A Bayesian Estimator of Sample Size

2024-04-11

AI Summary

Sample size estimation in oncology dose-finding trials traditionally relies on assumed true parameters and fixed Type I/II error rates, limiting interpretability, flexibility, and adaptability to accumulating evidence. Method: We propose the Bayesian Estimator of Sample Size (BESS), a principled Bayesian framework that calibrates sample size using posterior probabilities derived from observed data. BESS relaxes frequentist assumptions of known truth and rigid error control, enabling incorporation of prior information and mid-trial adaptive re-estimation. It is grounded in Bayesian inference and posterior confidence modeling, implemented in open-source R software. Contribution/Results: Evaluated on real oncology clinical trials, BESS substantially improves interpretability and robustness of sample size decisions. It supports dynamic, data-driven Phase II dose-exploration designs and generalizes naturally to single- or two-sided hypothesis testing settings.

Technology Category

• Machine Learning: Bayesian Learning
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

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

Problem

• Estimates sample size using Bayesian methods for clinical trials
• Compares Bayesian and Frequentist sample size estimation approaches
• Applies method to oncology dose optimization case study

Innovation

• Bayesian estimator using observed data evidence
• Sample size based on posterior probabilities
• Accommodates prior information and re-estimation