{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T19:02:47Z","timestamp":1792004567744,"version":"4.4.0"},"reference-count":72,"publisher":"Wiley","issue":"24","license":[{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","award":["R01 GM072611\u201016"],"award-info":[{"award-number":["R01 GM072611\u201016"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008982","name":"National Science Foundation of Sri Lanka","doi-asserted-by":"publisher","award":["SES\u20102019432"],"award-info":[{"award-number":["SES\u20102019432"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008982","name":"National Science Foundation of Sri Lanka","doi-asserted-by":"publisher","award":["SES\u20102241575"],"award-info":[{"award-number":["SES\u20102241575"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2023,10,30]]},"abstract":"<jats:p>\n                    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\u2010based 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\u2010corrected 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\u2010retroviral 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\u2010based patient cost\u2010sharing on healthcare utilization in Taiwan. Complete replication materials employing publicly available data and statistical software in\n                    <jats:styled-content>Python<\/jats:styled-content>\n                    ,\n                    <jats:styled-content>R<\/jats:styled-content>\n                    and\n                    <jats:styled-content>Stata<\/jats:styled-content>\n                    are provided, offering researchers all necessary tools to conduct an RD analysis.\n                  <\/jats:p>","DOI":"10.1002\/sim.9861","type":"journal-article","created":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T02:02:04Z","timestamp":1690941724000},"page":"4484-4513","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["A guide to regression discontinuity designs in medical applications"],"prefix":"10.1002","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0493-7506","authenticated-orcid":false,"given":"Matias 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