Random-effects meta-analysis typically assumes normally distributed study-specific effects, an assumption that may be unrealistic under certain conditions. This webinar explores models that relax this assumption and their ability to uncover underlying data structures, such as asymmetry and clustering, that may be obscured under the normal model. While summary estimates remain largely unaffected, these models are valuable exploratory tools in seemingly non-normal data.
The webinar is targeted at researchers and practitioners who are familiar with meta-analysis models, while remaining accessible to participants without a formal statistical background. The presentation emphasizes intuitive explanations and practical insights, making it suitable for a broad, interdisciplinary audience interested in evidence synthesis.
Presenter Bio
Dr. Kanella Panagiotopoulou specializes in evidence synthesis methodology, focusing on the development and application of advanced statistical models for meta-analysis. Kanella's research spans Frequentist and Bayesian frameworks, using parametric and semi-parametric approaches to explore heterogeneity across studies. Kanella also engages with large language models to further investigate heterogeneity in meta-analytic data, while promoting accessible implementation of flexible random-effects models in R.
