title: Individual participant data meta-analyses should not ignore clustering.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  Researchers must routinely account for clustering in IPD meta-analyses; otherwise, misleading effect estimates and conclusions may arise.
authors: 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.
authors: Ghada
authors: G
authors: Abo-Zaid
authors: Boliang
authors: B
authors: Guo
authors: Jonathan J
authors: JJ
authors: Deeks
authors: Thomas P A
authors: TP
authors: Debray
authors: Ewout W
authors: EW
authors: Steyerberg
authors: Karel G M
authors: KG
authors: Moons
authors: Richard David
authors: RD
authors: Riley
mesh_terms: D000328:Adult; D000367:Age Factors; D015982:Bias; D001930:Brain Injuries / Q000401:mortality; D016000:Cluster Analysis; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D006801:Humans; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D016017:Odds Ratio; D011379:Prognosis; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D016540:Smoking Cessation / Q000706:statistics & numerical data; D019851:Thrombophilia / Q000453:epidemiology; D061485:Tobacco Use Cessation Devices; D020246:Venous Thrombosis / Q000175:diagnosis / Q000453:epidemiology
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