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Abstract
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Due to heterogeneity of many chronic diseases, precise personalized medicine, also known as precision medicine, has drawn increasing attentions in the scientic community. One main goal of precision medicine is to develop the most effective tailored therapy for each individual patient. To that end, one needs to incorporate individual characteristics to detect a proper individual treatment rule, by which suitable decisions on treatment assignments can be made to optimize patients' clinical outcome. In this talk, I will present new statistical learning techniques which directly target the optimal individual treatment rule. Both binary and multi-arm treatments are considered. Theoretical and numerical comparisons with several existing methods will be presented to demonstrate the effectiveness of the proposed methods.
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