ReviewJournal of clinical epidemiology2018
Considerations when assessing heterogeneity of treatment effect in patient-centered outcomes research.
Review in Journal of clinical epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses 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
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Addressing Systematic Missing Data in the Context of Causally Interpretable Meta-analysis.Prevention science : the official journal of the Society for Prevention Research · 2023Pooled it
- Controversy and Debate : Questionable utility of the relative risk in clinical research: Paper 4 :Odds Ratios are far from "portable" - A call to use realistic models for effect variation in meta-analysis.Journal of clinical epidemiology · 2022Pooled it
- Which lumbar spinal stenosis patients will improve with nonsurgical treatment? A secondary analysis of a randomized controlled trial.Chiropractic & manual therapies · 2025Trial
- Multidimensional Recovery Trajectories Following Physiotherapy with or Without Pain Education in People with Chronic Low Back Pain.Journal of clinical medicine · 2026Article
- An introduction to implementation science in rehabilitation medicine.PM & R : the journal of injury, function, and rehabilitation · 2025Article
- Unlocking therapeutic potential in traumatic brain injury: exploring microenvironmental targets, signaling pathways and translational hurdles.Inflammopharmacology · 2025Review
- An evolved interpretation of Austin Bradford Hill's causal viewpoints and their influence on epidemiologic methods.American journal of epidemiology · 2025Article
- A General Framework to Assess Complex Heterogeneity in the Strength of a Surrogate Marker.Statistics in medicine · 2025Article
- Toward Precision in Critical Care Research: Methods for Observational and Interventional Studies.Critical care medicine · 2024Review
- Assessing the properties of patient-specific treatment effect estimates from causal forest algorithms under essential heterogeneity.BMC medical research methodology · 2024Article
- Clinical data mining: challenges, opportunities, and recommendations for translational applications.Journal of translational medicine · 2024Review
- Quantifying and reducing inequity in average treatment effect estimation.BMC medical research methodology · 2023Article
- A quantitative assessment of the frequency and magnitude of heterogeneous treatment effects in studies of the health effects of social policies.SSM - population health · 2023Article
- Assessing Heterogeneity of Treatment Effect in Real-World Data.Annals of internal medicine · 2023Article
- On the Need to Revitalize Descriptive Epidemiology.American journal of epidemiology · 2022Article
- Heterogeneous treatment effects in social policy studies: An assessment of contemporary articles in the health and social sciences.Annals of epidemiology · 2022Article
- Conducting Real-world Evidence Studies on the Clinical Outcomes of Diabetes Treatments.Endocrine reviews · 2021Article
- Subgroups of Foot-Ankle Movement Patterns Can Influence the Responsiveness to a Foot-Core Exercise Program: A Hierarchical Cluster Analysis.Frontiers in bioengineering and biotechnology · 2021Article
- How subgroup analyses can miss the trees for the forest plots: A simulation study.Journal of clinical epidemiology · 2020Article
- The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement.Annals of internal medicine · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
When baseline risk of an outcome varies within a population, the effect of a treatment on that outcome will vary on at least one scale (e.g., additive, multiplicative). This treatment effect heterogeneity is of interest in patient-centered outcomes research. Based on a literature review and solicited expert opinion, we assert the following: (1) Treatment effect heterogeneity on the additive scale is most interpretable to health-care providers and patients using effect estimates to guide treatment decision-making; heterogeneity reported on the multiplicative scale may be misleading as to the magnitude or direction of a substantively important interaction. (2) The additive scale may give clues about sufficient-cause interaction, although such interaction is typically not relevant to patients' treatment choices. (3) Statistical modeling need not be conducted on the same scale as results are communicated. (4) Statistical testing is one tool for investigations, provided important subgroups are identified a priori, but test results should be interpreted cautiously given nonequivalence of statistical and clinical significance. (5) Qualitative interactions should be evaluated in a prespecified manner for important subgroups. Principled analytic plans that take into account the purpose of investigation of treatment effect heterogeneity are likely to yield more useful results for guiding treatment decisions.
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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.