Regression Discontinuity Design: Simulation and Application in Two Cardiovascular Trials with Continuous Outcomes
Open accessAbstract
In epidemiology, the regression discontinuity design has received increasing attention recently and might be an alternative to randomized controlled trials (RCTs) to evaluate treatment effects. In regression discontinuity, treatment is assigned above a certain threshold of an assignment variable for which the treatment effect is adjusted in the analysis. We performed simulations and a validation study in which we used treatment effect estimates from an RCT as the reference for a prospectively performed regression discontinuity study. We estimated the treatment effect using linear regression adjusting for the assignment variable both as linear terms and restricted cubic spline and using local linear regression models. In the first validation study, the estimated treatment effect from a cardiovascular RCT was -4.0 mmHg blood pressure (95% confidence interval: -5.4, -2.6) at 2 years after inclusion. The estimated effect in regression discontinuity was -5.9 mmHg (95% confidence interval: -10.8, -1.0) with restricted cubic spline adjustment. Regression discontinuity showed different, local effects when analyzed with local linear regression. In the second RCT, regression discontinuity treatment effect estimates on total cholesterol level at 3 months after inclusion were similar to RCT estimates, but at least six times less precise. In conclusion, regression discontinuity may provide similar estimates of treatment effects to RCT estimates, but requires the assumption of a global treatment effect over the range of the assignment variable. In addition to a risk of bias due to wrong assumptions, researchers need to weigh better recruitment against the substantial loss in precision when considering a study with regression discontinuity versus RCT design.
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Restricting the bandwidth to reduce bias results in a loss of precision and estimates that may hold over only a small segment of the whole population exposed to the intervention.
“One key shortcoming is that restricting the bandwidth to reduce bias results in a loss of precision (46, 73), and estimates that may hold over only a small segment of the whole population exposed to the intervention.”
From Natural Experiments: An Overview of Methods, Approaches, and Contributions to Public Health Intervention Research (2017) · cites this paper as [73]
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- Authors
- van Leeuwen, N., Lingsma, H. F., de Craen, A. J. M., Nieboer, D., Mooijaart, S. P., Richard, E., & Steyerberg, E. W.
- Affiliations
- Radboud University Nijmegen (NL)
- Venue
- Epidemiology
- Published
- 2016-07
- Volume
- 27
- Issue
- 4
- Pages
- 503-511
- ISSN
- 1044-3983 (ISSN-L)
- OpenAlex
- W2337330039
- Cited by
- 17
- References
- 27
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 1 · 2023: 1 · 2022: 3 · 2021: 1 · 2020: 2 · 2019: 1 · 2018: 2 · 2017: 1 · 2016: 2
- FWCI
- 2.02
- Type
- journal article
- Language
- en
- Open access
- green
- pure.amsterdamumc.nl
- Licence
- other-oa
- Locations
- Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- Pure Amsterdam UMC (acceptedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- regression discontinuity design, treatment effect estimation, restricted cubic splines, local linear regression, total cholesterol
- MeSH terms
- Aged, Aged, 80 and over, Blood Pressure, Cardiovascular Diseases, Cholesterol, Computer Simulation, Coronary Disease, Dementia, Female, Humans, Male, Monte Carlo Method, Regression Analysis, Research Design, Statistics as Topic, Reproducibility of Results, Linear Models, Randomized Controlled Trials as Topic, Pravastatin, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Risk Reduction Behavior
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