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

Robust Estimation of Loss-Based Measures of Model Performance under Covariate Shift