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).
However, because existing clinical studies with limited sample sizes often
suffer from selection bias issues, 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, building on the biobank and
electronic health record data in the Partner Health System, we introduce a new
data analysis pipeline from a biological perspective and a novel statistical
methodology that address the limitations in existing studies to: (i)
systematically examine heterogeneous treatment effects of stain use on T2D
risk, (ii) uncover which patient subgroup is most vulnerable to T2D after
taking statins, and (iii) assess the replicability and statistical significance
of the most vulnerable subgroup via bootstrap calibration. Our proposed
bootstrap calibration approach delivers asymptotically sharp confidence
intervals and debiased estimates for the treatment effect of the most
vulnerable subgroup in the presence of possibly high-dimensional covariates. By
implementing our proposed approach, we find that females with high T2D genetic
risk at baseline are indeed at high risk of developing T2D due to statin use,
which provides evidences to support future clinical decisions with respect to
statin use.Comment: 31 pages, 2 figures, 6 table