Meta-analysis with zero-event studies: a comparative study with application to COVID-19 data

Abstract Background Meta-analysis is a statistical method to synthesize evidence from number of independent studies, including those clinical studies with binary outcomes. In practice, when there are zero events in one or both groups, it may cause problems the subsequent analysis. Methods this paper, by considering relative risk as effect size, we conduct comparative study that consists four continuity correction methods and another state-of-the-art without correction, namely generalized linear mixed models (GLMMs). To further advance literature, also introduce new for estimating risk. Results From simulation performs well terms mean squared error few studies. contrast, model best large. addition, reanalyzing recent coronavirus disease 2019 (COVID-19) data, evident double-zero-event impact estimate size. Conclusions We recommend handle zero-event meta-analysis, instead use GLMM The be informative, so suggest not excluding them.