  Due to label scarcity and covariate shift happening frequently in real-world
studies, transfer learning has become an essential technique to train models
generalizable to some target populations using existing labeled source data.
Most existing transfer learning research has been focused on model estimation,
while there is a paucity of literature on transfer inference for model accuracy
despite its importance. We propose a novel $\mathbf{D}$oubly $\mathbf{R}$obust
$\mathbf{A}$ugmented $\mathbf{M}$odel $\mathbf{A}$ccuracy $\mathbf{T}$ransfer
$\mathbf{I}$nferen$\mathbf{C}$e (DRAMATIC) method for point and interval
estimation of commonly used classification performance measures in an unlabeled
target population using labeled source data. Specifically, DRAMATIC derives and
evaluates the risk model for a binary response $Y$ against some low dimensional
predictors $\mathbf{A}$ on the target population, leveraging $Y$ from source
data only and high dimensional adjustment features $\mathbf{X}$ from both the
source and target data. The proposed estimators are doubly robust in the sense
that they are $n^{1/2}$ consistent when at least one model is correctly
specified and certain model sparsity assumptions hold. Simulation results
demonstrate that the point estimation have negligible bias and the confidence
intervals derived by DRAMATIC attain satisfactory empirical coverage levels. We
further illustrate the utility of our method to transfer the genetic risk
prediction model and its accuracy evaluation for type II diabetes across two
patient cohorts in Mass General Brigham (MGB) collected using different
sampling mechanisms and at different time points.
