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

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...

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Bibliographic Details
Published in:Research synthesis methods 2023-07, Vol.14 (4), p.647-651
Main Authors: Knop, Eric S., Pauly, Markus, Friede, Tim, Welz, Thilo
Format: Article
Language:English
Subjects:
acute heart failure
Age
Bias
confounding
Death
Heart Disorders
Heart failure
Humans
interactions
Meta Analysis
meta‐regression
Recruitment
Regression (Statistics)
Regression models
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Summary: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.
ISSN:1759-2879
1759-2887
DOI:10.1002/jrsm.1643