Bayesian Estimation of Sample Size

Bayesian Estimation of Sample Size

ABSTRACT

We consider a Bayesian estimator of sample size (BESS) and an application to oncology dose optimization clinical trials. BESS is built upon three pillars, Sample size, Evidence from observed data, and Confidence in posterior inference. It uses a simple logic of "given the evidence from data, a specific sample size can achieve a degree of confidence in the posterior inference." 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 while BESS assumes possible outcome from the observed data. As a result, the calibration of the sample size is not based on type I or type II error rates, but on posterior probabilities. 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. BESS can be applied to general hypothesis tests. An R tool is available at this https URL.

BIO

Dr. Yuan Ji is Professor of Biostatistics at The University of Chicago. His research focuses on innovative Bayesian statistical methods for translational cancer research. Dr. Ji is author of over 200 publications in peer-reviewed journals including across medical and statistical journals. He is the inventor of many innovative Bayesian adaptive designs such as the mTPI and i3+3 designs, which have been widely applied in dose-finding clinical trials worldwide. His work on cancer genomics has been reported by a large number of media outlets in 2015. He received Mitchell Prize in 2015 by the International Society for Bayesian Analysis. He is an elected fellow of the American Statistical Association.