ABSTRACT
El metaanálisis es una metodología para la revisión sistemática y cuantitativa de la investigación, ampliamente consolidada y aplicada en las Ciencias de la Salud. Ofrece las técnicas necesarias para acumular rigurosa y eficientemente los resultados cuantitativos de los estudios empíricos sobre un mismo problema de salud, permitiendo a los profesionales de la salud la adopción de decisiones bien informadas en sus respectivas áreas de trabajo. Con la ayuda de algunos ejemplos tomados del ámbito de la Fisioterapia, en este artículo describimos las principales características del metaanálisis, sus fases de ejecución y su utilidad en la investigación sanitaria. Finalmente, se muestran la relevancia del metaanálisis dentro del enfoque de la medicina basada en la evidencia y algunas perspectivas de futuro del metaanálisis(AU)
Meta-analysis is a methodology for the systematic and quantitative review of research that is widely consolidated and applied in Health Sciences. It offers the necessary techniques to rigorously and efficiently accumulate quantitative results on the empirical studies regarding a common health problem. It allows health care professionals to make well-informed decisions in their respective areas. In this article and with the help of some examples obtained from within the physiotherapy setting, we have described the main characteristics of meta-analysis, its execution phases and its usefulness in health care research. The relevance of meta-analysis in the evidence based medicine approach and some perspectives on the future of meta-analysis are shown(AU)
Subject(s)
Meta-Analysis as Topic , Biomedical Research/methods , Physical Therapy Modalities/trends , Data Collection/methods , Evidence-Based Medicine , KnowledgeABSTRACT
When a primary study includes several indicators of the same construct, the usual strategy to meta-analytically integrate the multiple effect sizes is to average them within the study. In this paper, the numerical and conceptual differences among three procedures for averaging dependent effect sizes are shown. The procedures are the simple arithmetic mean, the Hedges and Olkin (1985) procedure, and the Rosenthal and Rubin (1986) procedure. Whereas the simple arithmetic mean ignores the dependence among effect sizes, both the procedures by Hedges and Olkin and Rosenthal and Rubin take into account the correlational structure of the effect sizes, although in a different way. Rosenthal and Rubin's procedure provides the effect size for a single composite variable made up of the multiple effect sizes, whereas Hedges and Olkin's procedure presents an effect size estimate of the standard variable. The three procedures were applied to 54 conditions, where the magnitude and homogeneity of both effect sizes and correlation matrix among effect sizes were manipulated. Rosenthal and Rubin's procedure showed the highest estimates, followed by the simple mean, and the Hedges and Olkin procedure, this last having the lowest estimates. These differences are not trivial in a meta-analysis, where the aims must guide the selection of one of the procedures.
