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.

翻译：我们提出了一份在生物医学背景下进行断点回归（RD）分析的使用指南。首先，我们在连续框架和局部随机化框架内介绍核心概念、假设及估计量。随后，我们讨论两个框架下的现代估计与推断方法，包括带宽或局部邻域选择、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两个相关的特殊特征：（i）模糊RD设计——当治疗基于临床指南，但评分接近阈值的患者被违反分配规则进行治疗时，这种情况经常出现；（ii）离散评分的RD设计——这在生物医学应用中普遍存在。我们通过三个实证应用阐明了讨论内容：南非CD4指南对抗逆转录病毒治疗中HIV患者保留率的影响、美国遗传指南对化疗中乳腺癌复发的影响，以及台湾基于年龄的患者费用分摊对医疗利用的影响。我们提供了使用Python、R和Stata中公开可用的统计软件的重复实验材料，为研究人员提供了进行RD分析所需的所有必要工具。