University of North Carolina at Chapel Hill Graduate School
Doi
Abstract
In 2015 President Barack Obama announced the launch of the Precision Medicine Initiative, spurring an out pour of interest into research regarding patient-specific health. Precision medicine is the reproducible research from which health care professionals can provide targeted treatments to their patients. Two objectives in precision medicine include (i) identifying treatment-response subgroups and (ii) identifying disease subgroups. In this manuscript, we will consider a place for traditional study designs in the new age of precision medicine by presenting the machine learning tools and statistical theory necessary to do so. We begin with a newly proposed method for estimating the individualized treatment regime from crossover studies. This method expands generalized outcome weighted learning into the 2x2 crossover study framework by considering the difference in treatment response as the observed reward and correcting for carryover effects, estimated through regression methods. After, we propose a new technique for identifying disease subgroups by applying hierarchical clustering techniques to what can be interpreted as a set of denoised outcomes. These values are weighted averages of the observed and fitted outcomes, estimated by regressing on a set of features. Finally, we return to identifying treatment-response subgroups, but, in the realm of case-control studies. We again expand on generalized outcome weighted learning in addition to accounting for the difference in the covariate distribution between the selected study sample and the total population. Between this method and electronic health data, advancements for rare and expensive to study diseases may be closer than we think.Doctor of Philosoph
