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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Biometrical Journalarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Biometrical Journal
Article . 2012 . Peer-reviewed
License: Wiley Online Library User Agreement
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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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Article . 2012
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Binary regression: Total gain in positive and negative predictive values

Binary regression: total gain in positive and negative predictive values
Authors: Klotsche, Jens; Ferger, Dietmar; Leistner, David; Pieper, Lars; Zeiher, Andreas M.; Wittchen, Hans-Ulrich; Rehm, Juergen;

Binary regression: Total gain in positive and negative predictive values

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.

Keywords

Male, Models, Statistical, Computational problems in statistics, Middle Aged, Risk Assessment, Peptide Fragments, Applications of statistics to biology and medical sciences; meta analysis, Linear inference, regression, Medical applications (general), Cardiovascular Diseases, Predictive Value of Tests, Natriuretic Peptide, Brain, positive predictive value, Diabetes Mellitus, Humans, Regression Analysis, Female

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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