ArticleStatistics in medicine2016
Identification of subgroups with differential treatment effects for longitudinal and multiresponse variables.
Article in Statistics in medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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
9 citing papers in PubMed.
- Application of Heterogeneity of Treatment-Effects Methods: Exploratory Analyses of a Trial of Exercise-Based Interventions for Knee Osteoarthritis.Arthritis care & research · 2022Trial
- Trial
- SubgroupTE: Advancing Treatment Effect Estimation with Subgroup Identification.ACM transactions on intelligent systems and technology · 2025Article
- Heterogeneous Treatment Effect Estimation with Subpopulation Identification for Personalized Medicine in Opioid Use Disorder.Proceedings. IEEE International Conference on Data Mining · 2023Article
- Depth importance in precision medicine (DIPM): a tree- and forest-based method for right-censored survival outcomes.Biostatistics (Oxford, England) · 2022Article
- Tree-Values: Selective Inference for Regression Trees.Journal of machine learning research : JMLR · 2022Article
- Precision medicine: Subgroup identification in longitudinal trajectories.Statistical methods in medical research · 2020Article
- Subgroups from regression trees with adjustment for prognostic effects and postselection inference.Statistics in medicine · 2019Article
- Look before you leap: systematic evaluation of tree-based statistical methods in subgroup identification.Journal of biopharmaceutical statistics · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
We describe and evaluate a regression tree algorithm for finding subgroups with differential treatments effects in randomized trials with multivariate outcomes. The data may contain missing values in the outcomes and covariates, and the treatment variable is not limited to two levels. Simulation results show that the regression tree models have unbiased variable selection and the estimates of subgroup treatment effects are approximately unbiased. A bootstrap calibration technique is proposed for constructing confidence intervals for the treatment effects. The method is illustrated with data from a longitudinal study comparing two diabetes drugs and a mammography screening trial comparing two treatments and a control. Copyright © 2016 John Wiley & Sons, Ltd.
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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.