Binary regression: Total gain in positive and negative predictive values

Research output: Contribution to journalResearch articleContributedpeer-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 languageEnglish
Pages (from-to)808-823
Number of pages16
JournalBiometrical Journal
Volume54
Issue number6
Publication statusPublished - Nov 2012
Peer-reviewedYes

External IDs

PubMed 23044820

Keywords

Keywords

  • Binary regression, Negative predictive value, Positive predictive value, Total gain