Advances in meta-analysis: examples from internal medicine to neurology

We review the state of the art in meta-analysis and data pooling following the evolution of the statistical models employed.

Starting from a classic definition of meta-analysis of published data, a set of apparent antinomies which characterized the development of the meta-analytic tools are reconciled in dichotomies where the second term represents a possible generalization of the first one. Particular attention is given to the generalized linear mixed models as an overall framework for meta-analysis. Bayesian meta-analysis is discussed as a further possibility of generalization for sensitivity analysis and the use of priors as a data augmentation approach.

We provide relevant examples to underline how the need for adequate methods to solve practical issues in specific areas of research have guided the development of advanced methods in meta-analysis.

We show how all the advances in meta-analysis naturally merge into the unified framework of generalized linear mixed models and reconcile apparently conflicting approaches. All these complex models can be easily implemented with the standard commercial software available.