Abstract
Regression discontinuity design (RDD) is a quasi-experimental method intended for causal inference in observational settings. While RDD is gaining popularity in clinical studies, there are limited real-world studies examining the performance on estimating known trial casual effects. The goal of this paper is to estimate the effect of statins on myocardial infarction (MI) using RDD and compare with propensity score matching and Cox regression. For the RDD, we leveraged a 2008 UK guideline that recommends statins if a patient’s 10-year cardiovascular disease (CVD) risk score > 20%. We used UK electronic health record data from the Health Improvement Network on 49,242 patients aged 65 + in 2008–2011 (baseline) without a history of CVD and no statin use in the two years prior to the CVD risk score assessment. Both the regression discontinuity (n = 19,432) and the propensity score matched populations (n = 24,814) demonstrated good balance of confounders. Using RDD, the adjusted point estimate for statins on MI was in the protective direction and similar to the statin effect observed in clinical trials, although the confidence interval included the null (HR = 0.8, 95% CI 0.4, 1.4). Conversely, the adjusted estimates using propensity score matching and Cox regression remained in the harmful direction: HR = 2.42 (95% CI 1.96, 2.99) and 2.51 (2.12, 2.97). RDD appeared superior to other methods in replicating the known protective effect of statins with MI, although precision was poor. Our findings suggest that, when used appropriately, RDD can expand the scope of clinical investigations aimed at causal inference by leveraging treatment rules from everyday clinical practice.
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THIN data cannot be shared publicly because of constraints dictated by the study’s ethics approval and Institutional review board restrictions. The IQVIA Medical Research Data, incorporating The Health Improvement Network (THIN), a Cegedim database of electronic health records. The lead and senior authors affirm that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.
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Acknowledgements
We thank all members and participants of The Health Improvement Network (THIN). The THIN team includes study coordinators, research scientists, statisticians, data managers, data entry staff, health providers, and administrative assistants, who make this de-identified and anonymized data source possible.
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This work was funded by R56-AG061177 from the National Institute on Aging.
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AZ, MO, and SC designed and conceptualized the study. AZhang and AT curated the data. AZhang, NJ, and AT conducted the analysis. SC provided statistical and methodological support for the RDD analysis. MO and AZ wrote the original draft of the manuscript. All authors discussed results and commented on the manuscript. AZ, SC and MO supervised the project and acquired funding. All authors accept responsibility to submit for publication. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.
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Odden, M.C., Zhang, A., Jawadekar, N. et al. Regression discontinuity design to evaluate the effect of statins on myocardial infarction in electronic health records. Eur J Epidemiol 38, 393–402 (2023). https://doi.org/10.1007/s10654-023-00982-w
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DOI: https://doi.org/10.1007/s10654-023-00982-w