ArticleBiometrics2017
A general statistical framework for subgroup identification and comparative treatment scoring.
Article in Biometrics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.
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Who cites it
41 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Estimating individualized treatment effects using an individual participant data meta-analysis.BMC medical research methodology · 2024Pooled it
- Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review.International journal of molecular sciences · 2023Pooled it
- Weight loss at your fingertips: personalized nutrition with fasting glucose and insulin using a novel statistical approach.European journal of clinical nutrition · 2019Trial
- Machine learning-guided personalized immunochemotherapy strategies in intrahepatic cholangiocarcinoma.JHEP reports : innovation in hepatology · 2026Article
- Inference on summaries of a model-agnostic longitudinal variable importance trajectory with application to suicide prevention.The annals of applied statistics · 2026Article
- The optimal conditioning intensity of stem cell transplantation for acute myeloid leukemia in complete remission.Annals of hematology · 2026Article
- Estimation of the interpretable heterogeneous treatment effect with causal subgroup discovery in survival outcomes.Lifetime data analysis · 2026Article
- Deriving consensus sepsis clusters via goal-directed subgroup identification in multi-omics study.Nature communications · 2025Article
- Unveiling non-small cell lung cancer treatment effect heterogeneity: a comparative analysis of statistical methods.Journal of the National Cancer Institute · 2025Article
- Machine-learning driven strategies for adapting immunotherapy in metastatic NSCLC.Nature communications · 2025Article
- Deep learning-based ranking method for subgroup and predictive biomarker identification in patients.Communications medicine · 2025Article
- Evaluating Meta-Learners to Analyze Treatment Heterogeneity in Survival Data: Application to Electronic Health Records of Pediatric Asthma Care in COVID-19 Pandemic.Statistics in medicine · 2025Article
- Efficient and Robust Transfer Learning of Optimal Individualized Treatment Regimes with Right-Censored Survival Data.Journal of machine learning research : JMLR · 2025Article
- Hierarchical taxonomy of psychopathology and personalized mental health treatment selection.Frontiers in psychiatry · 2025Review
- Identifying septic shock subgroups to tailor fluid strategies through multi-omics integration.Nature communications · 2024Article
- BioPred: an R package for biomarkers analysis in precision medicine.Bioinformatics (Oxford, England) · 2024Article
- Measuring the individualization potential of treatment individualization rules: Application to rules built with a new parametric interaction model for parallel-group clinical trials.Statistical methods in medical research · 2024Article
- Identifying Subgroups with Differential Responses to Amiodarone among Cardiac Arrest Patients with a Shockable Rhythm at Hospital Arrival using the Machine Learning Approach.Reviews in cardiovascular medicine · 2024Article
- Variable selection for individualised treatment rules with discrete outcomes.Journal of the Royal Statistical Society. Series C, Applied statistics · 2024Article
- Designing Optimal, Data-Driven Policies from Multisite Randomized Trials.Psychometrika · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Many statistical methods have recently been developed for identifying subgroups of patients who may benefit from different available treatments. Compared with the traditional outcome-modeling approaches, these methods focus on modeling interactions between the treatments and covariates while by-pass or minimize modeling the main effects of covariates because the subgroup identification only depends on the sign of the interaction. However, these methods are scattered and often narrow in scope. In this article, we propose a general framework, by weighting and A-learning, for subgroup identification in both randomized clinical trials and observational studies. Our framework involves minimum modeling for the relationship between the outcome and covariates pertinent to the subgroup identification. Under the proposed framework, we may also estimate the magnitude of the interaction, which leads to the construction of scoring system measuring the individualized treatment effect. The proposed methods are quite flexible and include many recently proposed estimators as special cases. As a result, some estimators originally proposed for randomized clinical trials can be extended to observational studies, and procedures based on the weighting method can be converted to an A-learning method and vice versa. Our approaches also allow straightforward incorporation of regularization methods for high-dimensional data, as well as possible efficiency augmentation and generalization to multiple treatments. We examine the empirical performance of several procedures belonging to the proposed framework through extensive numerical studies.
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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.