Observational studyStatistical methods in medical research2017
Evaluating the impact of treating the optimal subgroup.
Observational study in Statistical methods in medical research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Internet interventions for mental health in university students: A systematic review and meta-analysis.International journal of methods in psychiatric research · 2019Pooled it
- Individualized prescriptive inference in ischaemic stroke.Nature communications · 2025Article
- Article
- 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
- A Machine-Learning Approach for Estimating Subgroup- and Individual-Level Treatment Effects: An Illustration Using the 65 Trial.Medical decision making : an international journal of the Society for Medical Decision Making · 2022Article
- Article
- Evaluating the heterogeneous effect of a modifiable risk factor on suicide: The case of vitamin D deficiency.International journal of methods in psychiatric research · 2022Observational
- Invited Commentary: New Directions in Machine Learning Analyses of Administrative Data to Prevent Suicide-Related Behaviors.American journal of epidemiology · 2021Article
- Propensity score weighting for causal subgroup analysis.Statistics in medicine · 2021Article
- Predictive approaches to heterogeneous treatment effects: a scoping review.BMC medical research methodology · 2020Article
- Development and Validation of a Machine Learning Individualized Treatment Rule in First-Episode Schizophrenia.JAMA network open · 2020Article
- Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System.Frontiers in psychiatry · 2020Article
- Machine learning methods for developing precision treatment rules with observational data.Behaviour research and therapy · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Suppose we have a binary treatment used to influence an outcome. Given data from an observational or controlled study, we wish to determine whether or not there exists some subset of observed covariates in which the treatment is more effective than the standard practice of no treatment. Furthermore, we wish to quantify the improvement in population mean outcome that will be seen if this subgroup receives treatment and the rest of the population remains untreated. We show that this problem is surprisingly challenging given how often it is an (at least implicit) study objective. Blindly applying standard techniques fails to yield any apparent asymptotic results, while using existing techniques to confront the non-regularity does not necessarily help at distributions where there is no treatment effect. Here, we describe an approach to estimate the impact of treating the subgroup which benefits from treatment that is valid in a nonparametric model and is able to deal with the case where there is no treatment effect. The approach is a slight modification of an approach that recently appeared in the individualized medicine literature.
Indexed as
Identifiers
What OpenQuestion holds
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.