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Activity Number: 226
Type: Topic Contributed
Date/Time: Monday, August 5, 2013 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #309674
Title: Combination Dose-Finding for Targeted Agents: A Bayesian Case Study in Oncology
Author(s): Suman Sen*+ and Meredith Goldwasser and Stuart Bailey
Companies: Novartis Pharmaceuticals Corp and Novartis Pharmaceuticals Corp and Novartis Pharma AG
Keywords: bayesian models ; dose-escalation ; oncology ; prior ; combination therapy

Within oncology there has been a paradigm shift moving from single agent therapies to combinations of drugs. In this setting there is no longer one maximum tolerated dose (MTD) but a range of dose pairs. A Bayesian model-based framework for phase I combination trials is presented in the context of a real case study. Critical aspects of the model and prior specifications are presented, along with the need to evaluate the uncertainty of estimated toxicity rates, which allows for risk monitoring. In addition, the importance of understanding the impact of combining two or more compounds on patient safety, and to define the toxicity profile interaction is highlighted. On-study discussions around dose escalation and determination of MTD are discussed as well, which underlines the importance of good statistical guidance and clinical/scientific expertise.

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