Covariate adjustment in continuous biomarker assessment

Abstract

Continuous biomarkers are common for disease screening and diagnosis. To reach a dichotomous clinical decision, a threshold would be imposed to distinguish subjects with disease from nondiseased individuals. Among various performance metrics, specificity at a controlled sensitivity level (or vice versa) is often desirable because it directly targets the clinical utility of the intended clinical test. Meanwhile, covariates, such as age, race, as well as sample collection conditions, could impact the biomarker distribution and may also confound the association between biomarker and disease status. Therefore, covariate adjustment is important in such biomarker evaluation. Most existing covariate adjustment methods do not specifically target the desired sensitivity/specificity level, but rather do so for the entire biomarker distribution. As such, they might be more prone to model misspecification. In this paper, we suggest a parsimonious quantile regression model for the diseased population, only locally at the controlled sensitivity level, and assess specificity with covariate-specific control of the sensitivity. Variance estimates are obtained from a sample-based approach and bootstrap. Furthermore, our proposed local model extends readily to a global one for covariate adjustment for the receiver operating characteristic (ROC) curve over the sensitivity continuum. We demonstrate computational efficiency of this proposed method and restore the inherent monotonicity in the estimated covariate-adjusted ROC curve. The asymptotic properties of the proposed estimators are established. Simulation studies show favorable performance of the proposal. Finally, we illustrate our method in biomarker evaluation for aggressive prostate cancer.

Covariate‐specific evaluation of continuous biomarker

“…It generalizes the PDF methods by targeting the particular controlled sensitivity level of interest only or accommodating potential varying covariate effects at different sensitivity levels. At the same time, the proposed approach extends our previous work 17 to provide covariate-specific biomarker assessment. We first model the covariate effects among the diseased population by quantile regression, locally at a sensitivity of interest.…”

“…The monotonicity-respecting restoration method of Huang 21 may be used, targeting either β(⋅) and γ(⋅) or the estimated covariate-specific ROC curves. In our related work, 17 the regression-based and the ROC-based monotonization method demonstrate comparable accuracy in the estimations, but ROC-based method has better computational performance. In this work, we shall adopt ROC-based monotonization.…”

“…It extends our previous work on pooled evaluation with covariate-adjusted threshold. 17 Although the modeling for the diseased population under quantile regression framework is similar to Li et al, 17 the covariate-specific evaluation requires further modeling on the controls, which substantially increases the model complexity.…”

“…Previous works using quantile regression have adopted similar assumptions. 17,19 Theorem 1. Suppose that the quantile regression model for the cases given in (1) and the logistic regression model for the controls given in (2) hold locally at the controlled sensitivity level 𝜌 0 , along with Conditions 1, 2, 3, and 4a.…”

Systemic Neutrophil Gelatinase-Associated Lipocalin Alterations in Chronic Pancreatitis: A Multicenter, Cross-Sectional Study

“…Interestingly, IL-17 can promote expression of NGAL ( 35 ). Combining NGAL with other potential mechanistic markers such as IL-17 may improve accuracy for CP compared with controls or AP/RAP ( 36 , 37 ). Urine NGAL is currently being investigated as a biomarker for acute kidney injury ( 38 , 39 ), diabetic nephropathy, or diabetic kidney disease ( 40 , 41 ), and others are working on ways to test plasma NGAL using automated assays ( 42 ).…”