The Consequences of Neglected Confounding and Interactions in Mixed-Effects Meta-Regression: An Illustrative Example

Autor/inn/en	Knop, Eric S.; Pauly, Markus; Friede, Tim; Welz, Thilo
Titel	The Consequences of Neglected Confounding and Interactions in Mixed-Effects Meta-Regression: An Illustrative Example
Quelle	In: Research Synthesis Methods, 14 (2023) 4, S.647-651 (5 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Knop, Eric S.) ORCID (Pauly, Markus) ORCID (Friede, Tim) ORCID (Welz, Thilo)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1759-2879
DOI	10.1002/jrsm.1643
Schlagwörter	Regression (Statistics); Meta Analysis; Death; Heart Disorders; Recruitment; Age; Bias + Suchen Sie Ihr Suchwort? Regression; Regressionsanalyse; Meta-analysis; Metaanalyse; Sterbefall; Tod; Todesfall; Herzkrankheit; Recruiting; Rekrutierung; Alter; Lebensalter
Abstract	Analysts seldom include interaction terms in their meta-regression model, which can introduce bias if an interaction is present. We illustrate this by reanalysing a meta-regression study in acute heart failure. Based on a total of 285 studies, the 1-year mortality rate related to acute heart failure is considered and the connection to the study-level covariates year of recruitment and average age of study participants are of interest. We show that neglecting a possibly confounding variable and an interaction term might lead to erroneous inference and conclusions. Based on our results and accompanying simulations, we recommend to include possible confounders and interaction terms, whenever they are plausible, in mixed-effects meta-regression models. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01