Variance prior specification for a basket trial design using Bayesian hierarchical modeling Journal Article


Authors: Cunanan, K. M.; Iasonos, A.; Shen, R.; Gönen, M.
Article Title: Variance prior specification for a basket trial design using Bayesian hierarchical modeling
Abstract: 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 approach used to capture the correlated binary endpoints across baskets is Bayesian hierarchical modeling. We evaluate a Bayesian adaptive design in the context of a non-randomized basket trial and investigate three popular prior specifications: an inverse-gamma prior on the basket-level variance, a uniform prior and half-t prior on the basket-level standard deviation. Results: From our simulation study, we can see that 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 places sufficient mass in the tail, such as the uniform or half-t 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 and the scale parameter of the half-t prior must be larger than 1. Conclusion: Based on the simulation results, we recommend that those involved in designing basket trials that implement hierarchical modeling avoid using a prior distribution that places a majority of the 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 or half-t prior on the standard deviation. © The Author(s) 2018.
Keywords: controlled study; sensitivity analysis; bayes theorem; simulation; clinical study; false positive result; phase ii; study design; statistical parameters; human; priority journal; article; basket trial; adaptive design; bayesian method; variance prior; bayesian hierarchical modeling; variance prior specification
Journal Title: Clinical Trials
Volume: 16
Issue: 2
ISSN: 1740-7745
Publisher: Sage Publications  
Date Published: 2019-04-01
Start Page: 142
End Page: 153
Language: English
DOI: 10.1177/1740774518812779
PUBMED: 30526008
PROVIDER: scopus
DOI/URL:
Notes: Source: Scopus
Altmetric Score
MSK Authors
  1. Mithat Gonen
    726 Gonen
  2. Ronglai Shen
    131 Shen
  3. Alexia Elia Iasonos
    188 Iasonos
  4. Kristen   Cunanan
    14 Cunanan