Abstract
Biomarker evaluation is important for diagnosing clinical diseases. Covariate adjusted receiver operating characteristic (ROC) regression has been used to identify significant biomarker candidates. Here, we show that the statistical significance of a biomarker can be affected by its prevalence. We propose a novel method that incorporates covariate prevalence information in the ROC regression. This approach is based on covariate balancing propensity scores proposed by Imai and van Dyk. Our method produces higher AUC values, demonstrating improved discrimination ability compared to direct ROC regression or unadjusted ROC analysis; this method can be used to improve biomarker development and can be implemented by an artificial intelligence (AI) system. Extensive simulation studies and data from a thyroid cancer study illustrate the advantages of our approach.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Communications in Statistics - Simulation and Computation
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
