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. 2014 Sep;25(5):729-37.
doi: 10.1097/EDE.0000000000000138.

Regression discontinuity designs in epidemiology: causal inference without randomized trials

Jacob Bor  1 , Ellen Moscoe, Portia Mutevedzi, Marie-Louise Newell, Till Bärnighausen
Affiliations
Free PMC article

Regression discontinuity designs in epidemiology: causal inference without randomized trials

Jacob Bor et al. Epidemiology. 2014 Sep.
Free PMC article

Abstract

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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Figures

FIGURE 1.
FIGURE 1.
Sharp regression discontinuity design. This figure shows the conditional expectation functions for each of the potential outcomes E[Yi(1) | Zi = z] and E[Yi(0) | Zi = z]. The solid lines show the conditional expectation function of the observed data, E[Yi | Zi = z].
FIGURE 2.
FIGURE 2.
Distribution of first CD4 counts in the HIV treatment and care program.
FIGURE 3.
FIGURE 3.
First CD4 count and ART initiation. Kaplan–Meier estimates of the probability that a patient initiated ART within 3 and 12 months of first CD4 count in the HIV treatment and care program.
FIGURE 4.
FIGURE 4.
First CD4 count and mortality hazard rate. Predicted hazards from the Table, model 2a are displayed as solid lines. Dashed line shows extrapolated prediction if all patients were treatment eligible at first CD4 count. Dots are hazards predicted for CD4 count bins of width 10 cells.
FIGURE 5.
FIGURE 5.
Potential applications of regression-discontinuity designs in epidemiology.
See this image and copyright information in PMC

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