论文标题
统计匹配和子分类以连续剂量：表征，算法和对健康结果的应用
Statistical matching and subclassification with a continuous dose: characterization, algorithm, and application to a health outcomes study
论文作者
论文摘要
在经验研究中通常使用亚分类和匹配来调整观察到的协变量。但是，它们在很大程度上仅限于相对简单的研究设计，并使用二元处理，而对于连续暴露的设计较少。与曝光剂量匹配在仪器变量设计和理解剂量反应关系方面特别有用。在本文中，我们提出了两个在持续暴露剂量的背景下基于子类同质性的最佳子类别的标准，并提出了有效的多项式时间算法，该算法可以保证，该算法可以针对一种标准找到一个最佳的亚类化，并作为一个2-苹果型临床算法作为2-苹果型算法。我们讨论如何合并剂量并使用适当的惩罚来控制设计中的子类的数量。通过广泛的模拟，我们将我们提出的设计与最佳的非双分配对匹配进行了系统地比较，并证明将我们提出的亚分类方案与回归调整相结合有助于减少对参数因果推理的模型依赖性与连续剂量的依赖性。我们将新的设计和相关的基于随机化的推论程序应用于冠状动脉旁路移植物（CABG）手术期间对经食管的超声心动图（TEE）监测对患者使用Medicare和Medicaid索赔数据的临床结果的影响，并找到TEE降低患者的所有因子降低了所有因子的证据。
Subclassification and matching are often used in empirical studies to adjust for observed covariates; however, they are largely restricted to relatively simple study designs with a binary treatment and less developed for designs with a continuous exposure. Matching with exposure doses is particularly useful in instrumental variable designs and in understanding the dose-response relationships. In this article, we propose two criteria for optimal subclassification based on subclass homogeneity in the context of having a continuous exposure dose, and propose an efficient polynomial-time algorithm that is guaranteed to find an optimal subclassification with respect to one criterion and serves as a 2-approximation algorithm for the other criterion. We discuss how to incorporate dose and use appropriate penalties to control the number of subclasses in the design. Via extensive simulations, we systematically compare our proposed design to optimal non-bipartite pair matching, and demonstrate that combining our proposed subclassification scheme with regression adjustment helps reduce model dependence for parametric causal inference with a continuous dose. We apply the new design and associated randomization-based inferential procedure to study the effect of transesophageal echocardiography (TEE) monitoring during coronary artery bypass graft (CABG) surgery on patients' post-surgery clinical outcomes using Medicare and Medicaid claims data, and find evidence that TEE monitoring lowers patients' all-cause $30$-day mortality rate.
