Power Enhancement in High-Dimensional Heterogeneous Mediation Analysis
高维异质中介分析的检验功效增强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。
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：摘要明确研究暴露通过中介影响结局的间接效应，与因果中介分析直接相关，但未说明因果识别条件。
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
高维异质中介分析的检验功效增强
- 研究问题
- 当高维中介变量彼此相关、各中介效应异质甚至方向相反时，如何避免基于总间接效应的检验因效应抵消而功效不足？
- 方法与思路
- 作者针对线性及广义线性结局模型设计新检验，分析既有总间接效应检验功效不足的原因，并结合理论与数值研究评估。
- 材料陈述的贡献
- 摘要称新检验能处理异质及反向中介效应，在不牺牲第一类错误控制的前提下提高功效，并提供实际应用与 POEM 包。
- 阅读关注点
- 应核对检验针对的原假设、相关中介和高维规模条件、误差控制的保证，以及经济增长与健康应用中的因果识别依据。
- 阅读边界
- OpenAlex 索引摘要未注明稿件版本，不能推断刊本变化；摘要未说明因果识别假设、具体功效增幅或软件可用状态。
原文摘要
Mediation models are widely used in many scientific areas to understand how an exposure variable impacts an outcome variable through an intermediate variable. In this article, we develop powerful inferential methods to analyze mediation effects in high-dimensional contexts with intercorrelated potential mediators in models with linear and generalized linear outcomes. We draw attention to heterogeneous mediation, and show that existing testing approaches based on the total indirect effect are often underpowered when the mediation effects are not homogeneous. To overcome this limitation, we investigate the root cause and propose new solutions. The proposed tests are equipped with expanded capability to handle heterogeneous and even contrasting mediation effects, and have substantial increases in statistical power compared to existing methods, importantly, without compromising the Type I error rate. We demonstrate robust test performance under different mediation patterns by providing rigorous theoretical justifications and comprehensive numerical studies. The practicality is further demonstrated using a real-data application investigating the mediating role of healthcare expenditures in the relationship between economic growth and public health outcomes. An R package POEM is developed to facilitate practical implementation.