目的:基于文献数据挖掘和Meta分析探究中药复方治疗幽门螺杆菌(Hp)相关性慢性萎缩性胃炎(CAG)的用药规律和疗效,为临床应用提供理论依据。方法:系统检索中国知网、万方数据库、中国生物医学文献数据库、Pubmed、Web of Science数据库收...目的:基于文献数据挖掘和Meta分析探究中药复方治疗幽门螺杆菌(Hp)相关性慢性萎缩性胃炎(CAG)的用药规律和疗效,为临床应用提供理论依据。方法:系统检索中国知网、万方数据库、中国生物医学文献数据库、Pubmed、Web of Science数据库收录的中药复方治疗Hp相关性CAG的文献,检索时间范围为各数据库自建库以来至2022年9月。首先进行数据频次统计和挖掘分析;其次进行Meta分析,采用RevMan 5.3软件对结局指标(临床疗效、Hp清除率、胃镜检查结果改善有效率、临床症状积分)进行分析。并采用GRADE证据质量分级系统对结局指标的证据质量进行评价。结果:数据挖掘部分纳入19篇文献,共涉及19个完整处方、76味中药,其中高频药物有甘草、黄芪、蒲公英、黄连、茯苓、半夏、莪术等,主要功效为益气健脾、清热祛湿、理气健胃等;药性多温、寒,药味多苦、辛,主要归脾、胃经。Meta分析纳入17篇文献,结果显示,与西药或中成药或西药联合中成药组相比,中药复方组的临床总有效率更高(P<0.05)。中药复方组在清除Hp、提高胃镜检查结果改善有效率方面优于西药或西药联合中成药组(P<0.05)。中药复方组在改善胃胀满、胃痛、喜热喜按、腹泻方面优于西药或中成药或西药联合中成药组(P<0.05),在改善口干口苦和纳差食少方面,中药复方组与西药或西药联合中成药组比较无统计学差异(P>0.05)。GRADE评价显示证据级别为中等到极低,提示结局指标的证据质量不高。结论:Hp相关性CAG的用药以益气健脾药为主,常与具有清热除湿、疏肝和胃等功效的药物配伍使用。中药复方治疗Hp相关性CAG疗效、改善临床症状方面优于西药或中成药或两者联合治疗。但相关研究获得证据级别低,尚需开展高质量的多中心、随机对照临床试验进一步验证。展开更多
Individual participant data (IPD) meta-analysis was developed to overcome several meta-analytical pitfalls of classical meta-analysis. One advantage of classical psychometric meta-analysis over IPD meta-analysis is th...Individual participant data (IPD) meta-analysis was developed to overcome several meta-analytical pitfalls of classical meta-analysis. One advantage of classical psychometric meta-analysis over IPD meta-analysis is the corrections of the aggregated unit of studies, namely study differences, i.e., artifacts, such as measurement error. Without these corrections on a study level, meta-analysts may assume moderator variables instead of artifacts between studies. The psychometric correction of the aggregation unit of individuals in IPD meta-analysis has been neglected by IPD meta-analysts thus far. In this paper, we present the adaptation of a psychometric approach for IPD meta-analysis to account for the differences in the aggregation unit of individuals to overcome differences between individuals. We introduce the reader to this approach using the aggregation of lens model studies on individual data as an example, and lay out different application possibilities for the future (e.g., big data analysis). Our suggested psychometric IPD meta-analysis supplements the meta-analysis approaches within the field and is a suitable alternative for future analysis.展开更多
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 problem...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.展开更多
异质图是由不同类型节点及边构成的图,可建模现实世界中各种类型对象及其关系。异质图嵌入旨在捕捉图中丰富的属性、结构和语义等信息,学习节点嵌入向量,用于节点分类、链接预测等任务,进而实现用户识别、商品推荐等应用。在异质图嵌入...异质图是由不同类型节点及边构成的图,可建模现实世界中各种类型对象及其关系。异质图嵌入旨在捕捉图中丰富的属性、结构和语义等信息,学习节点嵌入向量,用于节点分类、链接预测等任务,进而实现用户识别、商品推荐等应用。在异质图嵌入方法中,元路径通常被用来获取节点间的高阶结构和语义信息,然而现有方法忽略了元路径实例中不同类型节点或异质图中不同类型邻居节点的差异,导致信息丢失,进而影响节点嵌入质量。针对上述问题,提出基于数据增强的异质图注意力网络(Heterogeneous graph Attention Network based on Data Augmentation,HANDA),以更好地学习节点嵌入向量。首先,提出基于元路径邻居的边增强。该方法基于元路径获取节点的元路径邻居,用节点及其元路径邻居形成的语义边增强异质图。这些增强边不仅蕴含了节点间的高阶结构和语义,还缓解了异质图的稀疏性。其次,提出融入节点类型注意力的节点嵌入。该方法采用多头注意力从多个角度学习不同直接边邻居及增强边邻居的重要性并在注意力中融入节点的类型信息,进而通过消息传递、直接边邻居及增强边邻居同时获取节点的属性、高阶结构和语义信息,提升了节点嵌入质量。在真实数据集上的实验验证了HANDA模型在节点分类、链接预测任务上的效果优于基准模型。展开更多
文摘目的:基于文献数据挖掘和Meta分析探究中药复方治疗幽门螺杆菌(Hp)相关性慢性萎缩性胃炎(CAG)的用药规律和疗效,为临床应用提供理论依据。方法:系统检索中国知网、万方数据库、中国生物医学文献数据库、Pubmed、Web of Science数据库收录的中药复方治疗Hp相关性CAG的文献,检索时间范围为各数据库自建库以来至2022年9月。首先进行数据频次统计和挖掘分析;其次进行Meta分析,采用RevMan 5.3软件对结局指标(临床疗效、Hp清除率、胃镜检查结果改善有效率、临床症状积分)进行分析。并采用GRADE证据质量分级系统对结局指标的证据质量进行评价。结果:数据挖掘部分纳入19篇文献,共涉及19个完整处方、76味中药,其中高频药物有甘草、黄芪、蒲公英、黄连、茯苓、半夏、莪术等,主要功效为益气健脾、清热祛湿、理气健胃等;药性多温、寒,药味多苦、辛,主要归脾、胃经。Meta分析纳入17篇文献,结果显示,与西药或中成药或西药联合中成药组相比,中药复方组的临床总有效率更高(P<0.05)。中药复方组在清除Hp、提高胃镜检查结果改善有效率方面优于西药或西药联合中成药组(P<0.05)。中药复方组在改善胃胀满、胃痛、喜热喜按、腹泻方面优于西药或中成药或西药联合中成药组(P<0.05),在改善口干口苦和纳差食少方面,中药复方组与西药或西药联合中成药组比较无统计学差异(P>0.05)。GRADE评价显示证据级别为中等到极低,提示结局指标的证据质量不高。结论:Hp相关性CAG的用药以益气健脾药为主,常与具有清热除湿、疏肝和胃等功效的药物配伍使用。中药复方治疗Hp相关性CAG疗效、改善临床症状方面优于西药或中成药或两者联合治疗。但相关研究获得证据级别低,尚需开展高质量的多中心、随机对照临床试验进一步验证。
文摘Individual participant data (IPD) meta-analysis was developed to overcome several meta-analytical pitfalls of classical meta-analysis. One advantage of classical psychometric meta-analysis over IPD meta-analysis is the corrections of the aggregated unit of studies, namely study differences, i.e., artifacts, such as measurement error. Without these corrections on a study level, meta-analysts may assume moderator variables instead of artifacts between studies. The psychometric correction of the aggregation unit of individuals in IPD meta-analysis has been neglected by IPD meta-analysts thus far. In this paper, we present the adaptation of a psychometric approach for IPD meta-analysis to account for the differences in the aggregation unit of individuals to overcome differences between individuals. We introduce the reader to this approach using the aggregation of lens model studies on individual data as an example, and lay out different application possibilities for the future (e.g., big data analysis). Our suggested psychometric IPD meta-analysis supplements the meta-analysis approaches within the field and is a suitable alternative for future analysis.
文摘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.
文摘异质图是由不同类型节点及边构成的图,可建模现实世界中各种类型对象及其关系。异质图嵌入旨在捕捉图中丰富的属性、结构和语义等信息,学习节点嵌入向量,用于节点分类、链接预测等任务,进而实现用户识别、商品推荐等应用。在异质图嵌入方法中,元路径通常被用来获取节点间的高阶结构和语义信息,然而现有方法忽略了元路径实例中不同类型节点或异质图中不同类型邻居节点的差异,导致信息丢失,进而影响节点嵌入质量。针对上述问题,提出基于数据增强的异质图注意力网络(Heterogeneous graph Attention Network based on Data Augmentation,HANDA),以更好地学习节点嵌入向量。首先,提出基于元路径邻居的边增强。该方法基于元路径获取节点的元路径邻居,用节点及其元路径邻居形成的语义边增强异质图。这些增强边不仅蕴含了节点间的高阶结构和语义,还缓解了异质图的稀疏性。其次,提出融入节点类型注意力的节点嵌入。该方法采用多头注意力从多个角度学习不同直接边邻居及增强边邻居的重要性并在注意力中融入节点的类型信息,进而通过消息传递、直接边邻居及增强边邻居同时获取节点的属性、高阶结构和语义信息,提升了节点嵌入质量。在真实数据集上的实验验证了HANDA模型在节点分类、链接预测任务上的效果优于基准模型。
