Enhancing interpretability for Bayesian basket trial designs by effective sample size.

ABSTRACT:

Background

There is growing interest in utilizing Bayesian approaches to borrow information across tumor types in basket trials. Several innovative designs, primarily extensions of the Bayesian hierarchical model (BHM), have been proposed to dynamically borrow information based on observed data. However, there is no recognized solution to quantify the degree of information borrowing in such a context, posing a great challenge for non-statisticians to understand these complex designs.

Methods

The tool of effective sample size (ESS) is leveraged to Bayesian basket trials and several ESS-based borrowing strategies are proposed. The mean squared error (MSE), which explicitly accounts for the trade-off between estimation bias and variance reduction, is selected as the target meas

SUBMITTER: Chen X 

PROVIDER: S-EPMC12709804 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature