Individual participant data meta-analyses should not ignore clustering. | Biowebspin
Journal of clinical epidemiology. 2013 07 01

Individual participant data meta-analyses should not ignore clustering.


Abo-Zaid G, Guo B, Deeks JJ, Debray TP, Steyerberg EW, Moons KG, Riley RD. European Centre for Environment and Human Health, Peninsula College of Medicine and Dentistry, University of Exeter, Knowledge Spa, Royal Cornwall Hospital, Truro, Cornwall TR1 3HD, UK.
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. Copyright © 2013 Elsevier Inc. All rights reserved.

PMID: 23651765

Grant Support

Start page
End page
Book Title
Share on social networks :
Look on Google if the pdf is available or the publication is cited in other pdfs