The impacts of unobserved covariates on covariate-adaptive randomized experiments
未观测协变量对协变量自适应随机实验的影响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。
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：摘要直接讨论随机实验中的处理效应识别、估计及检验，并分析未观测协变量交互的影响。
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
未观测协变量对协变量自适应随机实验的影响
- 研究问题
- 当处理响应与未观测协变量发生交互时，平衡已观测协变量的自适应随机化如何影响处理效应、协变量效应的估计与检验？
- 方法与思路
- 作者在线性模型中纳入处理与未观测协变量的交互，推导渐近性质及效应可识别的充分条件，并提出基于残差的调整检验。
- 材料陈述的贡献
- 摘要称解释了效应估计不一致的机制及部分条件下检验偏保守的现象，并在效应可正确估计时通过残差调整恢复第一类错误水平。
- 阅读关注点
- 应核对目标效应的定义、未观测交互如何导致不一致、识别充分条件，以及残差调整何时能够校准检验水平。
- 阅读边界
- 依据版本未注明的 OpenAlex 索引摘要；随机化方案和识别条件未详述，残差调整的主张以效应可正确估计为前提。
原文摘要
Covariate-adaptive randomization (CAR) is commonly implemented in clinical trials to balance observed covariates. Recent studies have demonstrated the advantages of CAR procedures in balancing covariates and improving the subsequent statistical analysis. Covariate balance is crucial, but it is not a panacea for the valid statistical inferences. If the response to a treatment interacts with some unobserved covariates, the conclusion drawn from a CAR experiment may be affected, and thus, be inconsistent with other evidence. This paper aims to demonstrate the relationships between unobserved covariates and the analysis of treatment and covariate effects in CAR experiments. We first derive the asymptotic properties of the statistical methods based on a linear model framework with interactions between the treatment and an unobserved covariate. We also provide sufficient conditions for the identifiability of the treatment and covariate effects. Our results theoretically explain how inconsistent estimations are generated in CAR experiments when some important covariates are unobserved. Under these sufficient conditions, we show that the tests for the treatment and covariate effects can have reduced Type I errors under CAR procedures. A residual-based adjusted test is proposed to recover the Type I error when the effect can be correctly estimated. Numerical studies are conducted to evaluate the performance of our proposed procedure and theoretical findings.