Meta-analysis and subgroups
Abstract
Subgroup analysis is the process of comparing a treatment effect for two or more variants of an intervention-to ask, for example, if an intervention's impact is affected by the setting (school versus community), by the delivery agent (outside facilitator versus regular classroom teacher), by the quality of delivery, or if the long-term effect differs from the short-term effect. While large-scale studies often employ subgroup analyses, these analyses cannot generally be performed for small-scale studies, since these typically include a homogeneous population and only one variant of the intervention. This limitation can be bypassed by using meta-analysis. Meta-analysis allows the researcher to compare the treatment effect in different subgroups, even if these subgroups appear in separate studies. We discuss several statistical issues related to this procedure, including the selection of a statistical model and statistical power for the comparison. To illustrate these points, we use the example of a meta-analysis of obesity prevention.
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Because the results from our subgroup analyses are purely observational, we cannot conclude that treatment type and format caused the differences in outcomes we observed.
“Because the results from our subgroup analyses are purely observational (Borenstein & Higgins, 2013), we cannot conclude that treatment type and format caused the differences in outcomes we observed in outcomes.”
From Systematic review and meta-analysis of transdiagnostic psychological treatments for anxiety and depressive disorders in adulthood (2015) · cites this paper as Borenstein & Higgins, 2013
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- Authors
- Borenstein, M., & Higgins, J. P. T.
- Affiliations
- Biostat (United States) (US)
- University of Bristol (GB)
- Corresponding author
- Borenstein, Michael
- Venue
- Prevention Science
- Published
- 2013-03-13
- Volume
- 14
- Issue
- 2
- Pages
- 134-143
- ISSN
- 1389-4986 (ISSN-L)
- 1573-6695
- OpenAlex
- W2032737645
- Cited by
- 767
- References
- 17
- Citations by year
- 2026: 68 · 2025: 95 · 2024: 134 · 2023: 124 · 2022: 94 · 2021: 86 · 2020: 45 · 2019: 31 · 2018: 19 · 2017: 29 · 2016: 14 · 2015: 12 · 2014: 8 · 2013: 3 · 2012: 3
- FWCI
- 13.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Prevention Science (publishedVersion)
- PubMed (publishedVersion)
- Bristol Research (University of Bristol) (submittedVersion)
- Funders
- National Institutes of Health (5r44da019280-03, 5r44da022799-03, 4r44da025366-02)
- Medical Research Council
- National Institute on Drug Abuse (DA022799, DA019280, DA025366)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- subgroup analysis, meta-analysis, treatment effect heterogeneity, statistical power, statistical models, obesity prevention, delivery agents
- MeSH terms
- Data Interpretation, Statistical, Humans, Models, Theoretical, Obesity, Morbid
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