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The one-stage method further facilitates the adjustment for other factors, which is particularly important in non-randomised settings. Finally, stratification in one-stage models avoids the need for estimating correlations between random effects. One can simply estimate study-specific intercepts and slopes and place the random effect only on the factor of interest. These methods are usually preferred because sharing of IPD is often unfeasible due to, for instance, confidentiality agreements.

Results from our empirical example demonstrate that the full two-stage model, which when pooling the AD accounts for heterogeneity of baseline risk and risk factors, and their within-study and between-study correlation, tends to yield most consistent results with the one-stage models.