Individual participant data meta-analyses should not ignore clustering
Objectives Individual participant data (IPD) meta-analyses often analyze their IPD as if coming from a single study. We compare this approach with analyses that rather account for clustering of patients within studies. Study Design and Setting Comparison of effect estimates from logistic re...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Published: |
Elsevier
2013
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| Online Access: | https://eprints.nottingham.ac.uk/31498/ |
| _version_ | 1848794214953910272 |
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| author | Abo-Zaid, Ghada Guo, Boliang Deeks, Jonathan J. Debray, Thomas P.A. Steyerberg, Ewout W. Moons, Karel G.M. Riley, Richard David |
| author_facet | Abo-Zaid, Ghada Guo, Boliang Deeks, Jonathan J. Debray, Thomas P.A. Steyerberg, Ewout W. Moons, Karel G.M. Riley, Richard David |
| author_sort | Abo-Zaid, Ghada |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Objectives
Individual participant data (IPD) meta-analyses often analyze their IPD as if coming from a single study. We compare this approach with analyses that rather account for clustering of patients within studies.
Study Design and Setting
Comparison of effect estimates from logistic regression models in real and simulated examples.
Results
The estimated prognostic effect of age in patients with traumatic brain injury is similar, regardless of whether clustering is accounted for. However, a family history of thrombophilia is found to be a diagnostic marker of deep vein thrombosis [odds ratio, 1.30; 95% confidence interval (CI): 1.00, 1.70; P = 0.05] when clustering is accounted for but not when it is ignored (odds ratio, 1.06; 95% CI: 0.83, 1.37; P = 0.64). Similarly, the treatment effect of nicotine gum on smoking cessation is severely attenuated when clustering is ignored (odds ratio, 1.40; 95% CI: 1.02, 1.92) rather than accounted for (odds ratio, 1.80; 95% CI: 1.29, 2.52). Simulations show models accounting for clustering perform consistently well, but downwardly biased effect estimates and low coverage can occur when ignoring clustering.
Conclusion
Researchers must routinely account for clustering in IPD meta-analyses; otherwise, misleading effect estimates and conclusions may arise. |
| first_indexed | 2025-11-14T19:12:39Z |
| format | Article |
| id | nottingham-31498 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:12:39Z |
| publishDate | 2013 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-314982020-05-04T20:18:56Z https://eprints.nottingham.ac.uk/31498/ Individual participant data meta-analyses should not ignore clustering Abo-Zaid, Ghada Guo, Boliang Deeks, Jonathan J. Debray, Thomas P.A. Steyerberg, Ewout W. Moons, Karel G.M. Riley, Richard David Objectives Individual participant data (IPD) meta-analyses often analyze their IPD as if coming from a single study. We compare this approach with analyses that rather account for clustering of patients within studies. Study Design and Setting Comparison of effect estimates from logistic regression models in real and simulated examples. Results The estimated prognostic effect of age in patients with traumatic brain injury is similar, regardless of whether clustering is accounted for. However, a family history of thrombophilia is found to be a diagnostic marker of deep vein thrombosis [odds ratio, 1.30; 95% confidence interval (CI): 1.00, 1.70; P = 0.05] when clustering is accounted for but not when it is ignored (odds ratio, 1.06; 95% CI: 0.83, 1.37; P = 0.64). Similarly, the treatment effect of nicotine gum on smoking cessation is severely attenuated when clustering is ignored (odds ratio, 1.40; 95% CI: 1.02, 1.92) rather than accounted for (odds ratio, 1.80; 95% CI: 1.29, 2.52). Simulations show models accounting for clustering perform consistently well, but downwardly biased effect estimates and low coverage can occur when ignoring clustering. Conclusion Researchers must routinely account for clustering in IPD meta-analyses; otherwise, misleading effect estimates and conclusions may arise. Elsevier 2013-08 Article NonPeerReviewed Abo-Zaid, Ghada, Guo, Boliang, Deeks, Jonathan J., Debray, Thomas P.A., Steyerberg, Ewout W., Moons, Karel G.M. and Riley, Richard David (2013) Individual participant data meta-analyses should not ignore clustering. Journal of Clinical Epidemiology, 66 (8). 865-873.e4. ISSN 1878-5921 http://www.sciencedirect.com/science/article/pii/S0895435613000723 doi:10.1016/j.jclinepi.2012.12.017 doi:10.1016/j.jclinepi.2012.12.017 |
| spellingShingle | Abo-Zaid, Ghada Guo, Boliang Deeks, Jonathan J. Debray, Thomas P.A. Steyerberg, Ewout W. Moons, Karel G.M. Riley, Richard David Individual participant data meta-analyses should not ignore clustering |
| title | Individual participant data meta-analyses should not ignore clustering |
| title_full | Individual participant data meta-analyses should not ignore clustering |
| title_fullStr | Individual participant data meta-analyses should not ignore clustering |
| title_full_unstemmed | Individual participant data meta-analyses should not ignore clustering |
| title_short | Individual participant data meta-analyses should not ignore clustering |
| title_sort | individual participant data meta-analyses should not ignore clustering |
| url | https://eprints.nottingham.ac.uk/31498/ https://eprints.nottingham.ac.uk/31498/ https://eprints.nottingham.ac.uk/31498/ |