title: Bayesian Baskets: A Novel Design for Biomarker-Based Clinical Trials.
abstract: Purpose Biomarker-based clinical trials provide efficiencies during therapeutic development and form the foundation for precision medicine. These trials must generate information on both experimental therapeutics and putative predictive biomarkers in the context of varying pretrial information. We generated an efficient, flexible design that accommodates various pretrial levels of evidence supporting the predictive capacity of biomarkers while making pretrial design choices explicit. Methods We generated a randomization procedure that explicitly incorporates pretrial estimates of the predictive capacity of biomarkers. To compare the utility of this Bayesian basket (BB) design with that of a balanced randomized, biomarker agnostic (BA) design and a traditional basket (TB) design that includes only biomarker-positive patients, we iteratively simulated hypothetical multiarm clinical trials under various scenarios. Results BB was more efficient than BA while generating more information on the predictive capacity of putative biomarkers than both BA and TB. For simulations of hypothetical multiarm trials of experimental therapies and associated biomarkers of varying incident frequency, BB increased power over BA in cases when the biomarker was predictive and when the experimental therapeutic worked in all patients in a variety of scenarios. BB also generated more information about the predictive capacity of biomarkers than BA and categorically relative to TB, which generates no new biomarker information. Conclusion The BB design offers an efficient way to generate information on both experimental therapeutics and the predictive capacity of putative biomarkers. The design is flexible enough to accommodate varying levels of pretrial biomarker confidence within the same platform structure and makes clinical trial design decisions more explicit.
authors.affiliation: Lorenzo Trippa and Brian Michael Alexander, Dana-Farber Cancer Institute; Lorenzo Trippa, Harvard School of Public Health; and Brian Michael Alexander, Harvard Medical School, Boston, MA.
authors.forename: Lorenzo
authors.initials: L
authors.lastname: Trippa
authors.affiliation: Lorenzo Trippa and Brian Michael Alexander, Dana-Farber Cancer Institute; Lorenzo Trippa, Harvard School of Public Health; and Brian Michael Alexander, Harvard Medical School, Boston, MA.
authors.forename: Brian Michael
authors.initials: BM
authors.lastname: Alexander
mesh_terms: D001499:Bayes Theorem; D014408:Biomarkers, Tumor / Q000378:metabolism*; D003198:Computer Simulation; D006801:Humans; D009369:Neoplasms / Q000378:metabolism* / Q000628:therapy*; D016032:Randomized Controlled Trials as Topic / Q000379:methods*
chemical_list: D014408:Biomarkers, Tumor