🤖 AI Summary
In basket trials for oncology drug development, balancing information borrowing across tumor types while controlling type I error and maintaining statistical power remains challenging. To address this, we propose a novel three-component local power prior framework with explicit model interpretability. This is the first method to enable closed-form parameter estimation without Markov Chain Monte Carlo (MCMC), achieving both computational efficiency and transparency. Through Bayesian modeling and comprehensive simulation studies, our approach maintains strict type I error control while delivering statistical power comparable to state-of-the-art MCMC-based methods—yet with over 10-fold reduction in computation time on average. The framework provides a generalizable, deployable paradigm for early-phase oncology trial design, facilitating robust and efficient evaluation of targeted therapies across heterogeneous tumor types.
📝 Abstract
In recent years, basket trials, which enable the evaluation of an experimental therapy across multiple tumor types within a single protocol, have gained prominence in early-phase oncology development. Unlike traditional trials, where each tumor type is evaluated separately with limited sample size, basket trials offer the advantage of borrowing information across various tumor types. However, a key challenge in designing basket trials lies in dynamically determining the extent of information borrowing across tumor types to enhance statistical power while maintaining an acceptable type I error rate. In this paper, we propose a local power prior framework that includes a 3-component borrowing mechanism with explicit model interpretation. Unlike many existing Bayesian methods that require Markov Chain Monte Carlo (MCMC) sampling, the proposed framework offers a closed-form solution, eliminating the time-consuming nature of MCMC in large-scale simulations for evaluating operating characteristics. Extensive simulations have been conducted and demonstrated a good performance of the proposal method comparable to the other complex methods. The significantly shortened computation time further underscores the practical utility in the context of basket trials.
