A Review of Time-Dependent ROC Curve for Evaluating the Prognosis Capacity of Biomarkers and Semiparametric Regression Methods

• Activity Number: 618
• Type: Topic Contributed
• Date/Time: Thursday, August 5, 2010 : 8:30 AM to 10:20 AM
• Sponsor: Health Policy Statistics Section
• Author(s): Nan Hu*+ and Xiao-Hua Zhou
• Companies: The University of Utah and University of Washington
• Keywords: ROC curve ; Sensitivity ; Specificity ; Semiparametric ; Time-dependent ; Regression

Abstract

ROC curves are commonly used for visualizing sensitivity and specificity of a continuous biomarker or diagnostic test result, Y , for a binary disease outcome D. In practice, however, many disease outcomes depend on time and it is appropriate to derive the corresponding time-dependent ROC curves. In this paper, we motivate the time-dependent ROC curve using examples and review the previous statistical methods. Then, two proposed semiparametric methods are outlined. The first method is proposed to use semiparametric regression approach to estimate the covariate-adjusted time-dependent ROC curves by modeling time-dependent sensitivities and false positive rates (FPRs). We call this the indirect time-dependent ROC regression method. The second semiparametric regression approach is a directly method for the time-dependent ROC curve. We call this approach the direct time-dependent ROC method.