Nevin Manimala Statistics

Bayesian Sample Size Planning Tool for Phase I Dose-Finding Trials

JCO Precis Oncol. 2022 Aug;6:e2200046. doi: 10.1200/PO.22.00046.


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.

PMID:36001859 | DOI:10.1200/PO.22.00046

By Nevin Manimala

Portfolio Website for Nevin Manimala