A guide to regression discontinuity designs in medical applications

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.

Automated summary

We provide a practical guide for the analysis of regression discontinuity (RD) designs in biomedical research. The key concepts, assumptions, and estimands within both the continuity-based and local randomization frameworks are introduced. Modern estimation and inference methods within both frameworks are discussed, along with empirical falsification tests for supporting key assumptions. The importance of considering fuzzy RD designs and RD designs with discrete scores in biomedical research is also emphasized. Three empirical applications are presented to illustrate the discussion, and replication materials are provided for researchers to conduct RD analysis using publicly available data and statistical software in Python, R, and Stata.