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

Military Medical Research 2022年第1期9卷 126-137页

作者： Jia-Jin Wei En-Xuan Lin Jian-Dong Shi Ke Yang Zong-Liang Hu Xian-Tao Zeng Tie-Jun Tong department of Mathematics Hong Kong Baptist UniversityHong KongChina Shenzhen Research Institute of Big Data ShenzhenChina College of Mathematics and Statistics Shenzhen UniversityShenzhenChina Center for Evidence-Based and Translational Medicine Zhongnan Hospital of Wuhan UniversityWuhanChina.

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.

关键词： Continuity correction Coronavirus disease 2019 data Meta-analysis Relative risk Zero-event studies

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Minimum MSE Weighted Estimator to Make Inferences for a Common Risk Ratio across Sparse Meta-Analysis Data

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Open Journal of Statistics 2022年第1期12卷 49-69页

作者： Chukiat Viwatwongkasem Sutthisak Srisawad Pichitpong Soontornpipit Jutatip Sillabutra Pratana Satitvipawee Prasong Kitidamrongsuk Hathaikan Chootrakool Department of Biostatistics Faculty of Public Health Mahidol University Bangkok Thailand Nursing Research Center Faculty of Nursing Mahidol University Nakhon Pathom Thailand

The paper aims to discuss three interesting issues of statistical inferences for a common risk ratio (RR) in sparse meta-analysis data. Firstly, the conventional log-risk ratio estimator encounters a number of problems when the number of events in the experimental or control group is zero in sparse data of a 2 × 2 table. The adjusted log-risk ratio estimator with the Continuity correction points based upon the minimum Bayes risk with respect to the uniform prior density over (0, 1) and the Euclidean loss function is proposed. Secondly, the interest is to find the optimal weights of the pooled estimate that minimize the mean square error (MSE) of subject to the constraint on where , , . Finally, the performance of this minimum MSE weighted estimator adjusted with various values of points is investigated to compare with other popular estimators, such as the Mantel-Haenszel (MH) estimator and the weighted least squares (WLS) estimator (also equivalently known as the inverse-variance weighted estimator) in senses of point estimation and hypothesis testing via simulation studies. The results of estimation illustrate that regardless of the true values of RR, the MH estimator achieves the best performance with the smallest MSE when the study size is rather large and the sample sizes within each study are small. The MSE of WLS estimator and the proposed-weight estimator adjusted by , or , or are close together and they are the best when the sample sizes are moderate to large (and) while the study size is rather small.

关键词： Minimum MSE Weights Adjusted Log-Risk Ratio Estimator Sparse Meta-Analysis Data Continuity correction

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