Diagnostics for meta-analysis based on generalized linear mixed models

Abstract

Meta-analysis is the method to combine data coming from multiple studies, with the aim to provide an overall event-risk measure of interest summarizing information coming from the studies. In meta-analysis generalized linear mixed models (GLMM) are particularly used for a number of measures of interest since they allow the true effect size to differ from study to study while accepting binary, discrete as well as continuous response variable. In the present paper some strategies of influence diagnostics based on log-likelihood are suggested and discussed. These are considered for Individual Patient Data, Aggregate Data and their compounding.