Methods for Estimating Between-Study Variance and Overall Effect in Meta-Analysis of Odds Ratios

Autor/inn/en	Bakbergenuly, Ilyas; Hoaglin, David C.; Kulinskaya, Elena
Titel	Methods for Estimating Between-Study Variance and Overall Effect in Meta-Analysis of Odds Ratios
Quelle	In: Research Synthesis Methods, 11 (2020) 3, S.426-442 (17 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Bakbergenuly, Ilyas) ORCID (Hoaglin, David C.) ORCID (Kulinskaya, Elena)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1759-2879
DOI	10.1002/jrsm.1404
Schlagwörter	Meta Analysis; Statistical Bias; Intervals; Sample Size; Generalization; Simulation; Risk; Correlation; Mathematical Models + Suchen Sie Ihr Suchwort? Meta-analysis; Metaanalyse; Simulation program; Simulationsprogramm; Risiko; Korrelation; Mathematical model; Mathematisches Modell
Abstract	In random-effects meta-analysis the between-study variance ([tau][superscript 2]) has a key role in assessing heterogeneity of study-level estimates and combining them to estimate an overall effect. For odds ratios the most common methods suffer from bias in estimating [tau][superscript 2] and the overall effect and produce confidence intervals with below-nominal coverage. An improved approximation to the moments of Cochran's "Q" statistic, suggested by Kulinskaya and Dollinger (KD), yields new point and interval estimators of [tau][superscript 2] and of the overall log-odds-ratio. Another, simpler approach (SSW) uses weights based only on study-level sample sizes to estimate the overall effect. In extensive simulations we compare our proposed estimators with established point and interval estimators for [tau][superscript 2] and point and interval estimators for the overall log-odds-ratio (including the Hartung-Knapp-Sidik-Jonkman interval). Additional simulations included three estimators based on generalized linear mixed models and the Mantel-Haenszel fixed-effect estimator. Results of our simulations show that no single point estimator of [tau][superscript 2] can be recommended exclusively, but Mandel-Paule and KD provide better choices for small and large numbers of studies, respectively. The KD estimator provides reliable coverage of [tau][superscript 2]. Inverse-variance-weighted estimators of the overall effect are substantially biased, as are the Mantel-Haenszel odds ratio and the estimators from the generalized linear mixed models. The SSW estimator of the overall effect and a related confidence interval provide reliable point and interval estimation of the overall log-odds-ratio. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01