A product of independent beta probabilities dose escalation design for dual-agent phase I trials

Adrian P. Mander*, Michael J. Sweeting

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)

Abstract

Dual-agent trials are now increasingly common in oncology research, and many proposed dose-escalation designs are available in the statistical literature. Despite this, the translation from statistical design to practical application is slow, as has been highlighted in single-agent phase I trials, where a 3+3 rule-based design is often still used. To expedite this process, new dose-escalation designs need to be not only scientifically beneficial but also easy to understand and implement by clinicians. In this paper, we propose a curve-free (nonparametric) design for a dual-agent trial in which the model parameters are the probabilities of toxicity at each of the dose combinations. We show that it is relatively trivial for a clinician's prior beliefs or historical information to be incorporated in the model and updating is fast and computationally simple through the use of conjugate Bayesian inference. Monotonicity is ensured by considering only a set of monotonic contours for the distribution of the maximum tolerated contour, which defines the dose-escalation decision process. Varied experimentation around the contour is achievable, and multiple dose combinations can be recommended to take forward to phase II. Code for R, Stata and Excel are available for implementation.

Original languageEnglish
Pages (from-to)1261-1276
Number of pages16
JournalStatistics in Medicine
Volume34
Issue number8
DOIs
Publication statusPublished - 15 Apr 2015
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Keywords

  • Adaptive design
  • Dose escalation
  • Dual-agent trial
  • Nonparametric
  • Phase I clinical trial

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