A randomization-based theory for preliminary testing of covariate balance in controlled trials
随机试验协变量平衡预检验的随机化理论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：摘要研究随机试验中的处理效应推断，以及协变量平衡预检验和回归调整对推断有效性的影响。
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…
随机试验协变量平衡预检验的随机化理论
- 研究问题
- 随机试验出现基线协变量不平衡时，先检验平衡、仅在不平衡时调整的策略，会怎样影响处理效应推断的效率与覆盖率？
- 方法与思路
- 作者从随机化视角分析两种基于平衡预检验的回归调整策略，分别采用协方差分析与完全交互规格，并结合模拟进行评价。
- 材料陈述的贡献
- 摘要称前一种策略可能低效且区间反保守，后一种较始终调整低效且区间过保守，因此作者不推荐平衡预检验调整程序。
- 阅读关注点
- 应核对两种回归规格、平衡判定标准和随机化渐近条件，并分别比较不调整、预检验调整与始终调整的效率及覆盖率。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未获核实；摘要未说明完整试验设计条件，结论针对所研究的预检验调整与区间构造。
原文摘要
Randomized trials balance all covariates on average and provide the gold standard for estimating treatment effects. Chance imbalances nevertheless exist more or less in realized treatment allocations and intrigue an important question: what should we do in case the treatment groups differ with respect to some important baseline characteristics? A common strategy is to conduct a {\it preliminary test} of the balance of baseline covariates after randomization, and invoke covariate adjustment for subsequent inference if and only if the realized allocation fails some prespecified criterion. Although such practice is intuitive and popular among practitioners, the existing literature has so far only evaluated its properties under strong parametric model assumptions in theory and simulation, yielding results of limited generality. To fill this gap, we examine two strategies for conducting preliminary test-based covariate adjustment by regression, and evaluate the validity and efficiency of the resulting inferences from the randomization-based perspective. As it turns out, the preliminary-test estimator based on the analysis of covariance can be even less efficient than the unadjusted difference in means, and risks anticonservative confidence intervals based on normal approximation even with the robust standard error. The preliminary-test estimator based on the fully interacted specification is on the other hand less efficient than its counterpart under the {\it always-adjust} strategy, and yields overconservative confidence intervals based on normal approximation. Based on theory and simulation, we echo the existing literature and do not recommend the preliminary-test procedure for covariate adjustment in randomized trials.