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Modelling semi-attributable toxicity in dual-agent phase I trials with non-concurrent drug administration.

Published version
Peer-reviewed

Repository DOI


Type

Article

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Authors

Wheeler, Graham M 
Sweeting, Michael J  ORCID logo  https://orcid.org/0000-0003-0980-8965
Lee, Shing M 
Cheung, Ying Kuen K 

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.

Description

Keywords

Bayesian methods, adaptive designs, dose-toxicity modelling, drug combinations, phase I trials, Algorithms, Antineoplastic Agents, Antineoplastic Combined Chemotherapy Protocols, Biostatistics, Clinical Trials, Phase I as Topic, Cohort Studies, Computer Simulation, Dose-Response Relationship, Drug, Drug Administration Schedule, Humans, Interatrial Block, Models, Statistical, Neoplasms

Journal Title

Stat Med

Conference Name

Journal ISSN

0277-6715
1097-0258

Volume Title

Publisher

Wiley
Sponsorship
Medical Research Council (MR/L003120/1)
MRC (unknown)
European Research Council (268834)
British Heart Foundation (None)
G.M. Wheeler and A.P. Mander are supported by the Medical Research Council (grant number G0800860). M.J. Sweeting is supported by a European Research Council Advanced Investigator Award: EPIC-Heart (grant number 268834), the UK Medical Research Council (grant number MR/L003120/1), the British Heart Foundation and the Cambridge National Institute for Health Research Biomedical Research Centre. S.M. Lee is supported by the American Cancer Society (grant number MRSG-13-146-01-CPHPS).