Quantification of the added usefulness of new measures in risk prediction has traditionally relied upon significance tests from regression models and increases in the C-statistic. However, significant model predictors often cause only minor increases in the C-statistic, suggesting limited utility of the new measures in improving risk prediction. More recently, other discriminators have gained popularity amongst researchers. The Integrated Discrimination Improvement index (IDI) measures the difference between the change in the mean predicted risk of an event occurring for those who had the event and the change for those who didn’t have the event. The Net Reclassification Improvement index (NRI) quantifies the percentage of subjects correctly re-classified in terms of risk.
|Publication status||Published - 2011|
|Event||4rd Australian and New Zealand Stata Users' Group Meeting - University of Notre Dame , Fremantle, Australia|
Duration: 17 Sep 2011 → 21 Sep 2011
|Conference||4rd Australian and New Zealand Stata Users' Group Meeting|
|Period||17/09/11 → 21/09/11|
- Concordance statistic (C-statistic)
- probability interpretation
- hospital stay
Woodman, R., Thompson, C. H., Kim, S., & Hakendorf, P. (2011). Comparison of the C-statistic with new model discriminators in the prediction of long versus short hospital stay. Abstract from 4rd Australian and New Zealand Stata Users' Group Meeting, Fremantle, Australia.