ArticleStatistics in medicine2024
Comparison of methods that combine multiple randomized trials to estimate heterogeneous treatment effects.
Article in Statistics in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05267873 (Combining Data Sources to Identify Effect Moderation for Personalized Mental Health), which is not on this map. Cited by 10 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.
Combining Data Sources to Identify Effect Moderation for Personalized Mental Health
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Botanical drug preparations and related East Asian medicine interventions for central and idiopathic central precocious puberty: an umbrella review of systematic reviews and meta-analyses.Frontiers in pharmacology · 2026Pooled it
- Using methods to extend inferences to specific target populations to improve the precision of subgroup analyses.Journal of clinical epidemiology · 2025Trial
- Bayesian Hierarchical Models With Calibrated Mixtures of g-priors for Assessing Treatment Effect Moderation in Meta-Analysis.Statistics in medicine · 2026Article
- A novel digital twin strategy to examine the implications of randomized clinical trials for real-world populations.NPJ digital medicine · 2026Article
- Modern Causal Inference Approaches to Improve Power for Subgroup Analysis in Randomized Controlled Trials.Statistics in medicine · 2026Article
- Distributed fusion R-learner of heterogeneous treatment effect using distributed medicaid data.Biometrics · 2026Article
- Precision Mental Health: Predicting Heterogeneous Treatment Effects for Depression through Data Integration.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025Article
- MicroRNAs Regulating Oxidative Stress in Human Fertility: A Narrative Review of Mechanistic Insights and Clinical Potential.Medical sciences (Basel, Switzerland) · 2025Review
- The challenges of integrating diverse data sources: A case study in major depression.Health services & outcomes research methodology · 2024Article
- Pooling Alzheimer's disease clinical trial data to develop personalized medicine approaches is easier said than done: A proof-of-principle study and call to action.Alzheimer's & dementia (New York, N. Y.)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Individualized treatment decisions can improve health outcomes, but using data to make these decisions in a reliable, precise, and generalizable way is challenging with a single dataset. Leveraging multiple randomized controlled trials allows for the combination of datasets with unconfounded treatment assignment to better estimate heterogeneous treatment effects. This article discusses several nonparametric approaches for estimating heterogeneous treatment effects using data from multiple trials. We extend single-study methods to a scenario with multiple trials and explore their performance through a simulation study, with data generation scenarios that have differing levels of cross-trial heterogeneity. The simulations demonstrate that methods that directly allow for heterogeneity of the treatment effect across trials perform better than methods that do not, and that the choice of single-study method matters based on the functional form of the treatment effect. Finally, we discuss which methods perform well in each setting and then apply them to four randomized controlled trials to examine effect heterogeneity of treatments for major depressive disorder.
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.