Guanhua Chen, Vanderbilt University

"Personalized Dose Finding Using Outcome Weighted Learning"

In this talk, we discuss recent developed outcome weighted learning (O-learning) framework for precision medicine. The O-learning directly estimates optimal treatment rules without modeling the primary outcome as a function of patient-level features. The new approach helps leverage treatment heterogeneity to discover treatment rules with complex options, including options on a continuum such as dose level or administration timing. We propose a clinical trial design where candidate dose levels are randomly assigned from a continuous distribution within a safe range. An O-learning method, based on a non-convex loss function, is used to efficiently estimate the individualized dose rule (IDR) via a difference of convex functions algorithm. Consistency and convergence rates for the estimated IDR are derived, and the approach is evaluated via simulation studies and an analysis of data from a cohort study for Warfarin (an anti-thrombotic drug) dosing.

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