title: Toxicity-dependent feasibility bounds for the escalation with overdose control approach in phase I cancer trials.
abstract: Phase I trials of anti-cancer therapies aim to identify a maximum tolerated dose (MTD), defined as the dose that causes unacceptable toxicity in a target proportion of patients. Both rule-based and model-based methods have been proposed for MTD recommendation. The escalation with overdose control (EWOC) approach is a model-based design where the dose assigned to the next patient is one that, given all available data, has a posterior probability of exceeding the MTD equal to a pre-specified value known as the feasibility bound. The aim is to conservatively dose-escalate and approach the MTD, avoiding severe overdosing early on in a trial. The EWOC approach has been applied in practice with the feasibility bound either fixed or varying throughout a trial, yet some of the methods may recommend incoherent dose-escalation, that is, an increase in dose after observing severe toxicity at the current dose. We present examples where varying feasibility bounds have been used in practice, and propose a toxicity-dependent feasibility bound approach that guarantees coherent dose-escalation and incorporates the desirable features of other EWOC approaches. We show via detailed simulation studies that the toxicity-dependent feasibility bound approach provides improved MTD recommendation properties to the original EWOC approach for both discrete and continuous doses across most dose-toxicity scenarios, with comparable performance to other approaches without recommending incoherent dose escalation. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
authors.affiliation: Cancer Research UK and UCL Cancer Trials Centre, University College London, U.K.|MRC Biostatistics Unit Hub for Trials Methodology Research, Cambridge Institute of Public Health, Cambridge, U.K.
authors.forename: Graham M
authors.identifier: http://orcid.org/0000-0002-3254-5991
authors.initials: GM
authors.lastname: Wheeler
authors.affiliation: Cardiovascular Epidemiology Unit, Strangeways Research Laboratory University of Cambridge, U.K.
authors.forename: Michael J
authors.identifier: http://orcid.org/0000-0003-0980-8965
authors.initials: MJ
authors.lastname: Sweeting
authors.affiliation: MRC Biostatistics Unit Hub for Trials Methodology Research, Cambridge Institute of Public Health, Cambridge, U.K.
authors.forename: Adrian P
authors.identifier: http://orcid.org/0000-0002-0742-9040
authors.initials: AP
authors.lastname: Mander
mesh_terms: D000970:Antineoplastic Agents / Q000008:administration & dosage* / Q000633:toxicity; D001499:Bayes Theorem; D056808:Biostatistics; D017321:Clinical Trials, Phase I as Topic / Q000706:statistics & numerical data*; D003198:Computer Simulation; D062787:Drug Overdose / Q000517:prevention & control; D005240:Feasibility Studies; D006801:Humans; D020714:Maximum Tolerated Dose; D009369:Neoplasms / Q000188:drug therapy*
chemical_list: D000970:Antineoplastic Agents
keywords: Bayesian adaptive designs; dose-escalation; maximum tolerated dose; phase I trials