title: Modelling semi-attributable toxicity in dual-agent phase I trials with non-concurrent drug administration.
abstract: In oncology, combinations of drugs are often used to improve treatment efficacy and/or reduce harmful side effects. Dual-agent phase I clinical trials assess drug safety and aim to discover a maximum tolerated dose combination via dose-escalation; cohorts of patients are given set doses of both drugs and monitored to see if toxic reactions occur. Dose-escalation decisions for subsequent cohorts are based on the number and severity of observed toxic reactions, and an escalation rule. In a combination trial, drugs may be administered concurrently or non-concurrently over a treatment cycle. For two drugs given non-concurrently with overlapping toxicities, toxicities occurring after administration of the first drug yet before administration of the second may be attributed directly to the first drug, whereas toxicities occurring after both drugs have been given some present ambiguity; toxicities may be attributable to the first drug only, the second drug only or the synergistic combination of both. We call this mixture of attributable and non-attributable toxicity semi-attributable toxicity. Most published methods assume drugs are given concurrently, which may not be reflective of trials with non-concurrent drug administration. We incorporate semi-attributable toxicity into Bayesian modelling for dual-agent phase I trials with non-concurrent drug administration and compare the operating characteristics to an approach where this detail is not considered. Simulations based on a trial for non-concurrent administration of intravesical Cabazitaxel and Cisplatin in early-stage bladder cancer patients are presented for several scenarios and show that including semi-attributable toxicity data reduces the number of patients given overly toxic combinations. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
authors.affiliation: MRC Biostatistics Unit Hub for Trials Methodology Research, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge, CB2 0SR, U.K.
authors.forename: Graham M
authors.initials: GM
authors.lastname: Wheeler
authors.affiliation: Cardiovascular Epidemiology Unit, Strangeways Research Laboratory, University of Cambridge, Cambridge, CB1 8RN, U.K.
authors.forename: Michael J
authors.identifier: 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, Forvie Site, Robinson Way, Cambridge, CB2 0SR, U.K.
authors.forename: Adrian P
authors.identifier: 0000-0002-0742-9040
authors.initials: AP
authors.lastname: Mander
authors.affiliation: Department of Biostatistics, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY 10032, U.S.A.
authors.forename: Shing M
authors.initials: SM
authors.lastname: Lee
authors.affiliation: Department of Biostatistics, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY 10032, U.S.A.
authors.forename: Ying Kuen K
authors.initials: YK
authors.lastname: Cheung
mesh_terms: D000465:Algorithms; D000970:Antineoplastic Agents / Q000008:administration & dosage* / Q000633:toxicity*; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000008:administration & dosage* / Q000633:toxicity*; D056808:Biostatistics; D017321:Clinical Trials, Phase I as Topic / Q000379:methods* / Q000706:statistics & numerical data; D015331:Cohort Studies; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D004334:Drug Administration Schedule; D006801:Humans; D000074021:Interatrial Block; D015233:Models, Statistical; D009369:Neoplasms / Q000188:drug therapy
chemical_list: D000970:Antineoplastic Agents
keywords: Bayesian methods; adaptive designs; dose-toxicity modelling; drug combinations; phase I trials