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.

Binary outcome, Cluster, Evidence synthesis, Individual participant data meta-analysis, Individual patient data, Pooled analysis, Simulation
dx.doi.org/10.1016/j.jclinepi.2012.12.017, hdl.handle.net/1765/40727
Journal of Clinical Epidemiology
Erasmus MC: University Medical Center Rotterdam

Abo-Zaid, G, Guo, B, Deeks, J.J, Debray, T.P, Steyerberg, E.W, Moons, K.G.M, & Riley, R.D. (2013). Individual participant data meta-analyses should not ignore clustering. Journal of Clinical Epidemiology, 66(8). doi:10.1016/j.jclinepi.2012.12.017