A Bayesian adaptive design for estimating the maximum tolerated dose curve using drug combinations in cancer phase I clinical trials. (7th April 2016)

• Record Type: Journal Article
• Authors: Tighiouart, Mourad; Li, Quanlin; Rogatko, André
• Other Names: Cheung Ken guestEditor.; Iasonos Alexia guestEditor.; Jaki Thomas guestEditor.; O'Quigley John guestEditor.
• Is Part Of: Statistics in medicine. Volume 36:Number 2(2017)
• Page Start: 280
• Page End: 290
• Publication Date: 2016-04-07
• Subjects: cancer phase I trials -- maximum tolerated dose curve -- escalation with overdose control -- drug combination -- dose limiting toxicity -- continuous dose -- discrete dose; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.6961
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract: We present a cancer phase I clinical trial design of a combination of two drugs with the goal of estimating the maximum tolerated dose curve in the two‐dimensional Cartesian plane. A parametric model is used to describe the relationship between the doses of the two agents and the probability of dose limiting toxicity. The model is re‐parameterized in terms of the probabilities of toxicities at dose combinations corresponding to the minimum and maximum doses available in the trial and the interaction parameter. Trial design proceeds using cohorts of two patients receiving doses according to univariate escalation with overdose control (EWOC), where at each stage of the trial, we seek a dose of one agent using the current posterior distribution of the MTD of this agent given the current dose of the other agent. The maximum tolerated dose curve is estimated as a function of Bayes estimates of the model parameters. Performance of the trial is studied by evaluating its design operating characteristics in terms of safety of the trial and percent of dose recommendation at dose combination neighborhoods around the true MTD curve and under model misspecifications for the true dose–toxicity relationship. The method is further extended to accommodate discrete dose combinations and compared with previous approaches under several scenarios. Copyright © 2016 John Wiley & Sons, Ltd.