The Dark Side of the Force: Multiplicity Issues in Network Meta-Analysis and How to Address Them

Autor/inn/en	Efthimiou, Orestis; White, Ian R.
Titel	The Dark Side of the Force: Multiplicity Issues in Network Meta-Analysis and How to Address Them
Quelle	In: Research Synthesis Methods, 11 (2020) 1, S.105-122 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Efthimiou, Orestis)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1759-2879
DOI	10.1002/jrsm.1377
Schlagwörter	Models; Meta Analysis; Network Analysis; Simulation; Bayesian Statistics; Comparative Analysis; Medical Research; Research Problems + Suchen Sie Ihr Suchwort? Analogiemodell; Meta-analysis; Metaanalyse; Netzplantechnik; Simulation program; Simulationsprogramm; Forschungskritik
Abstract	Standard models for network meta-analysis simultaneously estimate multiple relative treatment effects. In practice, after estimation, these multiple estimates usually pass through a formal or informal selection procedure, eg, when researchers draw conclusions about the effects of the best performing treatment in the network. In this paper, we present theoretical arguments as well as results from simulations to illustrate how such practices might lead to exaggerated and overconfident statements regarding relative treatment effects. We discuss how the issue can be addressed via multilevel Bayesian modelling, where treatment effects are modelled exchangeably, and hence estimates are shrunk away from large values. We present a set of alternative models for network meta-analysis, and we show in simulations that in several scenarios, such models perform better than the usual network meta-analysis model. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01