ArticleEpidemiology (Cambridge, Mass.)2019
Selecting Optimal Subgroups for Treatment Using Many Covariates.
Article in Epidemiology (Cambridge, Mass.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
43 citing papers in PubMed.
- Low-Dose Aspirin for Individualized Cancer Prevention in Older Adults: A Secondary Analysis of the ASPREE Randomized Clinical Trial.JAMA oncology · 2025Trial
- Individualized transfusion decisions to minimize adverse cardiovascular outcomes in patients with acute myocardial infarction and anemia.American heart journal · 2025Trial
- The potential benefit of statin prescription based on prediction of treatment responsiveness in older individuals: an application to the PROSPER randomized controlled trial.European journal of preventive cardiology · 2024Trial
- Optimizing opioid use disorder treatment with naltrexone or buprenorphine.Drug and alcohol dependence · 2021Trial
- Optimizing Extracorporeal Cardiopulmonary Resuscitation Candidate Selection in out-of-Hospital Cardiac Arrest: A Machine-Learning Individualized Treatment Effect Approach Versus Rule-Based Criteria.Journal of the American Heart Association · 2026Article
- MOSIC: Model-Agnostic Optimal Subgroup Identification with Multi-Constraint for Improved Reliability.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2026Article
- Transforming Mental Health for Transgender and Gender-Diverse Young Adults Using Interventions to Drive Equity (TransHealthGUIDE): Protocol for a Digital Randomized Controlled Trial.JMIR research protocols · 2025Article
- Individualized prescriptive inference in ischaemic stroke.Nature communications · 2025Article
- Variable importance measures for heterogeneous treatment effects.Biometrics · 2025Article
- A novel high dimensional approach to assess heterogeneous treatment effect in claims data.American journal of epidemiology · 2025Article
- Budget Impact of Adopting Nirmatrelvir-Ritonavir for Treating COVID-19 in a Large Integrated Healthcare System.Open forum infectious diseases · 2025Article
- Article
- Heterogeneity of Cardiovascular Effects of Second-Line Glucose-Lowering Therapies in Adults With Type 2 Diabetes Across the Range of Moderate Baseline Cardiovascular Risk.Journal of the American Heart Association · 2025Article
- Optimizing treatment for depression in primary care using psychotherapy versus antidepressant medication in a low-resource setting: protocol for the OptimizeD randomized controlled trial.BMC psychiatry · 2025Article
- An evolved interpretation of Austin Bradford Hill's causal viewpoints and their influence on epidemiologic methods.American journal of epidemiology · 2025Article
- Estimating Treatment Effect Heterogeneity in Psychiatry: A Review and Tutorial With Causal Forests.International journal of methods in psychiatric research · 2025Review
- Characterizing Treatment Effect Heterogeneity Using Real-World Data.Clinical pharmacology and therapeutics · 2025Review
- Uplift modeling to determine which fluid-norepinephrine regime results in a postoperative acute kidney injury-free recovery in patients scheduled for cystectomy and urinary diversions.Frontiers in medicine · 2025Article
- A prediction model for differential resilience to the effects of combat-related stressors in US army soldiers.International journal of methods in psychiatric research · 2024Article
- Design of a multicenter randomized controlled trial of a post-discharge suicide prevention intervention for high-risk psychiatric inpatients: The Veterans Coordinated Community Care Study.International journal of methods in psychiatric research · 2024Article
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4 authors.
Funding
Abstract
We consider the problem of selecting the optimal subgroup to treat when data on covariates are available from a randomized trial or observational study. We distinguish between four different settings including: (1) treatment selection when resources are constrained; (2) treatment selection when resources are not constrained; (3) treatment selection in the presence of side effects and costs; and (4) treatment selection to maximize effect heterogeneity. We show that, in each of these cases, the optimal treatment selection rule involves treating those for whom the predicted mean difference in outcomes comparing those with versus without treatment, conditional on covariates, exceeds a certain threshold. The threshold varies across these four scenarios, but the form of the optimal treatment selection rule does not. The results suggest a move away from the traditional subgroup analysis for personalized medicine. New randomized trial designs are proposed so as to implement and make use of optimal treatment selection rules in healthcare practice.
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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.