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 ...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.展开更多
垂直地震剖面(Vertical Seismic Profiling,VSP)资料处理中波场分离是关键问题之一.随着属性提取技术的发展,新的属性参数(例如Q值)提取技术对波场分离的保真性要求越来越高.本文改进了传统奇异值分解(Singular Value Decomposition,SVD...垂直地震剖面(Vertical Seismic Profiling,VSP)资料处理中波场分离是关键问题之一.随着属性提取技术的发展,新的属性参数(例如Q值)提取技术对波场分离的保真性要求越来越高.本文改进了传统奇异值分解(Singular Value Decomposition,SVD)法,给出了一种对波场的动力学特征具有更好的保真性,可以作为Q值提取的预处理步骤的零偏VSP资料上下行波场分离方法.该方法通过两步奇异值分解变换实现:第一步,排齐下行波同相轴,利用SVD变换压制部分下行波能量;第二步,在剩余波场中排齐上行波同相轴,使用SVD变换提取上行波场.在该方法的实现过程中,压制部分下行波能量后的剩余波场中仍然存在较强的下行波干扰,使得上行波同相轴的排齐比较困难.本文给出了一种通过极大化多道数据线性相关程度(Maximize Coherence,MC)排齐同相轴的算法,在一定程度上解决了低信噪比下排齐同相轴的问题.将本文提出的方法用于合成数据和实际资料的处理,并与传统SVD法的处理结果进行对比,结果表明本文提出的波场分离方法具有良好的保真性,得到波场的质量明显优于传统SVD法.通过对本文方法和传统SVD法处理合成数据得到的下行波场提取Q值,然后进行对比可知,本文方法可以有效提高所提取Q值的准确性,适合作为Q值提取的预处理步骤.展开更多
基金supported by grants awarded to Tie-Jun Tong from the General Research Fund(HKBU12303918)the National Natural Science Foundation of China(1207010822)the Initiation Grants for Faculty Niche Research Areas(RC-IG-FNRA/17-18/13,RC-FNRAIG/20-21/SCI/03)of Hong Kong Baptist University。
文摘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.
