  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
https://ccte.uchicago.edu/BESS.
