Journal of Data Science

The receiver operating characteristics (ROC) curve has been widely used to evaluate the discrimination performance of biomarkers, but it has been criticized for overlooking their underlying distributions. In this paper, we propose a continuous version of the ROC curve that can assess not only the discrimination performance of biomarkers but also their continuity performance. Our method summarizes the biomarker values as conditional tail expectations at varying thresholds and compare them with true positive and false positive rates. The proposed method is particularly useful for an early phase of biomarker study that enrolls heterogeneous disease populations. Analysis of data from an ovarian cancer biomarker study illustrates the practical utility of the proposed method over the standard ROC curve analysis. The proposed methods are implemented in the R package varoc.

Time-to-event data analysis without a well-defined time origin commonly occurs in observational studies that retrospectively collect survival endpoints. For instance, after enrolling participants who have or have not received a specific treatment, an event status can be observed for all participants; however, the start date of treatment is only observable for the treatment group. The corresponding time origin does not exist for the control group, resulting in missing survival time data. Complete-case analysis is often considered the standard approach, but it disregards information from all participants in the control group and does not allow us to compare their survival distributions. To address this challenge, we propose a novel semiparametric proportional hazards model by regarding these missing time origins as nuisance parameters. We approximate the risk sets as cumulative normal distributions to deal with these nuisance parameters and develop estimation and inference procedures for our proposed estimator. We study the asymptotic properties of this model and conduct the simulation studies to validate its finite sample property. Analysis of data from a recent SARS-CoV-2 seroprevaluence study illustrates the applicability of our methods. The proposed methods are implemented in the R package coxphm.