跳到正文
← 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:摘要明确研究观察数据中的药物使用因果效应、异质性处理效应及高风险亚组推断。

反馈分类问题

Assessing the Most Vulnerable Subgroup to Type II Diabetes Associated with Statin Usage: Evidence from Electronic Health Record Data

识别他汀使用相关二型糖尿病风险最高的亚组:电子健康记录证据

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
Xinzhou Guo
Waverly Wei
Molei Liu
Tianxi Cai
Chong Wu
Jingshen Wang

识别他汀使用相关二型糖尿病风险最高的亚组:电子健康记录证据

研究问题
如何利用观察性健康记录评估他汀使用对新发二型糖尿病风险的因果效应,并识别最易受影响且结果可重复的患者亚组?
方法与思路
作者结合生物样本库与电子健康记录建立因果分析流程,识别高风险亚组,并用 bootstrap 校准处理重复性与显著性评估。
材料陈述的贡献
摘要称,方法在高维协变量下提供去偏处理效应估计和渐近尖锐区间,应用发现高糖尿病遗传风险女性是最易受影响亚组。
阅读关注点
应核对观察数据的因果识别、亚组搜索与效应估计的衔接,以及 bootstrap 校准如何处理选择偏差和结果重复性。
阅读边界
仅据 OpenAlex 索引摘要,稿件版本未注明,不能推断刊本变化;摘要未说明混杂控制假设或具体效应大小,亚组结论尚不能独立核验。

原文摘要

There have been increased concerns that the use of statins, one of the most commonly prescribed drugs for treating coronary artery disease, is potentially associated with the increased risk of new-onset Type II diabetes (T2D). Nevertheless, to date, there is no robust evidence supporting as to whether and what kind of populations are indeed vulnerable for developing T2D after taking statins. In this case study, leveraging the biobank and electronic health record data in the Partner Health System, we introduce a new data analysis pipeline and a novel statistical methodology that address existing limitations by (i) designing a rigorous causal framework that systematically examines the causal effects of statin usage on T2D risk in observational data, (ii) uncovering which patient subgroup is most vulnerable for developing T2D after taking statins, and (iii) assessing the replicability and statistical significance of the most vulnerable subgroup via a bootstrap calibration procedure. Our proposed approach delivers asymptotically sharp confidence intervals and debiased estimate for the treatment effect of the most vulnerable subgroup in the presence of high-dimensional covariates. With our proposed approach, we find that females with high T2D genetic risk are at the highest risk of developing T2D due to statin usage.

笔记与分组
相关领域论文