The regression discontinuity design: Methods and implementation with a worked example in health services research

Abstract

Background: The randomized controlled trial (RCT) is the gold standard in evidence-based medicine. However, this design may not be appropriate in every setting, so other methods or designs such as the regression discontinuity design (RDD) are required.

Method: The aim of this article is to introduce the RDD, summarise methodology in the context of health services research and present a worked example using the statistic software SPSS (Examples for R and Stata in the Appendix A). The mathematical notations of sharp and fuzzy RDD as well as their distinction are presented. Furthermore, examples from the literature and recent studies are highlighted, and both advantages and disadvantages of the design are discussed.

Application: The RDD consists of four essential steps: 1. Determine feasibility; 2. Note possible treatment manipulation, 3. Check for the treatment effect, and 4. Fit the regression models to measure the treatment effect.

Conclusion: The RDD comes as an alternative for studies in health service research where an RCT cannot be conducted, but a threshold-based comparison can be made.

Keywords: Health service research; Medical statistics; Medizinische Statistik; Quasi-experimental quasi-randomization Statistical software; Quasi-experimentelle Methoden; Regression discontinuity; Regressionsdiskontinuität; Statistische Software; Versorgungsforschung.