A Comparison of Methods for Meta-Analysis of a Small Number of Studies with Binary Outcomes

Autor/inn/en	Mathes, Tim; Kuss, Oliver
Titel	A Comparison of Methods for Meta-Analysis of a Small Number of Studies with Binary Outcomes
Quelle	In: Research Synthesis Methods, 9 (2018) 3, S.366-381 (16 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Mathes, Tim)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1759-2879
DOI	10.1002/jrsm.1296
Schlagwörter	Comparative Analysis; Meta Analysis; Probability; Statistical Analysis; Simulation; Medical Research; Outcomes of Treatment + Suchen Sie Ihr Suchwort? Meta-analysis; Metaanalyse; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Statistische Analyse; Simulation program; Simulationsprogramm
Abstract	Meta-analyses often include only a small number of studies ([less than or equal to]5). Estimating between-study heterogeneity is difficult in this situation. An inaccurate estimation of heterogeneity can result in biased effect estimates and too narrow confidence intervals. The beta-binominal model has shown good statistical properties for meta-analysis of sparse data. We compare the beta-binominal model with different inverse variance random (eg, DerSimonian-Laird, modified Hartung-Knapp, and Paule-Mandel) and fixed effects methods (Mantel-Haenszel and Peto) in a simulation study. The underlying true parameters were obtained from empirical data of actually performed meta-analyses to best mirror real-life situations. We show that valid methods for meta-analysis of a small number of studies are available. In fixed effects situations, the Mantel-Haenszel and Peto methods performed best. In random effects situations, the beta-binominal model performed best for meta-analysis of few studies considering the balance between coverage probability and power. We recommended the beta-binominal model for practical application. If very strong evidence is needed, using the Paule-Mandel heterogeneity variance estimator combined with modified Hartung-Knapp confidence intervals might be useful to confirm the results. Notable most inverse variance random effects models showed unsatisfactory statistical properties also if more studies (10-50) were included in the meta-analysis. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01