Abstract
Phase I clinical trials aim to identify a maximum tolerated dose (MTD), the highest possible dose that does not cause an unacceptable amount of toxicity in the patients. In trials of combination therapies, however, many different dose combinations may have a similar probability of causing a dose-limiting toxicity, and hence, a number of MTDs may exist. Furthermore, escalation strategies in combination trials are more complex, with possible escalation/de-escalation of either or both drugs. This paper investigates the properties of two existing proposed Bayesian adaptive models for combination therapy dose-escalation when a number of different escalation strategies are applied. We assess operating characteristics through a series of simulation studies and show that strategies that only allow ‘non-diagonal’ moves in the escalation process (that is, both drugs cannot increase simultaneously) are inefficient and identify fewer MTDs for Phase II comparisons. Such strategies tend to escalate a single agent first while keeping the other agent fixed, which can be a severe restriction when exploring dose surfaces using a limited sample size. Meanwhile, escalation designs based on Bayesian D-optimality allow more varied experimentation around the dose space and, consequently, are better at identifying more MTDs. We argue that for Phase I combination trials it is sensible to take forward a number of identified MTDs for Phase II experimentation so that their efficacy can be directly compared. Researchers, therefore, need to carefully consider the escalation strategy and model that best allows the identification of these MTDs. Copyright © 2012 John Wiley & Sons, Ltd.
Highlights
A decision rule to choose between admissible doses, using the posterior distribution Continue recruiting patients until either a fixed sample size is obtained the precision of a certain quantity reaches a pre-specified level
The pure variance gain strategy, Dv∗ar, could be unsafe Need to account for patient gain A solution: Further restrict admissible dose set
Six dose levels per drug TTL = 0.30, with ε = 0.025 for Dvar designs Sample size = 40 Prior as in Scenario 1 1000 simulations performed for each scenario and design/admissible dose combination (Dpat, Dvar ) × (Ωndiag, Ωdiag, Ωprev )
Summary
First experimentation of a new drug / clinical procedure in human subjects Find a safe, yet potentially effective, dose for future Phase II experimentation Seek the highest possible dose subject to toxicity constraints, known as the maximum tolerated dose (MTD). Ethical considerations require low starting dose Patients enrolled in a sequential fashion at different dose levels Bayesian adaptive designs (e.g. the CRM (O’Quigley, 1990)) used to choose the dose
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