title: Survival model predictive accuracy and ROC curves.
abstract: The predictive accuracy of a survival model can be summarized using extensions of the proportion of variation explained by the model, or R2, commonly used for continuous response models, or using extensions of sensitivity and specificity, which are commonly used for binary response models. In this article we propose new time-dependent accuracy summaries based on time-specific versions of sensitivity and specificity calculated over risk sets. We connect the accuracy summaries to a previously proposed global concordance measure, which is a variant of Kendall's tau. In addition, we show how standard Cox regression output can be used to obtain estimates of time-dependent sensitivity and specificity, and time-dependent receiver operating characteristic (ROC) curves. Semiparametric estimation methods appropriate for both proportional and nonproportional hazards data are introduced, evaluated in simulations, and illustrated using two familiar survival data sets.
authors.affiliation: Department of Biostatistics, University of Washington, P.O. Box 357232, Seattle, Washington 98195-7232, USA. heagerty@u.washington.edu
authors.forename: Patrick J
authors.initials: PJ
authors.lastname: Heagerty
authors.forename: Yingye
authors.initials: Y
authors.lastname: Zheng
mesh_terms: D001699:Biometry; D006801:Humans; D008105:Liver Cirrhosis, Biliary / Q000188:drug therapy / Q000401:mortality; D008175:Lung Neoplasms / Q000401:mortality / Q000628:therapy; D015233:Models, Statistical; D010396:Penicillamine / Q000627:therapeutic use; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D012372:ROC Curve; D016032:Randomized Controlled Trials as Topic / Q000379:methods / Q000706:statistics & numerical data; D012306:Risk; D012680:Sensitivity and Specificity; D016019:Survival Analysis; D013997:Time Factors
chemical_list: D010396:Penicillamine