Instrumental Variable Approach to Estimating Individual Causal Effects in N-of-1 Trials: Application to ISTOP Study
N-of-1 试验中个体因果效应的工具变量估计：ISTOP 应用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：摘要明确以潜在结果框架及随机分配工具变量估计不完全依从下的个体因果效应。
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…
关注这篇论文的作者
N-of-1 试验中个体因果效应的工具变量估计：ISTOP 应用
- 研究问题
- 在单个受试者反复交叉试验中，存在不完全依从、二元结果和序列相关时，如何估计持续暴露及实际行为的个体因果效应？
- 方法与思路
- 作者以潜在处理和结果路径定义效应，将随机分配作为工具变量，用两个参数化贝叶斯模型及潜在结构处理混杂和自相关。
- 材料陈述的贡献
- 摘要称通过后验泛函推断处理优势比的非可折叠等问题，模拟中减少偏差并改善覆盖率，且应用于酒精与房颤的个体效应。
- 阅读关注点
- 应核对两个个体效应的定义、随机分配不直接影响结果的依据，以及参数模型、自相关处理与后验推断之间的关系。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审与期刊接收状态未核实；摘要未说明工具变量识别的完整条件、先验敏感性或效果数值。
原文摘要
An N-of-1 trial is a multiple crossover trial conducted in a single individual to provide evidence to directly inform personalized treatment decisions. Advancements in wearable devices greatly improved the feasibility of adopting these trials to identify optimal individual treatment plans, particularly when treatments differ among individuals and responses are highly heterogeneous. Our work was motivated by the I-STOP-AFib Study, which examined the impact of different triggers on atrial fibrillation (AF) occurrence. We described a causal framework for 'N-of-1' trial using potential treatment selection paths and potential outcome paths. Two estimands of individual causal effect were defined:(a) the effect of continuous exposure, and (b) the effect of an individual observed behavior. We addressed three challenges: (a) imperfect compliance to the randomized treatment assignment; (b) binary treatments and binary outcomes which led to the 'non-collapsibility' issue of estimating odds ratios; and (c) serial inference in the longitudinal observations. We adopted the Bayesian IV approach where the study randomization was the IV as it impacted the choice of exposure of a subject but not directly the outcome. Estimations were through a system of two parametric Bayesian models to estimate the individual causal effect. Our model got around the non-collapsibility and non-consistency by modeling the confounding mechanism through latent structural models and by inferring with Bayesian posterior of functionals. Autocorrelation present in the repeated measurements was also accounted for. The simulation study showed our method largely reduced bias and greatly improved the coverage of the estimated causal effect, compared to existing methods (ITT, PP, and AT). We applied the method to I-STOP-AFib Study to estimate the individual effect of alcohol on AF occurrence.