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 Comparative Assessment of Three Common Algorithms for Estimating the Variance of the Area under the Nonparametric Receiver Operating Characteristic Curve

2002 ◽  
Vol 2 (3) ◽  
pp. 280-289 ◽  
Author(s):  
Mario A. Cleves
Keyword(s):  
Receiver Operating Characteristic ◽  
Diagnostic Test ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Simulated Data ◽  
Small Samples ◽  
Binary Outcomes ◽  
Discriminatory Accuracy ◽  
Receiver Operating

The area under the receiver operating characteristic (ROC) curve is often used to summarize and compare the discriminatory accuracy of a diagnostic test or modality, and to evaluate the predictive power of statistical models for binary outcomes. Parametric maximum likelihood methods for fitting of the ROC curve provide direct estimates of the area under the ROC curve and its variance. Nonparametric methods, on the other hand, provide estimates of the area under the ROC curve, but do not directly estimate its variance. Three algorithms for computing the variance for the area under the nonparametric ROC curve are commonly used, although ambiguity exists about their behavior under diverse study conditions. Using simulated data, we found similar asymptotic performance between these algorithms when the diagnostic test produces results on a continuous scale, but found notable differences in small samples, and when the diagnostic test yields results on a discrete diagnostic scale.

Optimal combination of biomarkers for time-dependent receiver operating characteristic estimation and related problems

2014 ◽  
Vol 26 (2) ◽  
pp. 898-913
Author(s):  
Zhong Guan ◽  
Jing Qin
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Diagnostic Test ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Time Dependent ◽  
Likelihood Method ◽  
Test Accuracy ◽  
Operating Characteristic Curve ◽  
Receiver Operating

The receiver operating characteristic curve is commonly used for assessing diagnostic test accuracy and for discriminatory ability of a medical diagnostic test in distinguishing between diseases and non-diseased individuals. With the advance of technology, many genetic variables and biomarker variables are easily collected. The most challenging problem is how to combine clinical, genetic, and biomarker variables together to predict disease status. If one is interested in predicting t-year survival, however, the status of “case” (death) and “control” (survival) at the given t-year is unknown for those individuals who were censored before t-year. To conduct a receiver operating characteristic analysis, one has to impute those ambiguous statuses. In this paper, we study a maximum pseudo likelihood method to estimate the underlying parameters and baseline distribution functions. The proposed approach produces more efficient and smoother estimate of the optimal time-dependent receiver operating characteristic curve and more stable estimation of the prediction rule for the t-year survivors. More importantly, the proposal is equipped with a goodness-of-fit test for the model assumption based on the bootstrap method. Two real medical data sets are used for illustration.

Sign in / Sign up

Export Citation Format

Share Document