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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.

Titel:	The consequences of neglected confounding and interactions in mixed-effects meta-regression: An illustrative example.
Autor/in / Beteiligte Person:	Knop, ES ; Pauly, M ; Friede, T ; Welz, T
Link:	Full Text Finder (Volltext)
Zeitschrift:	Research synthesis methods, Jg. 14 (2023-07-01), Heft 4, S. 647-651
Veröffentlichung:	<Mar. 2014-> : Chichester : Wiley Blackwell ; <i>Original Publication</i>: Malden, MA : John Wiley & Sons, 2010-, 2023
Medientyp:	academicJournal
ISSN:	1759-2887 (electronic)
DOI:	10.1002/jrsm.1643
Schlagwort:	Humans Heart Failure Bias Regression Analysis
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [Res Synth Methods] 2023 Jul; Vol. 14 (4), pp. 647-651. <i>Date of Electronic Publication: </i>2023 Jun 04. MeSH Terms: Bias* ; Regression Analysis* ; Humans ; Heart Failure References: Davey J, Turner RM, Clarke MJ, Higgins J. Characteristics of meta-analyses and their component studies in the cochrane database of systematic reviews: a cross-sectional, descriptive analysis. BMC Med Res Methodol. 2011;11(1):1-11. doi:10.1186/1471-2288-11-160. ; Andreas S, Röver C, Heinz J, Straube S, Watz H, Friede T. Decline of COPD exacerbations in clinical trials over two decades-a systematic review and meta-regression. Respir Res. 2019;20(1):1-11. doi:10.1186/s12931-019-1163-2. ; Kimmoun A, Takagi K, Gall E, et al. Temporal trends in mortality and readmission after acute heart failure: a systematic review and meta-regression in the past four decades. Eur J Heart Fail. 2021;23:420-431. doi:10.1002/ejhf.2103. ; Greene WH. Econometric Analysis, Ebook. global ed. Pearson Deutschland; 2019 https://elibrary.pearson.de/book/99.150005/9781292231150. ; Greenland S, Robins JM. Identifiability, exchangeability, and epidemiological confounding. Int J Epidemiol. 1986;15(3):413-419. ; Friedrich S, Antes G, Behr S, et al. Is there a role for statistics in artificial intelligence? Advances in Data Analysis and Classification. 2022;16(4):823-846. ; Durlak JA, Lipsey MW. A practitioner's guide to meta-analysis. Am J Community Psychol. 1991;19:291-332. ; Raudenbush S. Analyzing effect sizes: random-effects models. In: Cooper H, Hedges LV, Valentine JC, eds. The Handbook of Research Synthesis and Meta-Analysis. Russell Sage Foundation; 2009:295-315. ; Berkey C, Hoaglin D, Mosteller F, Colditz G. A random-effects regression model for meta-analysis. Stat Med. 1995;14:395-411. doi:10.1002/sim.4780140406. ; Fahrmeir L, Kneib T, Lang S, Marx B. Regression: Models. Springer-Verlag, Berlin; 2013. doi:10.1007/978-3-642-34333-9. ; Veroniki AA, Jackson D, Viechtbauer W, et al. Methods to estimate the between-study variance and its uncertainty in meta-analysis. Res Synth Methods. 2016;7(1):55-79. doi:10.1002/jrsm.1164. ; Knapp G, Hartung J. Improved tests for a random effects meta-regression with a single covariate. Stat Med. 2003;22(17):2693-2710. doi:10.1002/sim.1482. ; Viechtbauer W, López-López J, Sanchez-Meca J, Marín-Martínez F. A comparison of procedures to test for moderators in mixed-effects meta-regression models. Psychol Methods. 2015;20:360-374. doi:10.1037/met0000023. ; Welz T, Pauly M. A simulation study to compare robust tests for linear mixed-effects meta-regression. Res Synth Methods. 2020;11(3):331-342. doi:10.1002/jrsm.1388. ; R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing; 2021 https://www.R-project.org/. ; Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Softw. 2010;36(3):1-48. https://www.jstatsoft.org/v36/i03/. ; Aiken L, West S, Reno R. Multiple Regression: Testing and Interpreting Interactions. SAGE Publications; 1991. ; Tipton E, Pustejovsky JE, Ahmadi H. Current practices in meta-regression in psychology, education, and medicine. Res Synth Methods. 2019;10(2):180-194. Grant Information: FR 3070/3-1 Deutsche Forschungsgemeinschaft; PA-2409 7-1 Deutsche Forschungsgemeinschaft Contributed Indexing: Keywords: acute heart failure; confounding; interactions; meta-regression Entry Date(s): Date Created: 20230604 Date Completed: 20230711 Latest Revision: 20230718 Update Code: 20240513

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The consequences of neglected confounding and interactions in mixed-effects meta-regression: An illustrative example.