Precision Medicine

Abstract

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.

Keywords: data-driven decision science; dynamic treatment regimes; machine learning; patient heterogeneity; statistical inference.

Figure 1

Left: schematic for a prognostic biomarker. Center: schematic for a moderating biomarker. Right: schematic for a prescriptive biomarker.

Figure 2

Two-stage SMART for evaluating Pain Coping Skills Training (PCST) for cancer pain management. In the first stage, subjects are randomized to receive either PCST-Full or PCST-Brief. At the second stage, responders are randomized to a maintenance therapy or no further treatments whereas non-responders are randomly assigned to a maintenance therapy or more intensive treatment. See Kelleher et al (2017) for additional trial details.