Q- and A-Learning Methods for Estimating Optimal Dynamic Treatment Regimes

被引：144

作者：

Schulte, Phillip J. ^{[1
]}

Tsiatis, Anastasios A. ^{[2
]}

Laber, Eric B. ^{[2
]}

Davidian, Marie ^{[2
]}

机构：

[1] Duke Clin Res Inst, Durham, NC 27701 USA

[2] N Carolina State Univ, Dept Stat, Raleigh, NC 27695 USA

来源：

STATISTICAL SCIENCE | 2014年 / 29卷 / 04期

关键词：

Advantage learning; bias-variance trade-off; model misspecification; personalized medicine; potential outcomes; sequential decision-making; ADAPTIVE TREATMENT STRATEGIES; NESTED MEAN MODELS; CLINICAL-TRIALS; DESIGN; INFERENCE; RANDOMIZATION; DEPRESSION; REGRESSION; DECISIONS; SUBJECT;

D O I：

10.1214/13-STS450

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In clinical practice, physicians make a series of treatment decisions over the course of a patient's disease based on his/her baseline and evolving characteristics. A dynamic treatment regime is a set of sequential decision rules that operationalizes this process. Each rule corresponds to a decision point and dictates the next treatment action based on the accrued information. Using existing data, a key goal is estimating the optimal regime, that, if followed by the patient population, would yield the most favorable outcome on average. Q- and A-learning are two main approaches for this purpose. We provide a detailed account of these methods, study their performance, and illustrate them using data from a depression study.

引用

页码：640 / 661

页数：22

共 44 条

[21] A NEW APPROACH TO CAUSAL INFERENCE IN MORTALITY STUDIES WITH A SUSTAINED EXPOSURE PERIOD - APPLICATION TO CONTROL OF THE HEALTHY WORKER SURVIVOR EFFECT [J].