A hierarchical Bayesian design for randomized Phase II clinical trials with multiple groups

Jun Yin, Rui Qin, Daniel J. Sargent, Charles Erlichman, Qian Shi

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Enhanced knowledge of the biological and genetic basis of cancer is re-defining the target population for new treatments. In oncology, potential targets for a new therapeutic agent often include various solid and hematologic malignancies that share common signaling pathways. New agents are often tested in multiple tumor types across which information can be borrowed. We propose a hierarchical Bayesian design (HBD) to simultaneously test a novel agent in multiple groups for randomized Phase II clinical trials with binary endpoints. Compared to parallel design for individual tumor groups, the HBD has greatly reduced sample size. Therefore, this improves efficiency and decreases the financial cost of conducting randomized Phase II clinical trials. An R package hbdct has been developed to implement the HBD and streamline the sample size calibration.

Original languageEnglish (US)
Pages (from-to)451-462
Number of pages12
JournalJournal of Biopharmaceutical Statistics
Volume28
Issue number3
DOIs
StatePublished - May 4 2018

Keywords

  • Binary endpoint
  • biomarker-integrated design
  • hierarchical Bayesian design
  • randomized Phase II trial

ASJC Scopus subject areas

  • Statistics and Probability
  • Pharmacology
  • Pharmacology (medical)

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