ArticleInternational journal of epidemiology2016
Using group data to treat individuals: understanding heterogeneous treatment effects in the age of precision medicine and patient-centred evidence.
Article in International journal of epidemiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 6 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
78 citing papers in PubMed, 6 syntheses or guidelines pooled it, 134 citations in OpenAlex.
- Do all individuals benefit equally from non-surgical periodontal therapy? Secondary analyses of systematic review data.Journal of periodontal research · 2025Pooled it
- Age and Sex Differences in Efficacy of Treatments for Type 2 Diabetes: A Network Meta-Analysis.JAMA · 2025Pooled 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
- Causally Interpretable Meta-analysis: Application in Adolescent HIV Prevention.Prevention science : the official journal of the Society for Prevention Research · 2022Pooled it
- A systematic review of subgroup analyses in randomised clinical trials in cardiovascular disease.Clinical trials (London, England) · 2021Pooled it
- Pooled it
- Relationship Between Phenotyping and Individualized Absolute Risk Differences in Sepsis: A Secondary Analysis of Two Approaches in Two Multicenter Trials.Critical care explorations · 2025Trial
- Association between intensive blood pressure lowering and stroke-free survival among patients with and without Diabetes.Scientific reports · 2024Trial
- Sensitivity analysis using bias functions for studies extending inferences from a randomized trial to a target population.Statistics in medicine · 2023Trial
- Identifying Patients for Intensive Blood Pressure Treatment Based on Cognitive Benefit: A Secondary Analysis of the SPRINT Randomized Clinical Trial.JAMA network open · 2023Trial
- Regression-based estimation of heterogeneous treatment effects when extending inferences from a randomized trial to a target population.European journal of epidemiology · 2023Trial
- Expected individual benefit of prophylactic platelet transfusions in hemato-oncology patients based on bleeding risks.Transfusion · 2021Trial
- Patient Selection for Intensive Blood Pressure Management Based on Benefit and Adverse Events.Journal of the American College of Cardiology · 2021Trial
- Trial
- Machine Learning in Causal Effect Estimation and Causal Evaluation of ML-Enabled Interventions in Nursing-Related Research: A Scoping Review.Nursing & health sciences · 2026Article
- Generalizing Conditional Average Treatment Effects From Nested Randomized Trials to Trial-Eligible Individuals.Statistics in medicine · 2026Article
- Explainable SHAP-space fall risk profiling from electronic health records for inpatient prevention planning.npj health systems · 2026Article
- Comparison of disease risk score methods to study treatment effect heterogeneity: a simulation study.American journal of epidemiology · 2026Article
- Meta-analyses overgeneralize SGLT2 inhibitor effectiveness in population with low albuminuria, no diabetes, no heart failure and no atherovascular heart disease.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2026Article
- Estimating and discovering heterogeneous treatment effects using machine learning in epidemiological studies: a practical guide.International journal of epidemiology · 2026Review
18 more citing papers are in PubMed but not listed here.
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 at 3 institutions in 1 country.
Funding
Abstract
Although often conflated, determining the best treatment for an individual (the task of a doctor) is fundamentally different from determining the average effect of treatment in a population (the purpose of a trial). In this paper, we review concepts of heterogeneity of treatment effects (HTE) essential in providing the evidence base for precision medicine and patient-centred care, and explore some inherent limitations of using group data (e.g. from a randomized trial) to guide treatment decisions for individuals. We distinguish between person-level HTE (i.e. that individuals experience different effects from a treatment) and group-level HTE (i.e. that subgroups have different average treatment effects), and discuss the reference class problem, engendered by the large number of potentially informative subgroupings of a study population (each of which may lead to applying a different estimated effect to the same patient), and the scale dependence of group-level HTE. We also review the limitations of conventional 'one-variable-at-a-time' subgroup analyses and discuss the potential benefits of using more comprehensive subgrouping schemes that incorporate information on multiple variables, such as those based on predicted outcome risk. Understanding the conceptual underpinnings of HTE is critical for understanding how studies can be designed, analysed, and interpreted to better inform individualized clinical decisions.
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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.