ArticleBMC medical research methodology2020
Predictive approaches to heterogeneous treatment effects: a scoping review.
Article in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 86 citations in OpenAlex.
- Estimating Patient-Specific Relative Benefit of Adding Biologics to Conventional Rheumatoid Arthritis Treatment: An Individual Participant Data Meta-Analysis.JAMA network open · 2023Pooled it
- Precision medicine evaluation of heterogeneity of treatment effect for a time-to-event outcome with application in a trial of Initial treatment for people living with HIV.Clinical trials (London, England) · 2025Trial
- A comparative analysis of heterogeneity in lung cancer screening effectiveness in two randomised controlled trials.Nature communications · 2025Trial
- Trial
- Applying methods for personalized medicine to the treatment of alcohol use disorder.Journal of consulting and clinical psychology · 2021Trial
- Common reporting errors in subgroup analysis: a comparison of interaction and stratified regression models.BMC medical research methodology · 2026Article
- Medical Digital Twin Technology for Interoperability: Challenges and Opportunities.Biomedical engineering and computational biology · 2026Review
- Characterizing Treatment Effect Heterogeneity Using Real-World Data.Clinical pharmacology and therapeutics · 2025Review
- Measuring the Performance of Survival Models to Personalize Treatment Choices.Statistics in medicine · 2025Article
- Integrating predictive modeling and causal inference for advancing medical science.Childhood kidney diseases · 2024Review
- Developing clinical prediction models: a step-by-step guide.BMJ (Clinical research ed.) · 2024Article
- Toward Precision in Critical Care Research: Methods for Observational and Interventional Studies.Critical care medicine · 2024Review
- Computational simulation of the potential improvement in clinical outcomes of cardiovascular diseases with the use of a personalized predictive medicine approach.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Assessing the properties of patient-specific treatment effect estimates from causal forest algorithms under essential heterogeneity.BMC medical research methodology · 2024Article
- Machine learning in precision diabetes care and cardiovascular risk prediction.Cardiovascular diabetology · 2023Review
- Performance metrics for models designed to predict treatment effect.BMC medical research methodology · 2023Article
- Methodological concerns about "concordance-statistic for benefit" as a measure of discrimination in predicting treatment benefit.Diagnostic and prognostic research · 2023Article
- Measuring the performance of prediction models to personalize treatment choice.Statistics in medicine · 2023Article
- Decision Curve Analysis for Personalized Treatment Choice between Multiple Options.Medical decision making : an international journal of the Society for Medical Decision Making · 2023Article
- A standardized framework for risk-based assessment of treatment effect heterogeneity in observational healthcare databases.NPJ digital medicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundRecent evidence suggests that there is often substantial variation in the benefits and harms across a trial population. We aimed to identify regression modeling approaches that assess heterogeneity of treatment effect within a randomized clinical trial.
methodsWe performed a literature review using a broad search strategy, complemented by suggestions of a technical expert panel.
resultsThe approaches are classified into 3 categories: 1) Risk-based methods (11 papers) use only prognostic factors to define patient subgroups, relying on the mathematical dependency of the absolute risk difference on baseline risk; 2) Treatment effect modeling methods (9 papers) use both prognostic factors and treatment effect modifiers to explore characteristics that interact with the effects of therapy on a relative scale. These methods couple data-driven subgroup identification with approaches to prevent overfitting, such as penalization or use of separate data sets for subgroup identification and effect estimation. 3) Optimal treatment regime methods (12 papers) focus primarily on treatment effect modifiers to classify the trial population into those who benefit from treatment and those who do not. Finally, we also identified papers which describe model evaluation methods (4 papers).
conclusionsThree classes of approaches were identified to assess heterogeneity of treatment effect. Methodological research, including both simulations and empirical evaluations, is required to compare the available methods in different settings and to derive well-informed guidance for their application in RCT analysis.
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.