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Stat Colloquium [In-person]: Dr. Soutik Ghosal

University of Virginia


Mathematics/Psychology : 401

Date & Time

April 14, 2023, 11:00 am12:00 pm


Title: Importance of covariate adjustment in the ROC analysis

Abstract: The receiver operating characteristics (ROC) curve is a handy graphical tool to assess the diagnostic accuracy of biomarkers. The ROC curve further allows a summary of the performance of a biomarker through the area under its curve (AUC) which is used as an overall assessment of the performance and is one of the most useful diagnostic accuracy metrics. While the comprehensive evaluation is practical, the performance of the biomarker in diagnosing diseases can be impacted by relevant covariate information. For example, the estimated fetal weight (EFW) is an ultrasound biomarker for predicting birthweight-related adverse outcomes such as large-for-gestational-age (LGA) or small-for-gestational-age (SGA). However, it would be implausible to assume a uniform performance of EFW across the entire maternal population. Significant covariates such as maternal BMI, maternal race, or various other maternal or neonatal risk factors could potentially impact the diagnostic accuracy of EFW. The adjustment of these significant covariates on the diagnostic accuracy is of utmost importance as the classification of any future data can be impacted by this.

However, most of the well-known and conventional methods of estimating ROC curves don’t take the covariate information into account, or the ones that do, assess the covariate impact indirectly. In this talk, I will primarily focus on a particular framework for modeling the ROC curves that use placement value (PV). PV can be defined as the standardization of the diseased biomarker score with respect to the healthy biomarker distribution, and interestingly, it can be shown that the CDF of the PV is the ROC curve. Several PV-based ROC methodologies have been proposed by exploiting this relationship, and this framework seamlessly takes the covariate information into the model to assess their impact on diagnostic accuracy. Apart from the covariate, the PV-based framework can incorporate constraints whenever necessary. In the talk, I will present some of my recent and ongoing work based on this framework and apply them in a few NICHD-conducted studies.