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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.

Potential limitations such as missing data in a subset of studies could be overcome using imputation methods that account for clustering. Furthermore, Bayesian approaches facilitate sensitivity analyses through adjusting prior specification, and permit the the robustness of fitted models to be evaluated.

This is particularly useful when few studies are available and estimated parameters of one- and two-stage models may be severely biased due to estimation difficulties.