Campus Units

Statistics

Document Type

Article

Publication Version

Published Version

Publication Date

10-2018

Journal or Book Title

Statistica Sinica

Volume

28

Issue

4

First Page

2149

Last Page

2166

DOI

10.5705/ss.202016.0315

Abstract

The Area Under the Receiving Operating Characteristic Curve (AUC) is frequently used for assessing the overall accuracy of a diagnostic marker. However, estimation of AUC relies on knowledge of the true outcomes of subjects: diseased or non-diseased. Because disease verification based on a gold standard is often expensive and/or invasive, only a limited number of patients are sent to verification at doctors discretion. Estimation of AUC is generally biased if only small verified samples are used and it is thus necessary to make corrections for such lack of information. Correction based on the ignorable missingness assumption (or missing at random) is also biased if the missing mechanism depends on the unknown disease outcome, which is called nonignorable missing. In this paper, we propose a propensity-score-adjustment method for estimating the AUC based on the instrumental variable assumption when the missingness of disease status is nonignorable. The new method makes parametric assumptions on the verification probability, and the probability of being diseased for verified samples rather than for the whole sample. The proposed parametric assumption on the observed sample is easier to be verified than the parametric assumption on the full sample. We establish the asymptotic properties of the proposed estimators. A simulation study was performed to compare the proposed method with existing methods. The proposed method is applied to an Alzheimers disease data collected by National Alzheimers Coordinating Center.

Comments

This article is published as Yu, Wenbao, Jae Kwang Kim, and Taesung Park. "Estimation of Area Under the ROC Curve under nonignorable verification bias." Statistica Sinica 28, no. 4 (2018): 2149. doi: 10.5705/ss.202016.0315. Posted with permission.

Copyright Owner

Institute of Statistical Science, Academia Sinica

Language

en

File Format

application/pdf

Available for download on Tuesday, October 01, 2019

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