The DOI matches its Crossref record; 75 of the 75 references it cites were found in a registry or an index.
Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal
Open accessAbstract
Mounting evidence suggests that there is frequently considerable variation in the risk of the outcome of interest in clinical trial populations. These differences in risk will often cause clinically important heterogeneity in treatment effects (HTE) across the trial population, such that the balance between treatment risks and benefits may differ substantially between large identifiable patient subgroups; the "average" benefit observed in the summary result may even be non-representative of the treatment effect for a typical patient in the trial. Conventional subgroup analyses, which examine whether specific patient characteristics modify the effects of treatment, are usually unable to detect even large variations in treatment benefit (and harm) across risk groups because they do not account for the fact that patients have multiple characteristics simultaneously that affect the likelihood of treatment benefit. Based upon recent evidence on optimal statistical approaches to assessing HTE, we propose a framework that prioritizes the analysis and reporting of multivariate risk-based HTE and suggests that other subgroup analyses should be explicitly labeled either as primary subgroup analyses (well-motivated by prior evidence and intended to produce clinically actionable results) or secondary (exploratory) subgroup analyses (performed to inform future research). A standardized and transparent approach to HTE assessment and reporting could substantially improve clinical trial utility and interpretability.
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“RCTs provide estimates of the between-subject treatment response (the average difference between the two groups) but does not provide sufficient data on how a specific individual responds to a given treatment i.e. due to heterogeneity in treatment effects [21].”
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“RCTs provide estimates of the between-subject treatment response (the average difference between the two groups) but does not provide sufficient data on how a specific individual responds to a given treatment i.e. due to heterogeneity in treatment effects [21].”
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Cites
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Why Most Published Research Findings Are False
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“Additionally, because conventional subgroup analyses involve multiple comparisons and involve splitting the overall sample to smaller sub-samples, they are both under-powered for detecting genuine subgroup effects (prone to false-negatives), and even more commonly they are prone to false positive findings [25-31].”
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Metadata
- Authors
- Kent, D. M., Rothwell, P. M., Ioannidis, J. P., Altman, D. G., & Hayward, R. A.
- Affiliations
- Tufts Medical Center (US)
- John Radcliffe Hospital (GB)
- University of Oxford (GB)
- University of Michigan (US)
- Corresponding author
- Kent, David M
- Venue
- Trials
- Published
- 2010-08-12
- Volume
- 11
- Issue
- 1
- Pages
- 85-85
- ISSN
- 1745-6215 (ISSN-L)
- PMC
- PMC2928211
- OpenAlex
- W2128780239
- Cited by
- 504
- References
- 72
- Citations by year
- 2026: 32 · 2025: 20 · 2024: 33 · 2023: 26 · 2022: 25 · 2021: 43 · 2020: 32 · 2019: 42 · 2018: 36 · 2017: 37 · 2016: 41 · 2015: 31 · 2014: 39 · 2013: 15 · 2012: 28
- FWCI
- 17.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Trials (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Pfizer
- National Institutes of Health (P60DK20572)
- Quality Enhancement Research Initiative
- National Institute of Diabetes and Digestive and Kidney Diseases (P60DK020572, P60 DK 20572)
- National Institute of Neurological Disorders and Stroke (R01NS062153)
- National Center for Research Resources (U54 RR023562)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- treatment effect heterogeneity, subgroup analysis, clinical trial reporting, treatment risks and benefits, risk stratification
- MeSH terms
- Clinical Trials as Topic, Humans, Risk Factors, Therapeutics, Reproducibility of Results, Multivariate Analysis, Risk Assessment, Evidence-Based Medicine
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