Estimating time-varying exposure effects through continuous-time modelling in Mendelian randomization
通过连续时间建模估计孟德尔随机化中的时变暴露效应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：摘要明确使用孟德尔随机化工具变量方法研究时变暴露的因果效应。
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…
通过连续时间建模估计孟德尔随机化中的时变暴露效应
- 研究问题
- 在不同个体的暴露测量时点不一致、遗传工具可能较弱时，如何利用连续时间信息估计暴露对结果的时变因果效应？
- 方法与思路
- 作者结合函数主成分分析与弱工具稳健技术，在连续时间框架中估计效应，并用模拟和血压对尿素水平的案例说明。
- 材料陈述的贡献
- 摘要称，正确指定效应形式时估计与推断表现良好，并揭示效应复杂度和工具强度的权衡：复杂效应需非常强的工具。
- 阅读关注点
- 应核对遗传工具的有效性、连续时间效应的参数化、函数主成分截断规则，以及工具强度如何限制可估计的效应复杂度。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要的可靠推断结论以效应形式正确为前提，未给出完整识别条件。
原文摘要
Mendelian randomization is an instrumental variable method that utilizes genetic information to investigate the causal effect of a modifiable exposure on an outcome. In most cases, the exposure changes over time. Understanding the time-varying causal effect of the exposure can yield detailed insights into mechanistic effects and the potential impact of public health interventions. Recently, a growing number of Mendelian randomization studies have attempted to explore time-varying causal effects. However, the proposed approaches oversimplify temporal information and rely on overly restrictive structural assumptions, limiting their reliability in addressing time-varying causal problems. This paper considers a novel approach to estimate time-varying effects through continuous-time modelling by combining functional principal component analysis and weak-instrument-robust techniques. Our method effectively utilizes available data without making strong structural assumptions and can be applied in general settings where the exposure measurements occur at different timepoints for different individuals. We demonstrate through simulations that our proposed method performs well in estimating time-varying effects and provides reliable inference results when the time-varying effect form is correctly specified. The method could theoretically be used to estimate arbitrarily complex time-varying effects. However, there is a trade-off between model complexity and instrument strength. Estimating complex time-varying effects requires instruments that are unrealistically strong. We illustrate the application of this method in a case study examining the time-varying effects of systolic blood pressure on urea levels.