title: Doubly-robust dynamic treatment regimen estimation via weighted least squares.
abstract: Personalized medicine is a rapidly expanding area of health research wherein patient level information is used to inform their treatment. Dynamic treatment regimens (DTRs) are a means of formalizing the sequence of treatment decisions that characterize personalized management plans. Identifying the DTR which optimizes expected patient outcome is of obvious interest and numerous methods have been proposed for this purpose. We present a new approach which builds on two established methods: Q-learning and G-estimation, offering the doubly robust property of the latter but with ease of implementation much more akin to the former. We outline the underlying theory, provide simulation studies that demonstrate the double-robustness and efficiency properties of our approach, and illustrate its use on data from the Promotion of Breastfeeding Intervention Trial.
authors: Department of Epidemiology, Biostatistics and Occupational Health McGill University, Montreal, Canada.
authors: Michael P
authors: MP
authors: Wallace
authors: Department of Epidemiology, Biostatistics and Occupational Health McGill University, Montreal, Canada.
authors: Erica E M
authors: EE
authors: Moodie
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D020000:Decision Support Systems, Clinical; D006801:Humans; D016018:Least-Squares Analysis; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000379:methods*; D057285:Precision Medicine / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Adaptive treatment strategies; Backwards induction; Dynamic treatment regimens; G-estimation; Personalized medicine; Q-learning