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Abstract:
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Precision medicine, the paradigm of improving clinical care through data driven approaches to tailoring treatment to the individual, is an important area of statistical and biomedical research. The approach leverages heterogeneity of patients and treatments in both observational and randomized study data to discover individualized treatment rules (ITR's) which assign treatment to patients to maximize specific clinical outcomes. Machine learning has become an increasingly utilized and evolving methodology for ITR discovery, and we discuss recent progress in this area. We present several examples for illumination, including applications in type I diabetes, bipolar disorder, and other health areas. Some practically relevant inferential and theoretical aspects are also discussed.
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