E-Mail karger@karger.com Neuroepidemiology 2014;42:59–67 DOI: 10.1159/000355433 Advances in Meta-Analysis: Examples from Internal Medicine to Neurology Massimiliano Copetti a Andrea Fontana a Giusi Graziano b, d Federica Veneziani e Federica Siena f Marco Scardapane c Giuseppe Lucisano c Fabio Pellegrini a, c a Unit of Biostatistics, Casa Sollievo della Sofferenza, San Giovanni Rotondo, b Laboratory of Lipid Metabolism and Cancer, DTP, and c Unit of Biostatistics, DCPE, Consorzio Mario Negri Sud, Santa Maria Imbaro, d National Cancer Research Center, IRCCS Oncologico Giovanni Paolo II, e Neurodegenerative Disease Unit, Department of Neurosciences and Sense Organs, University of Bari, Bari, and f Department of Clinical and Research Neurology, Azienda Ospedaliera Pia Fondazione Panico, Tricase, Italy Background Quite a long time has passed since the methodological debate on uses and misuses of meta-analysis was domi- nating the scene of research [1–4]. Either as a statistical tool for combining evidence or as a methodological framework, meta-analysis is now widely accepted. This development, which occurred over the last decades, has been impressive [5] (fig. 1) and primarily driven by the needs of specific fields of research. We provide here a thorough review of advances in the methodology of meta- analysis, particularly stressing the clinical and research needs which somehow determined them. Biostatistics, in its essence as a ‘discourse on the meth- od’, is always challenged by specific problems [6], and its development often mirrors the necessity to solve practical issues in science. It is broadly accepted that the first at- tempt of meta-analysis goes back to 1904 when Karl Pear- son was concerned with the pooling of existing evidence in terms of a set of correlation coefficients obtained from independent experiments [7]. Meta-analysis is a statistical technique to combine ev- idence of different findings obtained by similar experi- ments conducted on the same topic. When referring to a finding, we usually intend a treatment or an intervention effect on a given outcome. Logically this can be general- ized to any form of exposure: risk factors, alleles, geno- Key Words Meta-analysis · Data pooling · Advanced techniques · Generalized linear mixed models · Exact methods · Bayesian approaches Abstract Objective: We review the state of the art in meta-analysis and data pooling following the evolution of the statistical models employed. Methods: 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 dis- cussed as a further possibility of generalization for sensitivity analysis and the use of priors as a data augmentation ap- proach. Results: 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. Conclusions: 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 com- plex models can be easily implemented with the standard commercial software available. © 2013 S. Karger AG, Basel Published online: December 12, 2013 Fabio Pellegrini Consorzio Mario Negri Sud Via Nazionale 8/A IT–66030 Santa Maria Imbaro, Chieti (Italy) E-Mail pellegrini  @  negrisud.it © 2013 S. Karger AG, Basel 0251–5350/14/0421–0059$39.50/0 www.karger.com/ned