of trial success. As a result, the calibration of the sample size is directly based on the probability of making a correct decision rather than type I or type II error rates. We demonstrate that BESS leads to a more interpretable statement for investigators, and can easily accommodates prior information as well as sample size re-estimation. We explore its performance in comparison to the standard SSE and demonstrate its usage through a case study of oncology optimization trial. An R tool is available at https://ccte.uchicago.edu/BESS.