Cited by 505Statistical Methods in Clinical TrialsAdvanced Causal Inference TechniquesMeta-analysis and systematic reviews

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Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal

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Kent, David M, Rothwell, Peter M, Ioannidis, John PA, Altman, Doug G, Hayward, Rodney A·2010·Trials

DOI 10.1186/1745-6215-11-85PMC PMC2928211

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Abstract

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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Charlson, M., Szatrowski, T. P., Peterson, J., & Gold, J. (1994) · Journal of Clinical Epidemiology · doi:10.1016/0895-4356(94)90129-5 · Cited by 6,587
Validation of Clinical Classification Schemes for Predicting Stroke: Results From the National Registry of Atrial Fibrillation
Gage, B. F., Waterman, A. D., Shannon, W., Boechler, M., Rich, M. W., & Radford, M. J. (2001) · JAMA · doi:10.1001/jama.285.22.2864 · Cited by 4,832
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Antman, E. M., Cohen, M. C., Bernink, P. J. L. M., McCabe, C. H., Horacek, T., Papuchis, G., Mautner, B., Corbalán, R., Radley, D. R., & Braunwald, E. (2000) · JAMA · doi:10.1001/jama.284.7.835 · Cited by 3,221
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Bender, R., & Lange, S. (2001) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(00)00314-0 · Cited by 3,168
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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
ORCID: Kent, David M
Rothwell, Peter M
Ioannidis, John PA
Hayward, Rodney A
Venue: Trials
Publisher: Springer Science and Business Media LLC
Published: 2010-08-12
Volume: 11
Issue: 1
Pages: 85-85
ISSN: 1745-6215 (ISSN-L)
DOI: 10.1186/1745-6215-11-85
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: cc-by
creativecommons.org/licenses/by/2.0 (tdm)
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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Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal

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