A Bayesian Decision-Theoretic Design for Simultaneous Biomarker-Based Subgroup Selection and Efficacy Evaluation

Zheyu Wang, Fujun Wang, Chenguang Wang, Jianliang Zhang, Hao Wang, Li Shi, Zhuojun Tang, Gary L. Rosner

Research output: Contribution to journalArticlepeer-review

Abstract

The success of drug development of targeted therapy often hinges on an appropriate selection of the sensitive patient population, mostly based on patients’ biomarker levels. At the planning stage of a Phase II study, although a potential biomarker may have been identified, a threshold value for defining sensitive patient population is often unavailable for adopting many existing biomarker-guided designs. To address this issue, we propose a two-stage design that allows for simultaneous biomarker threshold selection and efficacy evaluation while accommodating situations where the drug is efficacious in the entire patient population. The design uses a Bayesian decision-theoretic approach and incorporates the benefit and cost considerations of the study into a utility function. The operating characteristics of the proposed design under different scenarios are investigated via simulations. We also provide a discussion on the choice of the benefit and cost parameters in practice.

Original languageEnglish (US)
JournalStatistics in Biopharmaceutical Research
DOIs
StateAccepted/In press - 2021

Keywords

  • Bayesian decision-theoretic
  • Biomarker
  • Phase II trial
  • Subgroup selection
  • Two-stage design

ASJC Scopus subject areas

  • Statistics and Probability
  • Pharmaceutical Science

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