Addressing selection bias in cluster randomized experiments via weighting
通过加权处理整群随机试验中的选择偏差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：摘要直接研究随机试验招募选择、逆概率加权及主层处理效应识别。
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…
通过加权处理整群随机试验中的选择偏差
- 研究问题
- 当受试者在整群处理分配后才被招募、且仅有招募样本数据时，哪些总体或子群处理效应仍可识别并一致估计？
- 方法与思路
- 作者在可忽略招募假设下使用逆概率加权，并借助主层划分识别始终被招募者和仅处理下被招募者的效应。
- 材料陈述的贡献
- 摘要称可一致估计招募人群效应，并识别两个总体子群效应，但一般无法识别总体平均效应；另提供敏感性分析与应用。
- 阅读关注点
- 应核对可忽略招募的定义、主层识别是否需要额外限制、权重构造及整群推断方法，并明确各效应对应的目标人群。
- 阅读边界
- 这是arXiv预印本，同行评审与期刊接收未核实。摘要未列出全部主层识别条件和应用数值，随机分配本身不能消除招募偏差。
原文摘要
In cluster randomized experiments, individuals are often recruited after the cluster treatment assignment, and data are typically only available for the recruited sample. Post-randomization recruitment can lead to selection bias, inducing systematic differences between the overall and the recruited populations, and between the recruited intervention and control arms. In this setting, we define causal estimands for the overall and the recruited populations. We prove, under the assumption of ignorable recruitment, that the average treatment effect on the recruited population can be consistently estimated from the recruited sample using inverse probability weighting. Generally we cannot identify the average treatment effect on the overall population. Nonetheless, we show, via a principal stratification formulation, that one can use weighting of the recruited sample to identify treatment effects on two meaningful subpopulations of the overall population: individuals who would be recruited into the study regardless of the assignment, and individuals who would be recruited into the study under treatment but not under control. We develop an estimation strategy and a sensitivity analysis approach for checking the ignorable recruitment assumption. The proposed methods are applied to the ARTEMIS cluster randomized trial, where removing co-payment barriers increases the persistence of P2Y12 inhibitor among the always-recruited population.