A Finite Mixture Method for Outlier Detection and Robustness in Meta-Analysis

Autor/in	Beath, Ken J.
Titel	A Finite Mixture Method for Outlier Detection and Robustness in Meta-Analysis
Quelle	In: Research Synthesis Methods, 5 (2014) 4, S.285-293 (9 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1759-2879
DOI	10.1002/jrsm.1114
Schlagwörter	Mixed Methods Research; Robustness (Statistics); Meta Analysis; Prediction; Research Methodology; Sampling; Statistical Inference; Comparative Analysis; Probability; Weighted Scores + Suchen Sie Ihr Suchwort? Widerstandsfähigkeit; Meta-analysis; Metaanalyse; Vorhersage; Research method; Forschungsmethode; Inferential statistics; Schließende Statistik; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie
Abstract	When performing a meta-analysis unexplained variation above that predicted by within study variation is usually modeled by a random effect. However, in some cases, this is not sufficient to explain all the variation because of outlier or unusual studies. A previously described method is to define an outlier as a study requiring a higher random effects variance and testing each study sequentially. An extension is described where the studies are considered to be a finite mixture of outliers and non-outliers, allowing any number of outlier studies and the use of standard mixture model techniques. The bootstrap likelihood ratio test is used to determine if there are any outliers present by comparing models with and without outliers, and the outlier studies are identified using posterior predicted probabilities. The estimation of the overall treatment effect is then determined including all observations but with the outliers down-weighted. This has the advantage that studies that are marginal outliers are still included in the meta-analysis but with an appropriate weighting. The method is applied to examples from meta-analysis and meta-regression. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01