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Bayesian modelling for binary outcomes in the regression discontinuity design

Geneletti, S; Ricciardi, F; O'Keeffe, AG; Baio, G; (2019) Bayesian modelling for binary outcomes in the regression discontinuity design. Journal of the Royal Statistical Society: Series A (Statistics in Society) , 182 (3) pp. 983-1002. 10.1111/rssa.12440. Green open access

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Abstract

The regression discontinuity (RD) design is a quasi‐experimental design which emulates a randomized study by exploiting situations where treatment is assigned according to a continuous variable as is common in many drug treatment guidelines. The RD design literature focuses principally on continuous outcomes. We exploit the link between the RD design and instrumental variables to obtain an estimate for the causal risk ratio for the treated when the outcome is binary. Occasionally this risk ratio for the treated estimator can give negative lower confidence bounds. In the Bayesian framework we impose prior constraints that prevent this from happening. This is novel and cannot be easily reproduced in a frequentist framework. We compare our estimators with those based on estimating equation and generalized methods‐of‐moments methods. On the basis of extensive simulations our methods compare favourably with both methods and we apply our method to a real example to estimate the effect of statins on the probability of low density lipoprotein cholesterol levels reaching recommended levels.

Type: Article
Title: Bayesian modelling for binary outcomes in the regression discontinuity design
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/rssa.12440
Publisher version: https://doi.org/10.1111/rssa.12440
Language: English
Additional information: Copyright © 2019 The Authors Journal of the Royal Statistical Society: Series A (Statistics in Society) Published by John Wiley & Sons Ltd on behalf of Royal Statistical Society. This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: Bayesian inference, Binary outcomes, Causal inference, Instrumental variables, Prior constraints
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/1506030
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