Mean score equation and instrumental variables: Another look at estimating the volume under the receiver operating characteristic surface when data are missing not at random

Stat ◽  
2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Duc Khanh To ◽  
Gianfranco Adimari ◽  
Monica Chiogna
Keyword(s):  
Receiver Operating Characteristic ◽  
Instrumental Variables ◽  
Operating Characteristic ◽  
Characteristic Surface ◽  
Missing Not At Random ◽  
Receiver Operating

Estimation of the volume under the receiver-operating characteristic surface adjusting for non-ignorable verification bias

2018 ◽  
Vol 27 (3) ◽  
pp. 715-739 ◽  
Author(s):  
Ying Zhang ◽  
Todd A Alonzo ◽  
Keyword(s):  
Receiver Operating Characteristic ◽  
Diagnostic Test ◽  
Operating Characteristic ◽  
Disease Status ◽  
Characteristic Surface ◽  
Finite Sample ◽  
Classification Problems ◽  
Verification Bias ◽  
Inverse Probability Weighted ◽  
Receiver Operating

The receiver-operating characteristic surface is frequently used for presenting the accuracy of a diagnostic test for three-category classification problems. One common problem that can complicate the estimation of the volume under receiver-operating characteristic surface is that not all subjects receive the verification of the true disease status. Estimation based only on data from subjects with verified disease status may be biased, which is referred to as verification bias. In this article, we propose new verification bias correction methods to estimate the volume under receiver-operating characteristic surface for a continuous diagnostic test. We assume the verification process is missing not at random, which means the missingness might be related to unobserved clinical characteristics. Three classes of estimators are proposed, namely, inverse probability weighted, imputation-based, and doubly robust estimators. A jackknife estimator of variance is derived for all the proposed volume under receiver-operating characteristic surface estimators. The finite sample properties of the new estimators are examined via simulation studies. We illustrate our methods with data collected from Alzheimer’s disease research.

Confidence intervals for differences between volumes under receiver operating characteristic surfaces (VUS) and generalized Youden indices (GYIs)

2017 ◽  
Vol 27 (3) ◽  
pp. 675-688 ◽  
Author(s):  
Jingjing Yin ◽  
Christos T Nakas ◽  
Lili Tian ◽  
Benjamin Reiser
Keyword(s):  
Cognitive Function ◽  
Receiver Operating Characteristic ◽  
Confidence Intervals ◽  
Operating Characteristic ◽  
Youden Index ◽  
Characteristic Surface ◽  
Classification Problems ◽  
New Methods ◽  
Using Data ◽  
Receiver Operating

This article explores both existing and new methods for the construction of confidence intervals for differences of indices of diagnostic accuracy of competing pairs of biomarkers in three-class classification problems and fills the methodological gaps for both parametric and non-parametric approaches in the receiver operating characteristic surface framework. The most widely used such indices are the volume under the receiver operating characteristic surface and the generalized Youden index. We describe implementation of all methods and offer insight regarding the appropriateness of their use through a large simulation study with different distributional and sample size scenarios. Methods are illustrated using data from the Alzheimer's Disease Neuroimaging Initiative study, where assessment of cognitive function naturally results in a three-class classification setting.

scholarly journals Bayesian bootstrap inference for the receiver operating characteristic surface

Stat ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. e211 ◽  
Author(s):  
Vanda Inácio de Carvalho ◽  
Miguel Carvalho ◽  
Adam Branscum
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Surface ◽  
Bayesian Bootstrap ◽  
Bootstrap Inference ◽  
Receiver Operating
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