Bayesian Adaptive Designs for Phase I Trials

Michael J. Sweeting (), Adrian P. Mander () and Graham M. Wheeler ()
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Michael J. Sweeting: University of Leicester, Department of Health Sciences
Adrian P. Mander: Cardiff University, Centre for Trials Research
Graham M. Wheeler: Imperial College London, Imperial Clinical Trials Unit

Chapter 60 in Principles and Practice of Clinical Trials, 2022, pp 1105-1131 from Springer

Abstract: 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.

Keywords: Dose finding; Dose escalation; Phase I trial design; Toxicity; CRM (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-319-52636-2_92

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