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Although these models are fairly straightforward to implement, it is well known that factor-outcome associations are often influenced by extraneous variables rendering exposure groups incomparable. This situation may, for instance, arise when associations are estimated from cohort and cross-sectional studies (prognostic research) or treatment-by-patient-characteristic interactions occur (intervention research).
This raises the need for multivariable analyses, where the factor-outcome association under investigation is adjusted for potential confounders or other known predictors. Consequently, the methods from previous section performing a univariate (or bivariate) meta-analysis need to be extended to perform a (multivariate) meta-analysis where the factor-outcome associations (and intercept) are adjusted for additional factors.