ArticleJournal of the American Statistical Association2017
Residual Weighted Learning for Estimating Individualized Treatment Rules.
Article in Journal of the American Statistical Association, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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Who cites it
68 citing papers in PubMed, 218 citations in OpenAlex.
- Trial
- Precision Medicine Approach to Develop and Internally Validate Optimal Exercise and Weight-Loss Treatments for Overweight and Obese Adults With Knee Osteoarthritis: Data From a Single-Center Randomized Trial.Arthritis care & research · 2021Trial
- Estimating individualized treatment regimes from crossover designs.Biometrics · 2020Trial
- Efficacy of the Flexible Lifestyles Empowering Change intervention on metabolic and psychosocial outcomes in adolescents with type 1 diabetes (FLEX): a randomised controlled trial.The Lancet. Child & adolescent health · 2018Trial
- Censoring-adjusted tree-based policy learning for estimating dynamic treatment regimes with censored outcomes.Scientific reports · 2026Article
- Integrative learning of individualized treatment rules from multiple studies with partially overlapping treatments.Biometrics · 2026Article
- Robust transfer learning for individualized treatment rules in the presence of missing data.Biostatistics (Oxford, England) · 2025Article
- REINFORCEMENT LEARNING FOR INDIVIDUAL OPTIMAL POLICY FROM HETEROGENEOUS DATA.Annals of statistics · 2025Article
- Hierarchical taxonomy of psychopathology and personalized mental health treatment selection.Frontiers in psychiatry · 2025Review
- Learning optimal dynamic treatment regimes from longitudinal data.American journal of epidemiology · 2024Article
- Estimating individualized treatment rules by optimizing the adjusted probability of a longer survival.Statistical methods in medical research · 2024Article
- Fusing Individualized Treatment Rules Using Secondary Outcomes.Proceedings of machine learning research · 2024Article
- Penalized robust learning for optimal treatment regimes with heterogeneous individualized treatment effects.Journal of applied statistics · 2024Article
- A RELUCTANT ADDITIVE MODEL FRAMEWORK FOR INTERPRETABLE NONLINEAR INDIVIDUALIZED TREATMENT RULES.The annals of applied statistics · 2023Article
- Optimizing Treatment for Human Immunodeficiency Virus to Improve Clinical Outcomes Using Precision Medicine.American journal of epidemiology · 2023Article
- The optimal dynamic treatment rule superlearner: considerations, performance, and application to criminal justice interventions.The international journal of biostatistics · 2023Article
- The Sequential Multiple Assignment Randomized Trial for Controlling Infectious Diseases: A Review of Recent Developments.American journal of public health · 2023Review
- Transfer learning of individualized treatment rules from experimental to real-world data.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments.Journal of machine learning research : JMLR · 2023Article
- BATCH POLICY LEARNING IN AVERAGE REWARD MARKOV DECISION PROCESSES.Annals of statistics · 2022Article
8 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 3 institutions in 1 country.
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Abstract
Personalized medicine has received increasing attention among statisticians, computer scientists, and clinical practitioners. A major component of personalized medicine is the estimation of individualized treatment rules (ITRs). Recently, Zhao et al. (2012) proposed outcome weighted learning (OWL) to construct ITRs that directly optimize the clinical outcome. Although OWL opens the door to introducing machine learning techniques to optimal treatment regimes, it still has some problems in performance. (1) The estimated ITR of OWL is affected by a simple shift of the outcome. (2) The rule from OWL tries to keep treatment assignments that subjects actually received. (3) There is no variable selection mechanism with OWL. All of them weaken the finite sample performance of OWL. In this article, we propose a general framework, called Residual Weighted Learning (RWL), to alleviate these problems, and hence to improve finite sample performance. Unlike OWL which weights misclassification errors by clinical outcomes, RWL weights these errors by residuals of the outcome from a regression fit on clinical covariates excluding treatment assignment. We utilize the smoothed ramp loss function in RWL, and provide a difference of convex (d.c.) algorithm to solve the corresponding non-convex optimization problem. By estimating residuals with linear models or generalized linear models, RWL can effectively deal with different types of outcomes, such as continuous, binary and count outcomes. We also propose variable selection methods for linear and nonlinear rules, respectively, to further improve the performance. We show that the resulting estimator of the treatment rule is consistent. We further obtain a rate of convergence for the difference between the expected outcome using the estimated ITR and that of the optimal treatment rule. The performance of the proposed RWL methods is illustrated in simulation studies and in an analysis of cystic fibrosis clinical trial data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.