Regression Discontinuity Designs in Epidemiology: Causal Inference Without Randomized Trials

Bor, Jacoba,b,c; Moscoe, Ellenc; Mutevedzi, Portiab; Newell, Marie-Louiseb,d; Bärnighausen, Tillb,c

doi: 10.1097/EDE.0000000000000138
Methods

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.

From the aDepartment of Global Health, Boston University School of Public Health, Boston, MA; bAfrica Centre for Health and Population Studies, Somkhele, South Africa; cDepartment of Global Health and Population, Harvard School of Public Health, Boston, MA; and dFaculty of Medicine, University of Southampton, Southampton, United Kingdom.

Submitted 24 July 2013; accepted 07 February 2014.

This research was made possible with funding from the Wellcome Trust (Africa Centre for Health and Population Studies); National Institutes of Health grants R01 HD058482-01 and 1R01MH083539-01 (T.B., M.L.N.); the Rush Foundation (J.B., T.B.); Harvard Center for Population and Development Studies (J.B.); and US Agency for International Development (USAID) Cooperative Agreement AID 674-A-12-00029 (J.B.). The contents are the responsibility of the authors and do not necessarily reflect the views of any of the funders or the US Government.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com). This content is not peer-reviewed or copy-edited; it is the sole responsibility of the authors.

Editors' note: A commentary on this article appears on page 738.

Correspondence: Jacob Bor, 801 Massachusetts Avenue, Boston, MA 02118. E-mail: jbor@bu.edu. +1 617 414 1444

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2014 by Lippincott Williams & Wilkins, Inc