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10.1186/s40779-021-00331-6 http://scihub22266oqcxt.onion/10.1186/s40779-021-00331-6 34217371!8254431!34217371     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34217371&cmd=llinks): Failed to open stream: HTTP request failed! 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Meta-analysis with zero-event studies: a comparative study with application to COVID-19 data |pdf=|usr=}}{{34217371}} gab.com Text Meta-analysis with zero-event studies: a comparative study with application to COVID-19 data JO: Mil Med Res http://www.kidney.de/pget.php?&tw=1&pmid=34217371 #FOAvip BACKGROUND: Meta-analysis is a statistical method to synthesize evidence from a number of independent studies, including those from clinical studies with binary outcomes. In practice, when there are zero events in one or both groups, it may cause statistical problems in the subsequent analysis. METHODS: In this paper, by considering the relative risk as the effect size, we conduct a comparative study that consists of four continuity correction methods and another state-of-the-art method without the continuity correction, namely the generalized linear mixed models (GLMMs). To further advance the literature, we also introduce a new method of the continuity correction for estimating the relative risk. RESULTS: From the simulation studies, the new method performs well in terms of mean squared error when there are few studies. In contrast, the generalized linear mixed model performs the best when the number of studies is large. In addition, by reanalyzing recent coronavirus disease 2019 (COVID-19) data, it is evident that the double-zero-event studies impact the estimate of the mean effect size. CONCLUSIONS: We recommend the new method to handle the zero-event studies when there are few studies in a meta-analysis, or instead use the GLMM when the number of studies is large. The double-zero-event studies may be informative, and so we suggest not excluding them. Twit Text FOAVip Meta-analysis with zero-event studies: a comparative study with application to COVID-19 data ????Mil Med Res http://www.kidney.de/pget.php?&tw=1&pmid=34217371 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34217371 #FOAvip English Wikipedia <ref>{{Cite pmid|34217371}}</ref> Meta-analysis with zero-event studies: a comparative study with application to COVID-19 data #MMPMID34217371 Wei JJ; Lin EX; Shi JD; Yang K; Hu ZL; Zeng XT; Tong TJ Mil Med Res 2021[Jul]; 8 (1): 41 PMID34217371show ga BACKGROUND: Meta-analysis is a statistical method to synthesize evidence from a number of independent studies, including those from clinical studies with binary outcomes. In practice, when there are zero events in one or both groups, it may cause statistical problems in the subsequent analysis. METHODS: In this paper, by considering the relative risk as the effect size, we conduct a comparative study that consists of four continuity correction methods and another state-of-the-art method without the continuity correction, namely the generalized linear mixed models (GLMMs). To further advance the literature, we also introduce a new method of the continuity correction for estimating the relative risk. RESULTS: From the simulation studies, the new method performs well in terms of mean squared error when there are few studies. In contrast, the generalized linear mixed model performs the best when the number of studies is large. In addition, by reanalyzing recent coronavirus disease 2019 (COVID-19) data, it is evident that the double-zero-event studies impact the estimate of the mean effect size. CONCLUSIONS: We recommend the new method to handle the zero-event studies when there are few studies in a meta-analysis, or instead use the GLMM when the number of studies is large. The double-zero-event studies may be informative, and so we suggest not excluding them. |*COVID-19[MESH] |*Data Analysis[MESH] |*Meta-Analysis as Topic[MESH] |Humans[MESH] |Linear Models[MESH]Meta-analysis with zero-event studies: a comparative study with applic(epmc).pdf DeepDyve Pubget Overpricing