Xiaolei Lin1, Jiaying Lyu2, Shijie Yuan2, Dehua Bi2, Sue-Jane Wang3, Yuan Ji4. 1. School of Data Science, Fudan University, Shanghai, China. 2. Laiya Consulting, Inc, Shanghai, China. 3. US Food and Drug Administration, Silver Spring, MD. 4. Department of Public Health Sciences, The University of Chicago, Chicago, IL.
Abstract
PURPOSE: Through Bayesian inference, we propose a method called BayeSize as a reference tool for investigators to assess the sample size and its associated scientific property for phase I clinical trials. METHODS: BayeSize applies the concept of effect size in dose finding, assuming that the maximum tolerated dose can be identified on the basis of an interval surrounding its true value because of statistical uncertainty. Leveraging a decision framework that involves composite hypotheses, BayeSize uses two types of priors, the fitting prior (for model fitting) and sampling prior (for data generation), to conduct sample size calculation under the constraints of statistical power and type I error. RESULTS: Simulation results showed that BayeSize can provide reliable sample size estimation under the constraints of type I/II error rates. CONCLUSION: BayeSize could facilitate phase I trial planning by providing appropriate sample size estimation. Look-up tables and R Shiny app are provided for practical applications.
PURPOSE: Through Bayesian inference, we propose a method called BayeSize as a reference tool for investigators to assess the sample size and its associated scientific property for phase I clinical trials. METHODS: BayeSize applies the concept of effect size in dose finding, assuming that the maximum tolerated dose can be identified on the basis of an interval surrounding its true value because of statistical uncertainty. Leveraging a decision framework that involves composite hypotheses, BayeSize uses two types of priors, the fitting prior (for model fitting) and sampling prior (for data generation), to conduct sample size calculation under the constraints of statistical power and type I error. RESULTS: Simulation results showed that BayeSize can provide reliable sample size estimation under the constraints of type I/II error rates. CONCLUSION: BayeSize could facilitate phase I trial planning by providing appropriate sample size estimation. Look-up tables and R Shiny app are provided for practical applications.