Estimating the Marginal Effect of a Continuous Exposure on an Ordinal Outcome using Data Subject to Covariate-Driven Treatment and Visit Processes
协变量驱动暴露与随访下连续暴露对有序结局的边际效应估计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：摘要明确研究边际因果效应、连续暴露的混杂控制，以及纵向个体数据中受协变量影响的随访机制。
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…
协变量驱动暴露与随访下连续暴露对有序结局的边际效应估计
- 研究问题
- 当连续暴露和纵向随访时间均受协变量影响时，如何同时处理混杂与不规则观测偏差，估计连续暴露对有序结局的边际因果效应？
- 方法与思路
- 作者用比例优势模型描述有序结局、比例率模型处理不规则观测时间，并推广逆概率处理权重以适应连续暴露，辅以模拟和应用。
- 材料陈述的贡献
- 摘要提出同时处理暴露与观测机制的方法，并称模拟表明忽略两类机制造成的不平衡可能使暴露的边际优势比估计产生偏差。
- 阅读关注点
- 应核对暴露及观测机制的识别条件、权重构造与模型设定，并区分实际应用的预设随访时间和模拟中的个体间不规则观测。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实。摘要未列出完整识别条件和数值结果，实际应用并未直接覆盖高度不规则随访。
原文摘要
In the statistical literature, a number of methods have been proposed to ensure valid inference about marginal effects of variables on a longitudinal outcome in settings with irregular monitoring times. However, the potential biases due to covariate-driven monitoring times and confounding have rarely been considered simultaneously, and never in a setting with an ordinal outcome and a continuous exposure. In this work, we propose and demonstrate a methodology for causal inference in such a setting, relying on a proportional odds model to study the effect of the exposure on the outcome. Irregular observation times are considered via a proportional rate model, and a generalization of inverse probability of treatment weights is used to account for the continuous exposure. We motivate our methodology by the estimation of the marginal (causal) effect of the time spent on video or computer games on suicide attempts in the Add Health study, a longitudinal study in the United States. Although in the Add Health data, observation times are pre-specified, our proposed approach is applicable even in more general settings such as when analyzing data from electronic health records where observations are highly irregular. In simulation studies, we let observation times vary across individuals and demonstrate that not accounting for biasing imbalances due to the monitoring and the exposure schemes can bias the estimate for the marginal odds ratio of exposure.