Matias D. Cattaneo, Luke Keele, Rocio Titiunik
arXiv 15 Feb 2023 · Statistics — Methodology · publishedStatistics in Medicine (2023) · 32 citations (OpenAlex)
arXiv:2302.07413 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | height .65ex depth -.6ex width 3em\ (2023) b) | 1.000 | 9 | 5 | 100% |
| 2 | Cattaneo, M. D., Idrobo, N., and Titiunik, R (2020) a) self | 0.928 | 4 | 4 | 100% |
| 3 | Cattaneo, M. D., and Titiunik, R (2022) Regression Discontinuity Designs self | 0.928 | 4 | 4 | 100% |
| 4 | Cattaneo, M. D., Titiunik, R., and Vazquez-Bare, G (2017) Comparing Inference Approaches for RD Designs: A Reexamination of the Effect of Head Start on Child Mortality self | 0.874 | 5 | 2 | 100% |
| 5 | Imbens, G. W., and Rubin, D. B (2015) Causal Inference in Statistics, Social, and Biomedical Sciences | 0.843 | 3 | 3 | 100% |
| 6 | Rosenbaum, P. R (2010) Design of Observational Studies | 0.843 | 3 | 3 | 100% |
| 7 | Cattaneo, M. D., Frandsen, B., and Titiunik, R (2015) Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S self | 0.811 | 4 | 2 | 100% |
| 8 | Baiocchi, M., Cheng, J., and Small, D. S (2014) Instrumental variable methods for causal inference | 0.737 | 3 | 2 | 100% |
| 9 | Bor, J., Fox, M. P., Rosen, S., Venkataramani, A., Tanser, F., Pilla… (2017) Treatment eligibility and retention in clinical HIV care: A regression discontinuity study in South Africa | 0.737 | 3 | 2 | 100% |
| 10 | height .65ex depth -.6ex width 3em\ (2021) Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity Designs | 0.737 | 3 | 2 | 100% |
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