  We present methods for estimating loss-based measures of the performance of a
prediction model in a target population that differs from the source population
in which the model was developed, in settings where outcome and covariate data
are available from the source population but only covariate data are available
on a simple random sample from the target population. Prior work adjusting for
differences between the two populations has used various weighting estimators
with inverse odds or density ratio weights. Here, we develop more robust
estimators for the target population risk (expected loss) that can be used with
data-adaptive (e.g., machine learning-based) estimation of nuisance parameters.
We examine the large-sample properties of the estimators and evaluate finite
sample performance in simulations. Last, we apply the methods to data from lung
cancer screening using nationally representative data from the National Health
and Nutrition Examination Survey (NHANES) and extend our methods to account for
the complex survey design of the NHANES.
