Document Detail


Binary regression: Total gain in positive and negative predictive values.
MedLine Citation:
PMID:  23044820     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
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.
Authors:
Jens Klotsche; Dietmar Ferger; David Leistner; Lars Pieper; Andreas M Zeiher; Hans-Ulrich Wittchen; Juergen Rehm
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-10-9
Journal Detail:
Title:  Biometrical journal. Biometrische Zeitschrift     Volume:  -     ISSN:  1521-4036     ISO Abbreviation:  Biom J     Publication Date:  2012 Oct 
Date Detail:
Created Date:  2012-10-9     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7708048     Medline TA:  Biom J     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Affiliation:
Institute of Clinical Psychology and Psychotherapy, Technische Universitaet Dresden, Chemnitzer Straße 46, 01187 Dresden, Germany; Center of Clinical Epidemiology and Longitudinal Studies (CELOS), Technische Universitaet Dresden, Chemnitzer Straße 46, 01187 Dresden, Germany.
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