Binary regression: Total gain in positive and negative predictive values
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
Models that predict disease incidence or disease recurrence are attractive for clinicians as well as for patients. The usefulness of a risk prediction model is linked to the two questions whether the observed outcome is confirmed by the prediction and whether the risk prediction is accurate in predicting the future outcome, respectively. The first phrasing of the question is linked to considering sensitivity and specificity and the latter to the positive and negative predictive values. We present the measures of standardized total gain in positive and negative predictive values dealing with the performance or accuracy of the prediction model for a binary outcome. Both measures provide a useful tool for assessing the performance or accuracy of a set of predictor variables for the prediction of a binary outcome. This concept is a tool for evaluating the optimal prediction model in future research.
Details
Original language | English |
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Pages (from-to) | 808-823 |
Number of pages | 16 |
Journal | Biometrical Journal |
Volume | 54 |
Issue number | 6 |
Publication status | Published - Nov 2012 |
Peer-reviewed | Yes |
External IDs
PubMed | 23044820 |
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Keywords
ASJC Scopus subject areas
Keywords
- Binary regression, Negative predictive value, Positive predictive value, Total gain