Semiparametric estimation of the covariate-specific ROC curve in presence of ignorable verification bias.

Abstract:

:Covariate-specific receiver operating characteristic (ROC) curves are often used to evaluate the classification accuracy of a medical diagnostic test or a biomarker, when the accuracy of the test is associated with certain covariates. In many large-scale screening tests, the gold standard is subject to missingness due to high cost or harmfulness to the patient. In this article, we propose a semiparametric estimation of the covariate-specific ROC curves with a partial missing gold standard. A location-scale model is constructed for the test result to model the covariates' effect, but the residual distributions are left unspecified. Thus the baseline and link functions of the ROC curve both have flexible shapes. With the gold standard missing at random (MAR) assumption, we consider weighted estimating equations for the location-scale parameters, and weighted kernel estimating equations for the residual distributions. Three ROC curve estimators are proposed and compared, namely, imputation-based, inverse probability weighted, and doubly robust estimators. We derive the asymptotic normality of the estimated ROC curve, as well as the analytical form of the standard error estimator. The proposed method is motivated and applied to the data in an Alzheimer's disease research.

journal_name

Biometrics

journal_title

Biometrics

authors

Liu D,Zhou XH

doi

10.1111/j.1541-0420.2011.01562.x

subject

Has Abstract

pub_date

2011-09-01 00:00:00

pages

906-16

issue

3

eissn

0006-341X

issn

1541-0420

journal_volume

67

pub_type

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