How to Interpret AUC ROC in Public Health Studies?
When interpreting AUC ROC values in public health studies, consider the following:
Context-Specific Thresholds: The optimal threshold for a test may vary depending on the disease prevalence and the consequences of false positives and false negatives. Comparison Across Models: When comparing multiple models or tests, a higher AUC often indicates a superior model. However, it is essential to consider other metrics like precision, recall, and the F1 score. Clinical Relevance: Beyond statistical significance, the clinical relevance of the test or model should be considered, especially in terms of improving patient outcomes and public health.
