Estimation of Effect Heterogeneity in Rare Events Meta-Analysis

The paper outlines several approaches for dealing with meta-analyses of count outcome data. These counts are the accumulation of occurred events, and these events might be rare, so a special feature of the meta-analysis is dealing with low counts including zero-count studies. Emphasis is put on approaches which are state of the art for count data modelling including mixed log-linear (Poisson) and mixed logistic (binomial) regression as well as nonparametric mixture models for count data of Poisson and binomial type. A simulation study investigates the performance and capability of discrete mixture models in estimating effect heterogeneity. The approaches are exemplified on a meta-analytic case study investigating the acceptance of bibliotherapy.

Automated summary

The paper outlines various approaches for dealing with meta-analyses of count outcome data, with emphasis on advanced models for handling low and zero count studies, and investigates the performance and capability of discrete mixture models in estimating effect heterogeneity through a simulation study. The approaches are exemplified using a meta-analytic case study on the acceptance of bibliotherapy.