  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.
