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Activity Number: 632 - Statistical Issues Specific the Therapeutic Areas-4
Type: Contributed
Date/Time: Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract #329221 Presentation
Title: Optimal Treatment Recommendation via Subgroup Identification in Randomized Control Trials
Author(s): Yang (Grace) Zhao* and Haoda Fu
Companies: Gilead Sciences and Eli Lilly and Company
Keywords: multiple treatments; personalized medicine; randomized control trials; subgroup identification; value function; machine learning

In an era of rapid medical treatment development and with various options available to patients, personalized medicine has become an important topic to both researchers and practitioners. A new subgroup identification algorithm developed by Fu et al. (2016) provides individualized treatment recommendation under the outcome weighted learning framework. We here focus on its applications in randomized clinical trials to generate easy-to-interpret results. We applied this method to a dataset from a real clinical trial, and identified the optimal treatment recommendations for patient subgroups.

Authors who are presenting talks have a * after their name.

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