title: 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.
authors.affiliation: Institute of Clinical Psychology and Psychotherapy, Technische Universitaet Dresden, Chemnitzer Strasse 46, 01187 Dresden, Germany. klotsche@psychologie.tu-dresden.de
authors.forename: Jens
authors.initials: J
authors.lastname: Klotsche
authors.forename: Dietmar
authors.initials: D
authors.lastname: Ferger
authors.forename: David
authors.initials: D
authors.lastname: Leistner
authors.forename: Lars
authors.initials: L
authors.lastname: Pieper
authors.forename: Andreas M
authors.initials: AM
authors.lastname: Zeiher
authors.forename: Hans-Ulrich
authors.initials: HU
authors.lastname: Wittchen
authors.forename: Juergen
authors.initials: J
authors.lastname: Rehm
mesh_terms: D002318:Cardiovascular Diseases / Q000453:epidemiology / Q000378:metabolism; D003920:Diabetes Mellitus / Q000453:epidemiology / Q000378:metabolism; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D020097:Natriuretic Peptide, Brain / Q000378:metabolism; D010446:Peptide Fragments / Q000378:metabolism; D011237:Predictive Value of Tests; D012044:Regression Analysis; D018570:Risk Assessment
chemical_list: D010446:Peptide Fragments; C109794:pro-brain natriuretic peptide (1-76); D020097:Natriuretic Peptide, Brain