Bayesian response-adaptive designs for basket trials

Steffen Ventz, William T. Barry, Giovanni Parmigiani, Lorenzo Trippa

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


We develop a general class of response-adaptive Bayesian designs using hierarchical models, and provide open source software to implement them. Our work is motivated by recent master protocols in oncology, where several treatments are investigated simultaneously in one or multiple disease types, and treatment efficacy is expected to vary across biomarker-defined subpopulations. Adaptive trials such as I-SPY-2 (Barker et al., 2009) and BATTLE (Zhou et al., 2008) are special cases within our framework. We discuss the application of our adaptive scheme to two distinct research goals. The first is to identify a biomarker subpopulation for which a therapy shows evidence of treatment efficacy, and to exclude other subpopulations for which such evidence does not exist. This leads to a subpopulation-finding design. The second is to identify, within biomarker-defined subpopulations, a set of cancer types for which an experimental therapy is superior to the standard-of-care. This goal leads to a subpopulation-stratified design. Using simulations constructed to faithfully represent ongoing cancer sequencing projects, we quantify the potential gains of our proposed designs relative to conventional non-adaptive designs.

Original languageEnglish (US)
Pages (from-to)905-915
Number of pages11
Issue number3
StatePublished - Sep 2017
Externally publishedYes

Bibliographical note

Funding Information:
In addition Giovanni Parmigiani was supported by the NCI grant 5P30 CA006516-46, and Lorenzo Trippa was supported by the Claudia Adams Barr Program in Innovative Cancer and the Innovation in Regulatory Science Burroughs Wellcome Fund.

Publisher Copyright:
© 2017, The International Biometric Society


  • Adaptive randomization
  • Bayesian hierarchical models
  • Master protocols
  • Ulti-arm clinical trials


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