Nan Hu
Receiver operating characteristic (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. Therefore it is appropriate to derive the corresponding ROC curves that changes as a function of time. Recently, the ROC analysis has been extended to the time-to-event outcome data, including the nonparametric propose approach by Heagerty et.al (2000), and semi-parametric approach by Heagerty and Zheng (2005). However, none of these approaches incorporate covariates, and cannot be used to estimate time-dependent ROC curves adjusted for covariates. More recently, Song and Zhou (2008) proposed a semi-parametric regression approach for covariate-specific ROC curve for time-to-event outcome, but their method has strong assumption of proportionality in hazard. In this study, I propose a new semi-parametric method for estimating the time-dependent ROC curve based on non-parametric transformation model of event time. Since the transformation model neither assumes the distribution of error term, nor requires the specification the transformation function (other than the requirement of monotonicity), the proposed approach is more general and has a large extend of flexibility in model specification.
