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阅读说明

本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。

DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。

引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。

关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。

分类依据与纠错

AI 根据所提供的论文材料归类:摘要讨论离散数据荟萃分析,医学稀有事件不构成金融尾部风险,也未提供所列经济计量方向的直接依据。

反馈分类问题

正在核对关联版本…

代码、数据与补充材料

正在核对对应版本的资源…

关注这篇论文的作者
Dungang Liu
Regina Y. Liu
Min-ge Xie

离散数据的精确荟萃分析及其稀有事件列联表应用

研究问题
在稀有事件的多个二乘二列联表中,如何合并研究证据,避免大样本近似和人为零值修正造成问题,并纳入双组均无事件的研究?
方法与思路
所给摘要片段描述了合并单项研究精确检验的框架,允许选择不同检验及目标参数,并分析各研究对整体效率、功效与错误率的影响。
材料陈述的贡献
摘要称该方法无需人为修正即可纳入零总事件研究,并提供改善合并推断的经验方法;数值研究报告了相对常用方法的表现优势。
阅读关注点
应先查阅完整摘要和正文补全合并方法,再核对精确性含义、零事件研究的贡献机制,以及比较中的目标参数和错误率控制。
阅读边界
依据稿件版本未注明的 OpenAlex 索引摘要,开头明显缺失,核心合并构造无法完整判断。优势仅为所述数值研究结论,不能推断刊本变化。

原文摘要

) associated with the exact tests from individual studies. It encompasses a broad class of exact meta-analysis methods, as it permits broad choices for the combining elements, such as tests used in individual studies, and any parameter of interest. The approach yields statements that explicitly account for the impact of individual studies on the overall inference, in terms of efficiency/power and the type I error rate. Those statements also give rises to empirical methods for further enhancing the combined inference. Although the proposed approach is for general discrete settings, for convenience, it is illustrated throughout using the setting of meta-analysis of multiple 2 × 2 tables. In the context of rare events data, such as observing few, zero or zero total (i.e., zero events in both arms) outcomes in binomial trials or 2 × 2 tables, most existing meta-analysis methods rely on the large-sample approximations which may yield invalid inference. The commonly used corrections to zero outcomes in rare events data, aiming to improve numerical performance can also incur undesirable consequences. The proposed approach applies readily to any rare event setting, including even the zero total event studies without any artificial correction. While debates continue on whether or how zero total event studies should be incorporated in meta-analysis, the proposed approach has the advantage of automatically including those studies and thus making use of all available data. Through numerical studies in rare events settings, the proposed exact approach is shown to be efficient and, generally, outperform commonly used meta-analysis methods, including Mental-Haenszel and Peto methods.

笔记与分组