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A Guide to Regression Discontinuity Designs in Medical Applications by Matias D. Cattaneo; Luke Keele; Rocío Titiunik is a Mathematics article available to read on EtoBox.

What is A Guide to Regression Discontinuity Designs in Medical Applications about?

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 chemotherap

Who reads A Guide to Regression Discontinuity Designs in Medical Applications?

Author

Matias D. Cattaneo; Luke Keele; Rocío Titiunik

Publisher

Wiley

Published

2023

Language

EN

Field

Mathematics (Physical Sciences)