title: Quantifying the predictive performance of prognostic models for censored survival data with time-dependent covariates.
abstract: Prognostic models in survival analysis typically aim to describe the association between patient covariates and future outcomes. More recently, efforts have been made to include covariate information that is updated over time. However, there exists as yet no standard approach to assess the predictive accuracy of such updated predictions. In this article, proposals from the literature are discussed and a conditional loss function approach is suggested, illustrated by a publicly available data set.
authors.affiliation: Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Germany. rs@imbi.uni-freiburg.de
authors.forename: R
authors.initials: R
authors.lastname: Schoop
authors.forename: E
authors.initials: E
authors.lastname: Graf
authors.forename: M
authors.initials: M
authors.lastname: Schumacher
mesh_terms: D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015233:Models, Statistical; D016016:Proportional Hazards Models; D012107:Research Design; D012680:Sensitivity and Specificity; D016019:Survival Analysis; D015996:Survival Rate