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Binary regression: Total gain in positive and negative predictive values
Author(s) -
Klotsche Jens,
Ferger Dietmar,
Leistner David,
Pieper Lars,
Zeiher Andreas M.,
Wittchen HansUlrich,
Rehm Juergen
Publication year - 2012
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201100104
Subject(s) - outcome (game theory) , predictive modelling , statistics , predictive value , regression analysis , positive predicative value , regression , linear regression , mathematics , medicine , mathematical economics
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.