Estimating Marginal Treatment Effect in Cluster Randomized Trials with Multi-level Missing Outcomes
多层结局缺失下整群随机试验的边际处理效应估计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：摘要围绕整群随机试验的处理效应及信息性缺失校正展开，直接对应因果推断；多层嵌套结构不等同于面板数据。
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
多层结局缺失下整群随机试验的边际处理效应估计
- 研究问题
- 整群随机试验中，个体、子群及整群层面的信息性结局缺失如何影响边际处理效应估计，怎样避免忽略整群缺失的偏差？
- 方法与思路
- 作者基于加权广义估计方程构造处理多层缺失的估计量，并提出多层多重稳健估计，通过模拟和疟疾干预试验进行评估。
- 材料陈述的贡献
- 摘要称，在其中一组倾向评分模型正确设定时，多层多重稳健估计量具有一致性和渐近正态性，并可处理多个层级的缺失。
- 阅读关注点
- 应核对边际处理效应的目标总体、各层缺失机制及权重构造，明确一组模型正确的含义，并检查整群相关性的方差处理。
- 阅读边界
- 所给版本为 arXiv 预印本，不能据此确认同行评审或接收；摘要未完整说明缺失识别条件、正值性要求和有限样本结果。
原文摘要
Analyses of cluster randomized trials (CRTs) can be complicated by informative missing outcome data. Methods such as inverse probability weighted generalized estimating equations have been proposed to account for informative missingness by weighting the observed individual outcome data in each cluster. These existing methods have focused on settings where missingness occurs at the individual level and each cluster has partially or fully observed individual outcomes. In the presence of missing clusters, e.g., all outcomes from a cluster are missing due to drop-out of the cluster, these approaches effectively ignore this cluster-level missingness and can lead to biased inference if the cluster-level missingness is informative. Informative missingness at multiple levels can also occur in CRTs with a multi-level structure where study participants are nested in subclusters such as health care providers, and the subclusters are nested in clusters such as clinics. In this paper, we propose new estimators for estimating the marginal treatment effect in CRTs accounting for missing outcome data at multiple levels based on weighted generalized estimating equations. We show that the proposed multi-level multiply robust estimator is consistent and asymptotically normally distributed provided that one set of the propensity score models is correctly specified. We evaluate the performance of the proposed method through extensive simulation and illustrate its use with a CRT evaluating a Malaria risk-reduction intervention in rural Madagascar.