title: Explained variation and predictive accuracy in general parametric statistical models: the role of model misspecification.
abstract: When studying a regression model measures of explained variation are used to assess the degree to which the covariates determine the outcome of interest. Measures of predictive accuracy are used to assess the accuracy of the predictions based on the covariates and the regression model. We give a detailed and general introduction to the two measures and the estimation procedures. The framework we set up allows for a study of the effect of misspecification on the quantities estimated. We also introduce a generalization to survival analysis.
authors.affiliation: Department of Biostatistics, University of Copenhagen, Blegdamsvej 3, DK-2200 Copenhagen N, Denmark.
authors.forename: Susanne
authors.initials: S
authors.lastname: Rosthøj
authors.forename: Niels
authors.initials: N
authors.lastname: Keiding
mesh_terms: D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D011237:Predictive Value of Tests; D012044:Regression Analysis; D012680:Sensitivity and Specificity; D016019:Survival Analysis