Precision Medicine.
By Michael R. Kosorok, Eric B. Laber. 2019
237 citations recorded by the source corpus.
Precision medicine seeks to maximize the quality of healthcare by individualizing the healthcare process to the uniquely evolving health status of each patient. This endeavor spans a broad range of scientific areas including drug discovery, genetics/genomics, health communication, and causal inference all in support of evidence-based, i.e., data-driven, decision making. Precision medicine is formalized as a treatment regime which comprises a sequence of decision rules, one per decision point, which map up-to-date patient information to a recommended action. The potential actions could be the selection of which drug to use, the selection of dose, timing of administration, specific diet or exercise recommendation, or other aspects of treatment or care. Statistics research in precision medicine is broadly focused on methodological development for estimation of and inference for treatment regimes which maximize some cumulative clinical outcome. In this review, we provide an overview of this vibrant area of research and present important and emerging challenges.
Subfields: Economics and Econometrics, Statistics and Probability
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Citations in the corpus
- Learning When-to-Treat Policies (2019, methodology): “As this is a large literature spanning several fields, a comprehensive overview of the literature on dynamic decision rules is beyond the scope of this article; instead, we refer the reader to recent review papers (Vansteelandt and Joffe 2014; Kosorok and Laber 2019; Clifton and Laber 2020; Levine e”