Skip to main content

Research Repository

Advanced Search

Approaches to risk ratio estimation in a regression discontinuity design: Application to the prescription of statins for cholesterol reduction in UK primary care

Adeleke, Mariam O; O’Keeffe, Aidan G; Baio, Gianluca

Approaches to risk ratio estimation in a regression discontinuity design: Application to the prescription of statins for cholesterol reduction in UK primary care Thumbnail


Authors

Mariam O Adeleke

Gianluca Baio



Abstract

In recent years regression discontinuity designs have been used increasingly for the estimation of treatment effects in observational medical data where a rule-based decision to apply treatment is taken using a continuous assignment variable. Most regression discontinuity design applications have focused on effect estimation where the outcome of interest is continuous, with scenarios with binary outcomes receiving less attention, despite their ubiquity in medical studies. In this work, we develop an approach to estimation of the risk ratio in a fuzzy regression discontinuity design (where treatment is not always strictly applied according to the decision rule), derived using common regression discontinuity design assumptions. This method compares favourably to other risk ratio estimation approaches: the established Wald estimator and a risk ratio estimate from a multiplicative structural mean model, with promising results from extensive simulation studies. A demonstration and further comparison are made using a real example to evaluate the effect of statins (where a statin prescription is made based on a patient's 10-year cardiovascular disease risk score) on low-density lipoprotein cholesterol reduction in UK Primary Care.

Citation

Adeleke, M. O., O’Keeffe, A. G., & Baio, G. (2023). Approaches to risk ratio estimation in a regression discontinuity design: Application to the prescription of statins for cholesterol reduction in UK primary care. Statistical Methods in Medical Research, 32(10), 1994-2015. https://doi.org/10.1177/09622802231192958

Journal Article Type Article
Acceptance Date Jul 10, 2023
Online Publication Date Aug 17, 2023
Publication Date 2023-10
Deposit Date Jul 20, 2023
Publicly Available Date Aug 17, 2023
Journal Statistical Methods in Medical Research
Print ISSN 0962-2802
Electronic ISSN 1477-0334
Publisher SAGE Publications
Peer Reviewed Peer Reviewed
Volume 32
Issue 10
Pages 1994-2015
DOI https://doi.org/10.1177/09622802231192958
Keywords Binary outcome, local randomisation, observational data, regression discontinuity design, risk ratio
Public URL https://nottingham-repository.worktribe.com/output/23215138
Publisher URL https://journals.sagepub.com/doi/10.1177/09622802231192958

Files





You might also like



Downloadable Citations