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 statistical significance of the most vulnerable subgroup via a bootstrap calibration procedure. Our proposed approach delivers asymptotically sharp confidence intervals and debiased estimate for the treatment effect of the most vulnerable subgroup in the presence of high-dimensional covariates. With our proposed approach, we find that females with high T2D genetic risk are at the highest risk of developing T2D due to statin usage.
翻译:越来越多的人担心,使用死 Statins是治疗冠心动动脉病的最常用处方药物之一,这种使用与新发二型二型糖尿病(T2D)的风险增加有潜在联系。然而,迄今为止,没有确凿证据支持在服用死病后是否和哪种人口在开发T2D方面确实脆弱。在本案例研究中,利用伙伴保健系统的生物库和电子健康记录数据,我们引入了新的数据分析管道和新颖的统计方法,通过下列办法解决现有的局限性:(一) 设计一个严格的因果框架,系统地审查在观察数据中使用死病对T2D风险的因果效应;(二) 发现哪个病人分组在服用死病后最易受开发T2D的伤害,以及(三) 通过靴杆校准程序评估最脆弱分组的可复制性和统计意义。我们提议的办法提供了一种微调的互信间隔和对最脆弱分组在高位变量中的治疗效果的偏差估计。我们提出的办法发现,在T2遗传风险最高的情况下,女性的遗传风险最高比例为TD。