When meta-analyzing heterogeneous bodies of literature, meta-regression can be used to account for potentially relevant between-studies differences. A key challenge is that the number of candidate moderators is often high relative to the number of studies. This introduces risks of overfitting, spurious results, and model non-convergence. To…

Statistical inference is problematic in the common situation in meta-analysis where the random effects model is fitted to just a handful of studies. In particular, the asymptotic theory of maximum likelihood provides a poor approximation, and Bayesian methods are sensitive to the prior specification. Hence, less efficient, but easily computed and…

Existing methods for meta-analysis of diagnostic test accuracy focus primarily on a single index test. We propose models for the joint meta-analysis of studies comparing multiple index tests on the same participants in paired designs. These models respect the grouping of data by studies, account for the within-study correlation between the tests'…

Despite having drawn from empirical evidence and cumulative prior expertise in the formulation of research questions as well as study design, each study is treated as a stand-alone product rather than positioned within a sequence of cumulative evidence. While results of prior studies are typically cited within the body of prior literature review,…

To facilitate meta-analysis of diverse study findings, a mixed linear model with fixed random effects is presented and illustrated with data from teacher expectancy experiments. The standardized effect size is viewed as random and the variation among effect sizes is modeled as a function of study characteristics. (Author/BS).