A guide to regression discontinuity designs in medical applications
A guide to regression discontinuity designs in medical applications
复制标题
医疗应用中的回归不连续性设计指南
DOI：
10.1002/sim.9861
复制
发表时间：
2023
影响因子：
2
通讯作者：
R. Titiunik
中科院分区：
文献类型：
--
作者：
M. D. Cattaneo;L. Keele;R. Titiunik
我们提出了一个实用的指南，在生物医学背景下的回归不连续性（RD）设计的分析。我们开始介绍基于连续性的框架和局部随机化框架中的关键概念、假设和被估量。然后，我们在这两个框架内讨论现代估计和推理方法，包括带宽或局部邻域选择方法，最佳治疗效应点估计，以及用于不确定性量化的稳健偏差校正推理方法。我们还概述了可用于支持关键假设的经验证伪检验。我们的讨论集中在两个特定的功能，是相关的生物医学研究：（一）模糊的RD设计，这往往会出现时，治疗的治疗是基于临床指南，但患者的分数接近临界值的治疗相反的分配规则;和（ii）RD设计离散的分数，这是无处不在的生物医学应用。我们用三个实证应用来说明我们的讨论：南非抗逆转录病毒治疗的CD 4指南对HIV患者保留的影响，美国化疗的遗传指南对乳腺癌复发的影响，以及台湾基于年龄的患者成本分摊对医疗保健利用的影响。完整的复制材料采用公开可用的数据和统计软件在Python，R和Stata提供，为研究人员提供所有必要的工具来进行研发分析。
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 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. Complete replication materials employing publicly available data and statistical software in Python, R and Stata are provided, offering researchers all necessary tools to conduct an RD analysis.
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DOI：
10.1146/annurev-economics-080217-053402
发表时间：
2018-01-01
期刊：
ANNUAL REVIEW OF ECONOMICS, VOL 10
影响因子：
--
作者：
Abadie, Alberto;Cattaneo, Matias D.
通讯作者：
Cattaneo, Matias D.
影响因子：
51.1
作者：
Albain, Kathy S.;Barlow, William E.;Shak, Steven;Hortobagyi, Gabriel N.;Livingston, Robert B.;Yeh, I-Tien;Ravdin, Peter;Bugarini, Roberto;Boehner, Frederick L.;Davidson, Nancy E.;Sledge, George W.;Winer, Eric P.;Hudis, Clifford;Ingle, James N.;Perez, Edith A.;Pritchard, Kathleen I.;Shepherd, Lois;Gralow, Julie R.;Yoshizawa, Carl;Allred, D. Craig;Osborne, C. Kent;Hayes, Daniel F.
通讯作者：
Hayes, Daniel F.
影响因子：
1.5
作者：
Calonico, Sebastian;Cattaneo, Matias D.;Farrell, Max H.
通讯作者：
Farrell, Max H.
影响因子：
5
作者：
Glymour, M. Maria;Tchetgen, Eric J. Tchetgen;Robins, James M.
通讯作者：
Robins, James M.
影响因子：
45.3
作者：
Paik, Soonmyung;Tang, Gong;Wolmark, Norman
通讯作者：
Wolmark, Norman