Validation of a Risk-Prediction Model in the Presence of Outcome Misclassification.

Electronic health records (EHRs) provide a rich data source for building prediction models to improve the quality of care. However, EHR data are prone to measurement error, and outcomes used to evaluate prediction model performance may be misclassified. Comparing risk predictions to misclassified outcomes will result in unreliable estimates of a prediction model's performance. We propose a method that leverages a smaller chart review sample with gold-standard outcome measurements to adjust validation for outcome misclassification and provide more accurate prediction model evaluation. We derive formulae to estimate true positive rate (TPR), false positive rate (FPR), positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC) in the presence of outcome misclassification. Different scenarios of misclassification are explored, including when the misclassification is independent or dependent on features, and when misclassification is unidirectional (e.g., only missed diagnoses) or bidirectional. In simulation studies, we compare the bias and 95% confidence interval coverage of performance estimates obtained using our proposed method to those estimated with misclassified outcomes (without accounting for misclassification) or in the smaller chart review sample. Simulation results indicate that, across all misclassification scenarios examined, our proposed estimates have good accuracy and improved precision. Outcome misclassification should be considered when evaluating a prediction model's performance in order to accurately inform decision-making about whether and how to use a clinical prediction model.