Cambridge University

Regression discontinuity design to estimate the causal effect of statins on myocardial infarction in electronic health records

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 of this approach on estimation of known trial-established casual effects. The goal of this study is to replicate the known protective association of statins with myocardial infarction (MI) using RDD and propensity score matching in adults in the UK using electronic health record data from the Health Improvement Network (THIN). Our findings suggest that, when used appropriately, RDD can expand the scope of clinical investigations aimed at causal inference. The long-term goal of this validation work is to use RDD to provide more rigorous evidence on statin, or other treatment, effects on health outcomes, such as dementia, for which the evidence remains inconclusive.

Speakers:
Dr Adina Zeki Al Hazzouri - Epidemiologist, Assistant Professor at the Mailman School of Public Health, Columbia University
Dr Sebastian Calonico - Economist, Assistant Professor in Health Policy & Management at the Mailman School of Public Health, Columbia University.

Register for this free webinar, being held on July 28th, 2022, at 14:00 BST, here.

Event date: 
Thursday, July 28, 2022

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