Regression Discontinuity Designs in Epidemiology: A Practical Guide

Aidan G O’Keeffe,1 Irene Petersen2,3 1School of Mathematical Sciences, University of Nottingham, Nottingham, NG7 2RD, UK; 2Research Department of Primary Care and Population Health, University College London, London, NW3 2PF, UK; 3Department of Clinical Epidemiology, Aarhus University, Aarhus, DenmarkCorrespondence: Aidan G O’Keeffe, School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK, Tel +44 115 9514936, Email aidan.O’[email protected]: Quasi-experimental approaches are used routinely in clinical epidemiological research to enable causal treatment/intervention effect estimation from observational data. One such approach is the regression discontinuity design (RDD): a method to estimate a causal effect in situations when a treatment or intervention is assigned to individuals according to an externally defined decision rule, based on a continuous, individual-level “assignment variable”. RDDs were developed originally for use in econometrics but their use in clinical epidemiology is increasing, particularly with the widening availability of electronic health records and the use of rule-based treatment/intervention decisions. In particular, an RDD can be a useful method to assess the effectiveness of clinical decision making. In this paper, we provide an overview of the RDD, describing the method and key assumptions that permit its use in observational clinical data. We outline the common continuity-based and local randomisation RDDs and demonstrate how these can be fitted in both R and Stata. A worked example is presented of an RDD to estimate the treatment effect of statins on low density lipoprotein (LDL) cholesterol level, when statins are prescribed according to a rule based on a cardiovascular disease risk score.Keywords: causal inference, observational data, quasi-experimental, regression discontinuity design