Comparing Missing Data Methods for Estimating Average Treatment Effects Under Time-Varying Confounding: A Simulation Study
时变混杂下平均处理效应估计的缺失数据方法比较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：摘要直接比较缺失与时变混杂条件下的平均处理效应和倾向得分加权估计。
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…
时变混杂下平均处理效应估计的缺失数据方法比较
- 研究问题
- 二元变量存在时变混杂时，缺失机制、比例、位置与样本量如何共同影响不同缺失处理方法的平均处理效应估计与覆盖率？
- 方法与思路
- 作者在合成数据中比较热卡、众数、链式方程多重插补及完整案例分析，采用倾向得分加权估计效应，考察偏差、覆盖和经验标准误。
- 材料陈述的贡献
- 摘要称，多重插补通常获得更好的覆盖和更低偏差；缺失机制与方法选择影响最大，非随机缺失、高缺失率和小样本加剧正值性问题。
- 阅读关注点
- 应核对时变处理与混杂的生成结构、插补模型包含的信息、倾向得分权重构造，以及可交换性和正值性违背如何定义与测量。
- 阅读边界
- 这是预印本，不能据此认定已同行评审或期刊接收。摘要未说明完整纵向识别条件，模拟中偏差较小不能证明实际缺失数据下因果效应可识别。
原文摘要
Missing data and confounding are common in real-world statistical applications, yet few studies have examined how imputation methods perform under time-varying confounding in binary variables, or how missingness mechanism, missing rate, missingness location and sample size jointly affect performance and the underlying identifiability conditions. We generated synthetic data and conducted a simulation study comparing missing data methods across scenarios varying these factors. Missingness was introduced in both treatment and outcome variables, and we applied stratified hot deck imputation, single mode imputation, multiple imputation with chained equations (MICE), and complete-case analysis. Average treatment effect (ATE) estimates were obtained using logistic regression with propensity score weighting, and we measured coverage, absolute bias and empirical standard errors across 48 scenarios with 500 replications each. Performance was primarily driven by the missingness mechanism and choice of method, with multiple imputation generally achieving better coverage and lower bias than other methods. Missingness location was also important, while missing rate and sample size primarily affected positivity violations, which were most pronounced under MNAR, high missingness and low sample sizes. Exchangeability violations from mild to moderate confounding were adequately controlled for by propensity score models, whereas strong confounding produced a modest decrease in coverage. Further research should examine additional ways identifiability conditions can be violated under missingness, using more advanced methods and more complex missingness scenarios.