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Bayesian regression discontinuity designs: incorporating clinical knowledge in the causal analysis of primary care data

Author

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  • Geneletti, Sara
  • O'Keeffe, Aidan G.
  • Sharples, Linda D.
  • Richardson, Sylvia
  • Baio, Gianluca
Abstract
The regression discontinuity (RD) design is a quasi-experimental design that estimates the causal effects of a treatment by exploiting naturally occurring treatment rules. It can be applied in any context where a particular treatment or intervention is administered according to a pre-specified rule linked to a continuous variable. Such thresholds are common in primary care drug prescription where the RD design can be used to estimate the causal effect of medication in the general population. Such results can then be contrasted to those obtained from randomised controlled trials (RCTs) and inform prescription policy and guidelines based on a more realistic and less expensive context. In this paper, we focus on statins, a class of cholesterol-lowering drugs, however, the methodology can be applied to many other drugs provided these are prescribed in accordance to pre-determined guidelines. Current guidelines in the UK state that statins should be prescribed to patients with 10-year cardiovascular disease risk scores in excess of 20%. If we consider patients whose risk scores are close to the 20% risk score threshold, we find that there is an element of random variation in both the risk score itself and its measurement. We can therefore consider the threshold as a randomising device that assigns statin prescription to individuals just above the threshold and withholds it from those just below. Thus, we are effectively replicating the conditions of an RCT in the area around the threshold, removing or at least mitigating confounding. We frame the RD design in the language of conditional independence, which clarifies the assumptions necessary to apply an RD design to data, and which makes the links with instrumental variables clear. We also have context-specific knowledge about the expected sizes of the effects of statin prescription and are thus able to incorporate this into Bayesian models by formulating informative priors on our causal parameters. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Suggested Citation

  • Geneletti, Sara & O'Keeffe, Aidan G. & Sharples, Linda D. & Richardson, Sylvia & Baio, Gianluca, 2015. "Bayesian regression discontinuity designs: incorporating clinical knowledge in the causal analysis of primary care data," LSE Research Online Documents on Economics 65600, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:65600
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    References listed on IDEAS

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    1. Wilbert Van Der Klaauw, 2008. "Regression–Discontinuity Analysis: A Survey of Recent Developments in Economics," LABOUR, CEIS, vol. 22(2), pages 219-245, June.
    2. Finkelstein, M.O. & Levin, B. & Robbins, H., 1996. "Clinical and prophylactic trials with assured new treatment for those at greater risk: I. A design proposal," American Journal of Public Health, American Public Health Association, vol. 86(5), pages 691-695.
    3. Wilbert van der Klaauw, 2002. "Estimating the Effect of Financial Aid Offers on College Enrollment: A Regression-Discontinuity Approach," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 43(4), pages 1249-1287, November.
    4. Finkelstein, M.O. & Levin, B. & Robbins, H., 1996. "Clinical and prophylactic trials with assured new treatment for those at greater risk: II. Examples," American Journal of Public Health, American Public Health Association, vol. 86(5), pages 696-702.
    5. Hahn, Jinyong & Todd, Petra & Van der Klaauw, Wilbert, 2001. "Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design," Econometrica, Econometric Society, vol. 69(1), pages 201-209, January.
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    Cited by:

    1. Matias D. Cattaneo & Rocío Titiunik, 2022. "Regression Discontinuity Designs," Annual Review of Economics, Annual Reviews, vol. 14(1), pages 821-851, August.
    2. Matias D. Cattaneo & Luke Keele & Rocio Titiunik, 2021. "Covariate Adjustment in Regression Discontinuity Designs," Papers 2110.08410, arXiv.org, revised Aug 2022.
    3. Sharples, Linda D., 2018. "The role of statistics in the era of big data: Electronic health records for healthcare research," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 105-110.
    4. Aidan G. O'Keeffe & Gianluca Baio, 2016. "Approaches to the Estimation of the Local Average Treatment Effect in a Regression Discontinuity Design," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 978-995, December.
    5. Mariam O. Adeleke & Gianluca Baio & Aidan G. O'Keeffe, 2022. "Regression discontinuity designs for time‐to‐event outcomes: An approach using accelerated failure time models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1216-1246, July.
    6. Ximing Wu, 2021. "Hierarchical Gaussian Process Models for Regression Discontinuity/Kink under Sharp and Fuzzy Designs," Papers 2110.00921, arXiv.org, revised Feb 2022.

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    More about this item

    Keywords

    regression discontinuity design; causal inference; local average treatment effect; informative priors; causal inference; local average treatment effects; informative priors;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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