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The regression discontinuity design: Methods and implementation with a worked example in health services research
Anna Hagemeier1, Christina Samel1, Martin Hellmich1
1Institute of Medical Statistics and Computational Biology, Medical Faculty, University of Cologne, University Hospital Cologne, Cologne, Germany.
Summary
The Regression Discontinuity Design (RDD) offers an alternative to randomized controlled trials (RCTs) in health services research. This method is useful when threshold-based comparisons are possible but RCTs are not feasible.
Area of Science:
- Health Services Research
- Evidence-Based Medicine
- Biostatistics
Background:
- The randomized controlled trial (RCT) is the standard for evidence-based medicine.
- However, RCTs are not always feasible in all research settings.
- Alternative designs like the Regression Discontinuity Design (RDD) are necessary.
Purpose of the Study:
- To introduce the Regression Discontinuity Design (RDD).
- To summarize RDD methodology for health services research.
- To provide a practical example using SPSS, with R and Stata examples.
Main Methods:
- Explains the mathematical notations for sharp and fuzzy RDD.
- Highlights examples from existing literature and recent studies.
- Discusses the advantages and disadvantages of the RDD.
Main Results:
- The RDD involves four key steps: feasibility assessment, treatment manipulation identification, treatment effect verification, and regression modeling.
- The RDD provides a robust method for causal inference when an RCT is not possible.
Conclusions:
- The RDD is a valuable alternative to RCTs in health services research.
- It is particularly useful in situations where a clear threshold allows for comparison.
- This design enables rigorous study when traditional randomization is not an option.
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