On the uncertainty from the first-stage estimation of prognostic covariate adjustment in randomized controlled trials
随机对照试验中预后协变量调整的第一阶段估计不确定性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：摘要研究随机对照试验平均处理效应的预后协变量调整，以及两阶段估计对效应推断方差的影响。
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
随机对照试验中预后协变量调整的第一阶段估计不确定性
- 研究问题
- 先用历史数据估计预后评分，再在随机试验中进行协方差分析时，忽略评分估计误差的方差估计是否仍然具有渐近有效性？
- 方法与思路
- 作者比较预后评分已知与估计两种情形的渐近方差，考虑具有 L₂ 一致性的机器学习评分，并构造计入或忽略评分误差的方差估计。
- 材料陈述的贡献
- 摘要称两种渐近方差相等，两类方差估计均渐近有效；通常建议使用较简单者，历史样本较小时可为保守推断考虑显式计入误差。
- 阅读关注点
- 应核对历史与试验样本的关系、渐近规模及评分一致性条件，以及方差相等的推导、小历史样本情形和保守性的证据。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实。摘要未列出完整正则条件，渐近方差相等并不保证两种估计在有限样本中等同。
原文摘要
Prognostic covariate adjustment (PROCOVA) is a two-sample two-stage estimation method for covariate adjustment in randomized controlled trials. In the first stage, a prognostic score, defined as the conditional expectation of an outcome given covariates under the control treatment, is estimated using historical data. In the second stage, analysis of covariance with the estimated prognostic score and treatment assignment as explanatory variables is performed, and the average treatment effect is estimated. Although the prognostic score is estimated in this procedure, the variance estimator, which treats the prognostic score as known, has been used. Furthermore, the difference in the asymptotic variance between cases where the prognostic score is known versus where it is estimated has not been previously clarified. In this study, we derived these two asymptotic variances and showed that they are equal. This result also holds when the prognostic score is estimated using machine learning with $L_2$ consistency. We also constructed two variance estimators: one that treats the prognostic score as known, and another that accounts for its estimation, and compared their performance through simulation studies and data applications. For PROCOVA, since both variance estimators are asymptotically valid, it is generally recommended to use a variance estimator that treats the prognostic score as known, as it is simpler to derive and implement. When historical data is small, a variance estimator that explicitly accounts for prognostic score estimation is recommended if conservative inference is preferred.