Robust Bayesian Meta-Analysis: Model-Averaging across Complementary Publication Bias Adjustment Methods

Autor/inn/en	Bartoš, František; Maier, Maximilian; Wagenmakers, Eric-Jan; Doucouliagos, Hristos; Stanley, T. D.
Titel	Robust Bayesian Meta-Analysis: Model-Averaging across Complementary Publication Bias Adjustment Methods
Quelle	In: Research Synthesis Methods, 14 (2023) 1, S.99-116 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Bartoš, František) ORCID (Maier, Maximilian) ORCID (Wagenmakers, Eric-Jan) ORCID (Doucouliagos, Hristos) ORCID (Stanley, T. D.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1759-2879
DOI	10.1002/jrsm.1594
Schlagwörter	Robustness (Statistics); Bayesian Statistics; Meta Analysis; Publications; Bias; Models + Suchen Sie Ihr Suchwort? Widerstandsfähigkeit; Meta-analysis; Metaanalyse; Analogiemodell
Abstract	Publication bias is a ubiquitous threat to the validity of meta-analysis and the accumulation of scientific evidence. In order to estimate and counteract the impact of publication bias, multiple methods have been developed; however, recent simulation studies have shown the methods' performance to depend on the true data generating process, and no method consistently outperforms the others across a wide range of conditions. Unfortunately, when different methods lead to contradicting conclusions, researchers can choose those methods that lead to a desired outcome. To avoid the condition-dependent, all-or-none choice between competing methods and conflicting results, we extend robust Bayesian meta-analysis and model-average across two prominent approaches of adjusting for publication bias: (1) selection models of "p"-values and (2) models adjusting for small-study effects. The resulting model ensemble weights the estimates and the evidence for the absence/presence of the effect from the competing approaches with the support they receive from the data. Applications, simulations, and comparisons to preregistered, multi-lab replications demonstrate the benefits of Bayesian model-averaging of complementary publication bias adjustment methods. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01