Statistical Modelling 14 (1) (2014), 49–76
An over-dispersion model in meta-analysis
Elena Kulinskaya
School of Computing Sciences,
University of East Anglia,
Norwich NR4 7TJ,
e-mail: e.kulinskaya@uea.ac.uk
United Kingdom
Ingram Olkin
Department of Statistics,
Stanford University,
Stanford,
CA 94305-4065,
USA
Abstract:
We propose a general approach based on the concept of over-dispersion for specification of a random effects model (REM) in meta-analysis. This approach is similar to that used in generalized linear models, and includes the traditional REM as a particular case. A key feature of the model is the interpretation of the multiplicative factor as an intra-class correlation parameter. We provide several motivating examples, discuss statistical inference, and compare the new and standard methods on two examples of published meta-analyses. Estimation of the over-dispersion parameter in the proposed model is compared in simulations to that of the traditional between-studies variance in the case of normal means. For small values of heterogeneity, the coverage of the confidence intervals for the over-dispersion parameter is more stable.
Keywords:
Intra-class correlation; between-study variability; random effects model; heterogeneity
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