医学应用中的回归断点设计指南
A Guide to Regression Discontinuity Designs in Medical Applications
AI总结：
本文为生物医学中的回归断点设计提供实用指南，涵盖连续性和局部随机化框架下的估计、推断及证伪检验，并通过三个实证应用展示模糊和离散分数RD设计，附有Python、R和Stata复制材料。
摘要：
我们为生物医学背景下的回归断点（RD）设计分析提供了一本实用指南。我们首先在基于连续性的框架和局部随机化框架内介绍关键概念、假设和估计目标。然后，我们讨论这两个框架内的现代估计和推断方法，包括带宽或局部邻域选择的方法、最优处理效应点估计，以及用于不确定性量化的稳健偏差校正推断方法。我们还概述了可用于支持关键假设的经验证伪检验。我们的讨论聚焦于生物医学研究中相关的两个特定特征：（i）模糊RD设计，这通常出现在治疗基于临床指南但分数接近截止点的患者被违背分配规则治疗的情况下；（ii）离散分数的RD设计，这在生物医学应用中普遍存在。我们用三个实证应用来说明我们的讨论：南非CD4指南对抗逆转录病毒治疗对HIV患者保留的影响，美国遗传指南对化疗对乳腺癌复发的影响，以及台湾基于年龄的患者费用分担对医疗保健利用的影响。我们提供了使用Python、R和Stata中公开可用的统计软件的复制材料，为研究人员提供了进行RD分析的所有必要工具。
英文摘要：
We present a practical guide for the analysis of regression discontinuity (RD) designs in biomedical contexts. We begin by introducing key concepts, assumptions, and estimands within both the continuity-based framework and the local randomization framework. We then discuss modern estimation and inference methods within both frameworks, including approaches for bandwidth or local neighborhood selection, optimal treatment effect point estimation, and robust bias-corrected inference methods for uncertainty quantification. We also overview empirical falsification tests that can be used to support key assumptions. Our discussion focuses on two particular features that are relevant in biomedical research: (i) fuzzy RD designs, which often arise when therapeutic treatments are based on clinical guidelines but patients with scores near the cutoff are treated contrary to the assignment rule; and (ii) RD designs with discrete scores, which are ubiquitous in biomedical applications. We illustrate our discussion with three empirical applications: the effect of CD4 guidelines for anti-retroviral therapy on retention of HIV patients in South Africa, the effect of genetic guidelines for chemotherapy on breast cancer recurrence in the United States, and the effects of age-based patient cost-sharing on healthcare utilization in Taiwan. We provide replication materials employing publicly available statistical software in Python, R and Stata, offering researchers all necessary tools to conduct an RD analysis.
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