Net reclassification improvement

Net reclassification improvement (NRI) is an index that attempts to quantify how well a new model reclassifies subjects - either appropriately or inappropriately - as compared to an old model. While c-statistics or AUC has been the standard metric for quantifying improvements over the last few decades, several studies have analyzed the limitations of this metric including lack of clinical relevance and difficulty in interpretation of small magnitude changes. This limitation can be best seen in the example of HDL and Framingham Risk Score (FRS). When a models with and without HDL were analyzed with AUC regarding effect of HDL of modifying FRS, HDL was found not to have a statistical significant effect. However, when analyzed in terms of outcomes, HDL was found to be a significant predictor of heart disease and thus should affect FRS. To overcome this limitation the concept of reclassification, that is how well a new model correctly reclassifies cases, was introduced through the metric of NRI. Net reclassification improvement (NRI) is an index that attempts to quantify how well a new model reclassifies subjects - either appropriately or inappropriately - as compared to an old model. While c-statistics or AUC has been the standard metric for quantifying improvements over the last few decades, several studies have analyzed the limitations of this metric including lack of clinical relevance and difficulty in interpretation of small magnitude changes. This limitation can be best seen in the example of HDL and Framingham Risk Score (FRS). When a models with and without HDL were analyzed with AUC regarding effect of HDL of modifying FRS, HDL was found not to have a statistical significant effect. However, when analyzed in terms of outcomes, HDL was found to be a significant predictor of heart disease and thus should affect FRS. To overcome this limitation the concept of reclassification, that is how well a new model correctly reclassifies cases, was introduced through the metric of NRI.