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We consider the estimation of measures of model performance in a target
population when covariate and outcome data are available on a sample from some
source population and covariate data, but not outcome data, are available on a
simple random sample from the target population. When outcome data are not
available from the target population, identification of measures of model
performance is possible under an untestable assumption that the outcome and
population (source or target population) are independent conditional on
covariates. In practice, this assumption is uncertain and, in some cases,
controversial. Therefore, sensitivity analysis may be useful for examining the
impact of assumption violations on inferences about model performance. Here, we
propose an exponential tilt sensitivity analysis model and develop statistical
methods to determine how sensitive measures of model performance are to
violations of the assumption of conditional independence between outcome and
population. We provide identification results and estimators for the risk in
the target population, examine the large-sample properties of the estimators,
and apply the estimators to data on individuals with stable ischemic heart
disease.