发表机构
Harvard T.H. Chan School of Public Health; Heidelberg University, Department of Mathematics; Brigham and Women’s Hospital（哈佛陈曾熙公共卫生学院; 海德堡大学; 布莱根妇女医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文扩展增强逆概率加权框架至风险比估计，整合外部基础模型预测，所得估计器渐近方差不劣于仅用实验数据的默认估计器。
AI 中文摘要
我们考虑利用观察性研究的数据来增强随机化实验或试验，以提高统计精度。特别地，我们关注混合增强逆概率加权估计器，该估计器旨在整合多个基础模型的预测，同时保持有效的统计推断。在本文中，我们将增强逆概率加权框架扩展到风险比的估计，风险比是暴露组与未暴露组绝对风险的比值。我们的方法允许使用在外部且可能非结构化数据上训练的黑盒基础模型的信息，从而得到一个风险比估计器，其渐近方差永远不会大于仅基于实验数据的默认估计器的渐近方差。
英文摘要
We consider the augmentation of randomized experiments or trials with data from observational studies for the purpose of improving statistical precision. In particular, we focus on the Hybrid Augmented Inverse Probability Weighting estimator, designed to integrate predictions from several foundation models while preserving valid statistical inference. In this article, we extend the Augmented Inverse Probability Weighting framework to the estimation of the risk ratio, the quotient of the absolute risk of an exposed to an unexposed group. Our approach allows one to use information from black-box foundation models trained on external and possibly unstructured data, yielding an estimator of the risk ratio whose asymptotic variance is never larger than the one of the default estimator based on experimental data alone.
