The DOI matches its Crossref record; 51 of the 52 references it cites were found in a registry or an index.
Regression Discontinuity Designs in Epidemiology: Causal Inference Without Randomized Trials
Open accessAbstract
When patients receive an intervention based on whether they score below or above some threshold value on a continuously measured random variable, the intervention will be randomly assigned for patients close to the threshold. The regression discontinuity design exploits this fact to estimate causal treatment effects. In spite of its recent proliferation in economics, the regression discontinuity design has not been widely adopted in epidemiology. We describe regression discontinuity, its implementation, and the assumptions required for causal inference. We show that regression discontinuity is generalizable to the survival and nonlinear models that are mainstays of epidemiologic analysis. We then present an application of regression discontinuity to the much-debated epidemiologic question of when to start HIV patients on antiretroviral therapy. Using data from a large South African cohort (2007-2011), we estimate the causal effect of early versus deferred treatment eligibility on mortality. Patients whose first CD4 count was just below the 200 cells/μL CD4 count threshold had a 35% lower hazard of death (hazard ratio = 0.65 [95% confidence interval = 0.45-0.94]) than patients presenting with CD4 counts just above the threshold. We close by discussing the strengths and limitations of regression discontinuity designs for epidemiology.
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“Some methods, such as fuzzy regression discontinuity (RD) and instrumental variables (IVs), estimate a different effect, the complier average causal effect (CACE), which is the effect of the intervention on those who comply with their allocation into the control or intervention group (11). Under certain assumptions, the CACE is equivalent to the ITT effect divided by the proportion of compliers.”
From Natural Experiments: An Overview of Methods, Approaches, and Contributions to Public Health Intervention Research (2017) · cites this paper as [11]
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“RD is based on the insight that units with values of the assignment variable just above or below the cutoff for entitlement will be similar in other respects, especially if there is random error in the assignment variable (11).”
From Natural Experiments: An Overview of Methods, Approaches, and Contributions to Public Health Intervention Research (2017) · cites this paper as [11]
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Cites
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Identification of Causal Effects Using Instrumental Variables
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Manipulation of the Running Variable in the Regression Discontinuity Design: A Density Test,
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Local Polynomial Modelling and its Applications
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Craig, P., Katikireddi, S. V., Leyland, A., & Popham, F. (2017) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-031816-044327 · Cited by 570
Skivington, K., Matthews, L., Simpson, S. A., Craig, P., Baird, J., Blazeby, J. M., Boyd, K., Craig, N. J., French, D., McIntosh, E., et al. (2021) · Health Technology Assessment · doi:10.3310/hta25570 · Cited by 585
Outcome-Wide Longitudinal Designs for Causal Inference: A New Template for Empirical Studies
VanderWeele, T. J., Mathur, M. B., & Chen, Y. (2020) · Statistical Science · doi:10.1214/19-sts728 · Cited by 338
Moore, T. H., Kesten, J. M., López-López, J. A., Ijaz, S., McAleenan, A., Richards, A. R., Gray, S. F., Savović, J., & Audrey, S. (2018) · Health & Place · doi:10.1016/j.healthplace.2018.07.012 · Cited by 290
Been, J. V., Ochoa, L. B., Bertens, L. C. M., Schoenmakers, S., Steegers, E. A. P., & Reiss, I. K. M. (2020) · The Lancet Public Health · doi:10.1016/s2468-2667(20)30223-1 · Cited by 261
Metadata
- Authors
- Bor, J., Moscoe, E., Mutevedzi, P., Newell, M.-L., & Bärnighausen, T.
- Affiliations
- Boston University (US)
- Harvard University (US)
- University of Southampton (GB)
- Corresponding author
- Bor, Jacob
- Venue
- Epidemiology
- Published
- 2014-09
- Volume
- 25
- Issue
- 5
- Pages
- 729-737
- ISSN
- 1044-3983 (ISSN-L)
- PMC
- PMC4162343
- OpenAlex
- W1989279315
- Cited by
- 291
- References
- 52
- Citations by year
- 2026: 15 · 2025: 29 · 2024: 25 · 2023: 21 · 2022: 29 · 2021: 30 · 2020: 32 · 2019: 22 · 2018: 27 · 2017: 20 · 2016: 20 · 2015: 17 · 2014: 4
- FWCI
- 21.23
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Epidemiology (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Funders
- United States Agency for International Development
- Wellcome Trust (097410)
- National Institutes of Health (5r01mh083539-04)
- National Institute of Mental Health (R01-MH083539, 1R01MH083539-01)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD058482)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Health Sciences › Medicine › Infectious Diseases › HIV/AIDS Research and Interventions
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- regression discontinuity design, causal inference, treatment effect estimation, HIV, antiretroviral therapy, CD4 count, survival analysis, nonlinear models, mortality
- MeSH terms
- Data Interpretation, Statistical, Drug Administration Schedule, Humans, Regression Analysis, Models, Statistical, Cohort Studies, Epidemiologic Research Design, HIV Infections, Causality, Treatment Outcome, CD4 Lymphocyte Count, Anti-HIV Agents
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