title: Adaptive contrast weighted learning for multi-stage multi-treatment decision-making.
abstract: Dynamic treatment regimes (DTRs) are sequential decision rules that focus simultaneously on treatment individualization and adaptation over time. To directly identify the optimal DTR in a multi-stage multi-treatment setting, we propose a dynamic statistical learning method, adaptive contrast weighted learning. We develop semiparametric regression-based contrasts with the adaptation of treatment effect ordering for each patient at each stage, and the adaptive contrasts simplify the problem of optimization with multiple treatment comparisons to a weighted classification problem that can be solved by existing machine learning techniques. The algorithm is implemented recursively using backward induction. By combining doubly robust semiparametric regression estimators with machine learning algorithms, the proposed method is robust and efficient for the identification of the optimal DTR, as shown in the simulation studies. We illustrate our method using observational data on esophageal cancer.
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.
authors.forename: Yebin
authors.initials: Y
authors.lastname: Tao
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.
authors.forename: Lu
authors.initials: L
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003657:Decision Making; D004938:Esophageal Neoplasms / Q000628:therapy; D006801:Humans; D000069550:Machine Learning / Q000706:statistics & numerical data*; D015233:Models, Statistical; D057285:Precision Medicine / Q000379:methods*; D012044:Regression Analysis
keywords: Backward induction; Causal inference; Classification; Dynamic treatment regime; Personalized medicine