Moment-based Random-effects Meta-analysis Equipped with Huber's M-Estimation
结合 Huber M 估计的矩方法随机效应元分析AI 解读 ↓
← 返回论文列表与筛选
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。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
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：摘要研究一般随机效应元分析与稳健矩估计，治疗效应汇总本身不足以确立因果识别分类。
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
结合 Huber M 估计的矩方法随机效应元分析
- 研究问题
- 当纳入研究数量较少时，如何改善矩方法对研究间方差及总体治疗效应不确定性的估计，避免置信区间过窄？
- 方法与思路
- 作者将 Huber M 估计融入矩方法随机效应元分析，并推导总体治疗效应与研究间方差的联合分布用于可视化。
- 材料陈述的贡献
- 摘要称方法在少量研究下提供更保守的估计，并在模拟和真实数据分析中改善置信区间准确性；具体结果未列出。
- 阅读关注点
- 应核对 Huber 调节参数、联合分布的推导条件、保守性的定义，以及置信区间覆盖率与区间长度之间的权衡。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要的“始终更保守”陈述未给出范围，不能外推为所有情形均更优。
原文摘要
Meta-analyses are commonly used to provide solid evidence across numerous studies. Traditional moment methods, such as the DerSimonian-Laird method, remain popular in spite of the availability of more accurate alternatives. While moment estimators are simple and intuitive, they are known to underestimate the variance of the overall treatment effect, particularly when the number of studies is small. This underestimation can lead to excessively narrow confidence intervals that do not meet the nominal confidence level, potentially resulting in misleading conclusions. In this study, we improve traditional moment-based meta-analysis methods by incorporating Huber's M-estimation to more accurately capture the distributional characteristics of between-study variance. Our approach enables conservative parameter estimation, even when almost all existing methods lead to underestimation of between-study variance under a small number of studies. Additionally, by deriving the simultaneous distribution of overall treatment effect and between-study variance, we propose facilitating a visual exploration of the relationship between these two quantities. Our method provides more reliable estimators for the overall treatment effect and between-study variance, particularly in situations with few studies. Using simulations and real data analysis, we demonstrate that our approach always yields more conservative results compared to traditional moment methods, and ensures more accurate confidence intervals in meta-analyses.