Kosorok Michael R, Laber Eric B
Department of Biostatistics and Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, U.S.A.;
Department of Statistics, North Carolina State University, Raleight, North Carolina, 27695, U.S.A.;
Annu Rev Stat Appl. 2019 Mar;6:263-286. doi: 10.1146/annurev-statistics-030718-105251.
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.
精准医学旨在通过根据每位患者独特的健康状况演变情况对医疗过程进行个性化定制，从而实现医疗保健质量的最大化。这一努力涵盖了广泛的科学领域，包括药物研发、遗传学/基因组学、健康传播以及因果推断，所有这些都是为了支持基于证据（即数据驱动）的决策制定。精准医学被形式化为一种治疗方案，它由一系列决策规则组成，每个决策点对应一个规则，这些规则将最新的患者信息映射到推荐的行动上。潜在的行动可能包括选择使用哪种药物、选择剂量、给药时间、特定的饮食或运动建议，或者治疗或护理的其他方面。精准医学中的统计学研究广泛聚焦于治疗方案的估计和推断的方法学发展，这些治疗方案能使某些累积临床结果最大化。在本综述中，我们对这一充满活力的研究领域进行了概述，并提出了重要的和新出现的挑战。
