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632 – Statistical Issues Specific the Therapeutic Areas-4
Optimal Treatment Recommendation via Subgroup Identification in Randomized Control Trials
Yang Zhao
Gilead Sciences
Haoda Fu
Eli Lilly and Company Corporate Center
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.