Estimators for covariate-adjusted ROC curves with missing biomarkers values

TL;DR

This paper introduces three new estimators for covariate-adjusted ROC curves in the presence of missing biomarker data, utilizing inverse probability weighting and regression models, with proven consistency and demonstrated effectiveness.

Contribution

The paper proposes novel estimators for covariate-adjusted ROC curves handling missing biomarkers, combining inverse probability weighting and regression-based methods with theoretical consistency.

Findings

Estimators are consistent under mild conditions.

Numerical studies show good finite sample performance.

Application to real data demonstrates practical utility.

Abstract

In this paper, we present three estimators of the ROC curve when missing observations arise among the biomarkers. Two of the procedures assume that we have covariates that allow to estimate the propensity and the estimators are obtained using an inverse probability weighting method or a smoothed version of it. The other one assumes that the covariates are related to the biomarkers through a regression model which enables us to construct convolution--based estimators of the distribution and quantile functions. Consistency results are obtained under mild conditions. Through a numerical study we evaluate the finite sample performance of the different proposals. A real data set is also analysed.