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Validation of a Risk-Prediction Model in the Presence of Outcome Misclassification
Runjia Zou1, Brian D Williamson1,2,3, Susan M Shortreed1,2
1Department of Biostatistics, University of Washington, Seattle, Washington, USA.
Statistics in Medicine
|April 3, 2026
Summary
Outcome misclassification in electronic health records (EHRs) can lead to inaccurate performance estimates for clinical prediction models. This study introduces a method using chart review data to correct these errors, improving model evaluation accuracy.
Area of Science:
- Health Informatics
- Biostatistics
- Clinical Epidemiology
Background:
- Electronic health records (EHRs) are valuable for clinical prediction models.
- Measurement error and outcome misclassification in EHR data can compromise model evaluation.
- Accurate assessment of prediction model performance is crucial for clinical decision-making.
Purpose of the Study:
- To develop and validate a method for adjusting prediction model performance estimates when outcome data are misclassified.
- To provide accurate estimates of key performance metrics (TPR, FPR, PPV, NPV, AUC) despite outcome misclassification.
- To compare the proposed method's accuracy and precision against traditional evaluation methods.
Main Methods:
- Leveraging a smaller chart review sample with gold-standard outcome data.
- Deriving formulae to adjust performance metrics for various outcome misclassification scenarios (independent, dependent, unidirectional, bidirectional).
- Conducting simulation studies to compare bias and confidence interval coverage of proposed vs. unadjusted methods.
Main Results:
- The proposed method demonstrated good accuracy and improved precision in performance estimates across all examined misclassification scenarios.
- Adjusting for outcome misclassification yielded more reliable estimates of prediction model performance.
- Unadjusted estimates using misclassified outcomes were shown to be unreliable.
Conclusions:
- Outcome misclassification is a critical factor that must be addressed when evaluating clinical prediction models.
- Accurate model evaluation, accounting for misclassification, is essential for informed decisions on clinical implementation.
- The proposed method offers a robust approach to enhance the reliability of prediction model validation.
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