The spread of meta-analysis started with the need of collecting evidence from several small underpowered RCTs, the results of which needed to be combined to reach conclusive evidence on a given topic in view of the complexities of designing and conducting adequately powered RCTs. Meta-regression, in particular, corresponds to the classical regression analysis though at study-level, through which we try to explain the between-study variability (say, the heterogeneity of treatment effect) according to study characteristics. Thereafter, focus was directed to a different concern: to explain heterogeneity by subgroup analysis and meta-regression, which simply consisted in exploring the sources of heterogeneity itself. At the beginning, the purpose of resorting to random-effects models mainly consisted in relaxing the assumption that all the combined evidence stemmed from the same underlying population. The introduction of linear mixed models along with a seminal paper by DerSimonian and Laird provided the theoretical background to this approach.
This model yields an estimate of heterogeneity (namely, between-study variance) which is included in the process of weighting.
#Meta definition medical plus
In the presence of heterogeneity, evidence can be combined using the inverse of within-study plus between-study variance as weights, i.e. Cochran's Q test provides a formal test for heterogeneity assessment, although criticism about its low power to detect presence of heterogeneity makes a straightforward and intuitive measure (I 2) of the degree of heterogeneity preferable.
#Meta definition medical software
All these complex models can be easily implemented with the standard commercial software available.Ĭan we always combine our available evidence? The answer to this concern has so far represented the very first dichotomy, which was based on the presence or absence of heterogeneity of effects, that is, whether the evidence to be combined belonged or not to the same underlying population. 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. 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. Bayesian meta-analysis is discussed as a further possibility of generalization for sensitivity analysis and the use of priors as a data augmentation approach.
Particular attention is given to the generalized linear mixed models as an overall framework for meta-analysis. 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. Objective: We review the state of the art in meta-analysis and data pooling following the evolution of the statistical models employed.
0 kommentar(er)
