  Analysts seldom include interaction terms in meta-regression model, what can
introduce bias if an interaction is present. We illustrate this in the current
paper by re-analyzing an example from research on acute heart failure, where
neglecting an interaction might have led to erroneous inference and
conclusions. Moreover, we perform a brief simulation study based on this
example highlighting the effects caused by omitting or unnecessarily including
interaction terms. Based on our results, we recommend to always include
interaction terms in mixed-effects meta-regression models, when such
interactions are plausible.
