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arXiv · 2608.00671

How to optimize dynamic borrowing in basket trials - A utility-based framework and results of a comparison study

Lukas D Sauer·Alexander Ritz·Meinhard Kieser
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Abstract

Basket trials are clinical trials in which one treatment is investigated in multiple subpopulations within a single trial. The subpopulations are called strata in the following. Stratified cohorts are typical but not limited to early oncological trials, where targeted therapies can be investigated in different strata defined by tumor tissue. In terms of statistical methodology, the stratification of the trial can be leveraged using information borrowing. The idea is that the strata will be analyzed separately if they respond differently to treatment, but that a stratum may share information from another if their response rates are similar. This can increase power while keeping type-I error inflation moderate. The borrowing methods can be tuned with respect to possible outcome scenarios. In this paper, we provide a framework for tuning basket trials: The compromise between power and type-I error rate is defined by a utility function which is then optimized using optimization algorithms. We investigated this framework by performing a pre-specified comparison study. Part I of the study compared optimization algorithms in terms of reliability and efficiency, part II compared the statistical performance of utility functions in simulated scenarios. This study shows how targeting different utility functions based on measures such as local power in each stratum or the number of correct decisions leads to different borrowing behavior of the optimized design. Furthermore, we discuss a mathematical counterexample which shows that no uniformly most powerful (UMP) test exists for basket trials, thus showing the limitations of basket trials. While information borrowing may result in power gains for some scenarios, there is no design which is optimal for all possible scenarios. Hence, a transparent optimization procedure is crucial when planning basket trials.

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Lukas D Sauer, Alexander Ritz, Meinhard Kieser. 2026-08-01. How to optimize dynamic borrowing in basket trials - A utility-based framework and results of a comparison study. https://arxiv.org/abs/2608.00671

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