Background: In the era of targeted therapies, clinical trials in oncology are rapidly evolving, wherein patients from multiple diseases are now enrolled and treated according to their genomic mutation(s). In such trials, known as basket trials, the different disease cohorts form the different baskets for inference. Several approaches have been proposed in the literature to efficiently use information from all baskets while simultaneously screening to find individual baskets where the drug works. Most proposed methods are developed in a Bayesian paradigm that requires specifying a prior distribution for a variance parameter, which controls the degree to which information is shared across baskets.

Methods: A common method used to capture the correlated endpoints across baskets is Bayesian hierarchical modeling. We evaluate a Bayesian adaptive design in the context of a basket trial and investigate two popular prior specifications: an inverse-gamma prior on the basket-level variance and a uniform prior on the basket-level standard deviation.

Results: From our simulation study, we see the inverse-gamma prior is highly sensitive to the input hyperparameters. When the prior mean value of the variance parameter is set to be near zero (<0.5), this can lead to unacceptably high false positive rates (>40%) in some scenarios. Thus, use of this prior requires a fully comprehensive sensitivity analysis before implementation. Alternatively, we see that a prior that moves the mass of the variance parameter away from zero, such as the uniform prior, displays desirable and robust operating characteristics over a wide range of prior specifications, with the caveat that the upper bound of the uniform prior must be larger than 1.

Conclusion: Based on our results, we recommend that those involved in designing basket trials that implement hierarchical modeling avoid using a prior distribution that places a large density mass near zero for the variance parameter. Priors with this property force the model to share information regardless of the true efficacy configuration of the baskets. Many commonly used inverse-gamma prior specifications have this undesirable property. We recommend to instead consider the more robust uniform prior on the standard deviation.


Biostatistics | Clinical Trials | Statistical Methodology