Assessing the Most Vulnerable Subgroup to Type II Diabetes Associated with Statin Usage: Evidence from Electronic Health Record Data
Xinzhou Guo, Waverly Wei, Molei Liu, Tianxi Cai, Chong Wu, Jingshen, Wang

TL;DR
This study develops a new analytical approach to identify which patient subgroups are most vulnerable to developing type II diabetes after statin use, revealing that females with high genetic risk are at greatest risk.
Contribution
The paper introduces a novel statistical methodology and causal framework to identify vulnerable subgroups for T2D post-statin use using electronic health record data.
Findings
Females with high T2D genetic risk are most vulnerable.
The approach provides sharp confidence intervals and debiased estimates.
The methodology improves causal inference in high-dimensional observational data.
Abstract
There have been increased concerns that the use of statins, one of the most commonly prescribed drugs for treating coronary artery disease, is potentially associated with the increased risk of new-onset type II diabetes (T2D). Nevertheless, to date, there is no robust evidence supporting as to whether and what kind of populations are indeed vulnerable for developing T2D after taking statins. In this case study, leveraging the biobank and electronic health record data in the Partner Health System, we introduce a new data analysis pipeline and a novel statistical methodology that address existing limitations by (i) designing a rigorous causal framework that systematically examines the causal effects of statin usage on T2D risk in observational data, (ii) uncovering which patient subgroup is most vulnerable for developing T2D after taking statins, and (iii) assessing the replicability and…
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Taxonomy
TopicsAdvanced Causal Inference Techniques · Genetic Associations and Epidemiology · Statistical Methods in Clinical Trials
