Abstract
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.
Original language | English |
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Pages (from-to) | 865-873.e4 |
Journal | Journal of Clinical Epidemiology |
Volume | 66 |
Issue number | 8 |
DOIs | |
Publication status | Published - Aug 2013 |
Bibliographical note
Copyright © 2013 Elsevier Inc. All rights reserved.Keywords
- Adult
- Age Factors
- Bias (Epidemiology)
- Brain Injuries
- Cluster Analysis
- Confidence Intervals
- Data Interpretation, Statistical
- Humans
- Meta-Analysis as Topic
- Models, Statistical
- Odds Ratio
- Prognosis
- Randomized Controlled Trials as Topic
- Smoking Cessation
- Thrombophilia
- Tobacco Use Cessation Products
- Venous Thrombosis