Using Bayesian modeling in frequentist adaptive enrichment designs

Biostatistics
Noah Simon, Richard Simon

Abstract

Our increased understanding of the mechanistic heterogeneity of diseases has pushed the development of targeted therapeutics. We do not expect all patients with a given disease to benefit from a targeted drug; only those in the target population. That is, those with sufficient dysregulation in the biomolecular pathway targeted by treatment. However, due to complexity of the pathway, and/or technical issues with our characterizing assay, it is often hard to characterize the target population until well into large-scale clinical trials. This has stimulated the development of adaptive enrichment trials; clinical trials in which the target population is adaptively learned; and enrollment criteria are adaptively updated to reflect this growing understanding. This paper proposes a framework for group-sequential adaptive enrichment trials. Building on the work of Simon & Simon (2013). Adaptive enrichment designs for clinical trials. Biostatistics 14(4), 613-625), it includes a frequentist hypothesis test at the end of the trial. However, it uses Bayesian methods to optimize the decisions required during the trial (regarding how to restrict enrollment) and Bayesian methods to estimate effect size, and characterize the target population...Continue Reading

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Citations

May 29, 2018·Biometrical Journal. Biometrische Zeitschrift·Yanxun XuDonald Berry
Sep 18, 2018·Statistics in Medicine·Ting WangStephen L George
Aug 4, 2020·Journal of Animal Science·Christopher J Bryant
Nov 30, 2019·Journal of Biopharmaceutical Statistics·Yanxun XuYili L Pritchett
Sep 15, 2020·JCO Precision Oncology·Ciara NugentYuan Ji
Jun 2, 2018·Statistics in Medicine·Peter K KimaniNigel Stallard
Jul 14, 2018·Frontiers in Immunology·Victor B TalisaDerek C Angus

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