Objectives: Regression discontinuity (RD) designs allow for rigorous causal inference when patients receive a treatment based on scoring above or below a cutoff point on a continuously measured variable. We provide an introduction to the theory of RD and a systematic review and assessment of the RD literature in medicine, epidemiology, and public health.
Study design and setting: We review the necessary conditions for valid RD results, provide a practical guide to RD implementation, compare RD to other methodologies, and conduct a systematic review of the RD literature in PubMed.
Results: We describe five key elements of analysis all RD studies should report, including tests of validity conditions and robustness checks. Thirty two empirical RD studies in PubMed met our selection criteria. Most of the 32 RD articles analyzed the effectiveness of social policies or mental health interventions, with only two evaluating clinical interventions to improve physical health. Seven out of the 32 studies reported on all the five key elements.
Conclusion: Increased use of RD provides an exciting opportunity for obtaining unbiased causal effect estimates when experiments are not feasible or when we want to evaluate programs under "real-life" conditions. Although treatment eligibility in medicine, epidemiology, and public health is commonly determined by threshold rules, use of RD in these fields has been very limited until now.
Keywords: Causal inference; Confounding; Natural experiments; Observational studies; Quasi-experimental methods, Systematic review; Regression discontinuity.
Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.