Bayesian optimal interval designs for phase I clinical trials

Research output: Contribution to journalArticle

59 Scopus citations

Abstract

In phase I trials, effectively treating patients and minimizing the chance of exposing them to subtherapeutic and overly toxic doses are clinicians' top priority. Motived by this practical consideration, we propose Bayesian optimal interval (BOIN) designs to find the maximum tolerated dose and to minimize the probability of inappropriate dose assignments for patients. We show, both theoretically and numerically, that the BOIN design not only has superior finite and large sample properties but also can be easily implemented in a simple way similar to the traditional '3+3' design. Compared with the well-known continual reassessment method, the BOIN design yields comparable average performance to select the maximum tolerated dose but has a substantially lower risk of assigning patients to subtherapeutic and overly toxic doses. We apply the BOIN design to two cancer clinical trials.

Original languageEnglish (US)
Pages (from-to)507-523
Number of pages17
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume64
Issue number3
DOIs
StatePublished - Apr 1 2015

Keywords

  • Bayesian adaptive design
  • Decision error
  • Dose finding
  • Maximum tolerated dose

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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