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 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.

Automated summary

Analysts often overlook interaction terms in meta-regression models, which may result in biased conclusions. We demonstrate this through a reanalysis of a meta-regression study on acute heart failure. With a total of 285 studies, we examine the 1-year mortality rate and its association with study-level factors such as recruitment year and average age. Our findings highlight the importance of including possible confounders and interaction terms in mixed-effects meta-regression models to avoid erroneous inference.