BUPD: A Bayesian under-parameterized basket design with the unit information prior in oncology trials
Ryo Kitabayashi, Hiroyuki Sato, and Akihiro Hirakawa

TL;DR
This paper introduces BUPD, a Bayesian basket trial design that uses minimal parameters to control information sharing across cancer types, improving error control and power in oncology trials.
Contribution
BUPD is a novel Bayesian basket design that simplifies heterogeneity modeling with only one or two parameters, enhancing flexibility and interpretability.
Findings
BUPD reduces type 1 error in scenarios with few ineffective types.
BUPD improves power when few cancer types are effective.
BUPD performs better than existing methods in simulation studies.
Abstract
Basket trials in oncology enroll multiple patients with cancer harboring identical gene alterations and evaluate their response to targeted therapies across cancer types. Several existing methods have extended a Bayesian hierarchical model borrowing information on the response rates in different cancer types to account for the heterogeneity of drug effects. However, these methods rely on several pre-specified parameters to account for the heterogeneity of response rates among different cancer types. Here, we propose a novel Bayesian under-parameterized basket design with a unit information prior (BUPD) that uses only one (or two) pre-specified parameters to control the amount of information borrowed among cancer types, considering the heterogeneity of response rates. BUPD adapts the unit information prior approach, originally developed for borrowing information from historical clinical…
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Taxonomy
TopicsStatistical Methods in Clinical Trials · Optimal Experimental Design Methods
