Power of the adjusted Q statistic to evaluate heterogeneity in meta-analyses of cluster randomized trials. Issue 9 (21st October 2017)

• Record Type: Journal Article
• Authors: Lee, Shun Fu; Donner, Allan; Klar, Neil
• Is Part Of: Communications in statistics. Volume 46:Issue 9(2017)
• Page Start: 7062
• Page End: 7073
• Publication Date: 2017-10-21
• Subjects: Binary outcome -- Cluster randomized trial -- Heterogeneity -- Meta-analysis -- Power -- Q statistic; 62F03 -- 62H30 -- 62P10; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2016.1222426
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: Because of its simplicity, the Q statistic is frequently used to test the heterogeneity of the estimated intervention effect in meta-analyses of individually randomized trials. However, it is inappropriate to apply it directly to the meta-analyses of cluster randomized trials without taking clustering effects into account. We consider the properties of the adjusted Q statistic for testing heterogeneity in the meta-analyses of cluster randomized trials with binary outcomes. We also derive an analytic expression for the power of this statistic to detect heterogeneity in meta-analyses, which can be useful when planning a meta-analysis. A simulation study is used to assess the performance of the adjusted Q statistic, in terms of its Type I error rate and power. The simulation results are compared to that obtained from the proposed formula. It is found that the adjusted Q statistic has a Type I error rate close to the nominal level of 5%, as compared to the unadjusted Q statistic commonly used to test for heterogeneity in the meta-analyses of individually randomized trials with an inflated Type I error rate. Data from a meta-analysis of four cluster randomized trials are used to illustrate the procedures.