Regression Discontinuity Designs in Epidemiology: Causal Inference Without Randomized Trials
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CitationBor, Jacob, Ellen Moscoe, Portia Mutevedzi, Marie-Louise Newell, and Till Bärnighausen. 2014. “Regression Discontinuity Designs in Epidemiology: Causal Inference Without Randomized Trials.” Epidemiology (Cambridge, Mass.) 25 (5): 729-737. doi:10.1097/EDE.0000000000000138. http://dx.doi.org/10.1097/EDE.0000000000000138.
AbstractWhen 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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