Individual participant data meta-analyses should not ignore clustering

Ghada Abo-Zaid,Boliang Guo,Jonathan J Deeks,Thomas P.A Debray,Ewout W Steyerberg,Karel G.M Moons,Richard David Riley

doi:10.1016/j.jclinepi.2012.12.017

Abstract

ObjectivesIndividual 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 SettingComparison of effect estimates from logistic regression models in real and simulated examples. ResultsThe 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. ConclusionResearchers must routinely account for clustering in IPD meta-analyses; otherwise, misleading effect estimates and conclusions may arise.

Highlights

Individual participant data (IPD) meta-analysis refers to when participant-level data are obtained from multiple studies and synthesized [1]
Key findings When meta-analyzing individual participant data (IPD) from multiple studies, our findings show that statistical and clinical conclusions can change depending on whether the analysis accounts for the clustering of patients within studies
When there is large variability in baseline risk, logistic regression simulations show that this naive approach leads to a downward bias in effect estimates, with small standard errors that produce a low coverage substantially less than 95%; this problem becomes worse as the true effect size increases

Summary

Introduction

Individual participant data (IPD) meta-analysis refers to when participant-level data are obtained from multiple studies and synthesized [1]. This contrasts the usual meta-analysis approach, which obtains and synthesizes aggregate data (such as a treatment effect estimates) extracted from study publication or study authors [2].

Objectives

Methods

Findings

Conclusion