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Bayesian Adaptive Designs for Phase I Trials

In: Principles and Practice of Clinical Trials

Author

Listed:
  • Michael J. Sweeting

    (University of Leicester, Department of Health Sciences
    University of Cambridge, Department of Public Health and Primary Care)

  • Adrian P. Mander

    (Cardiff University, Centre for Trials Research)

  • Graham M. Wheeler

    (Imperial College London, Imperial Clinical Trials Unit
    University College London, Cancer Research UK & UCL Cancer Trials Centre)

Abstract

Phase I trials mark the first experimentation of a new drug or combination of drugs in a human population. The primary aim of a cancer phase I trial is to seek a safe dose or range of doses suitable for phase II experimentation. Bayesian adaptive designs have long been proposed to allow safe dose escalation and dose finding within phase I trials. There are now a vast number of designs proposed for use in phase I trials though widespread application of these designs is still limited. More recent designs have focused on the incorporation of multiple sources of information into dose-finding algorithms to improve trial safety and efficiency. This chapter reviews some of the papers that extend the simple dose-escalation trial design with a binary toxicity outcome. Specifically, the chapter focuses on five key topics: (1) overdose control, (2) use of partial outcome follow-up, (3) grading of toxicity outcomes, (4) incorporation of both toxicity and efficacy information, and (5) dual-agent or dose-scheduling designs. Each extension is illustrated with an example from a real-life trial with reference to freely available software. These extensions open the way to a broader class of phase I trials being conducted, leading to safer and more efficient trials.

Suggested Citation

  • Michael J. Sweeting & Adrian P. Mander & Graham M. Wheeler, 2022. "Bayesian Adaptive Designs for Phase I Trials," Springer Books, in: Steven Piantadosi & Curtis L. Meinert (ed.), Principles and Practice of Clinical Trials, chapter 60, pages 1105-1131, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-52636-2_92
    DOI: 10.1007/978-3-319-52636-2_92
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