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.

scholarly journals On the Binormal Predictive Receiver Operating Characteristic Curve for the Joint Assessment of Positive and Negative Predictive Values

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 593 ◽  
Author(s):  
Gareth Hughes
Keyword(s):  
Receiver Operating Characteristic ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Statistical Evaluation ◽  
Characteristic Curve ◽  
Disease Status ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Predictive Values ◽  
Receiver Operating

The predictive receiver operating characteristic (PROC) curve is a diagrammatic format with application in the statistical evaluation of probabilistic disease forecasts. The PROC curve differs from the more well-known receiver operating characteristic (ROC) curve in that it provides a basis for evaluation using metrics defined conditionally on the outcome of the forecast rather than metrics defined conditionally on the actual disease status. Starting from the binormal ROC curve formulation, an overview of some previously published binormal PROC curves is presented in order to place the PROC curve in the context of other methods used in statistical evaluation of probabilistic disease forecasts based on the analysis of predictive values; in particular, the index of separation (PSEP) and the leaf plot. An information theoretic perspective on evaluation is also outlined. Five straightforward recommendations are made with a view to aiding understanding and interpretation of the sometimes-complex patterns generated by PROC curve analysis. The PROC curve and related analyses augment the perspective provided by traditional ROC curve analysis. Here, the binormal ROC model provides the exemplar for investigation of the PROC curve, but potential application extends to analysis based on other distributional models as well as to empirical analysis.

The area under the generalized receiver-operating characteristic curve

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Pablo Martínez-Camblor ◽  
Sonia Pérez-Fernández ◽  
Susana Díaz-Coto
Keyword(s):  
Receiver Operating Characteristic ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Asymptotic Variance ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Finite Samples ◽  
Real World Problem ◽  
Graphical Tool ◽  
Receiver Operating

Abstract The receiver operating-characteristic (ROC) curve is a well-known graphical tool routinely used for evaluating the discriminatory ability of continuous markers, referring to a binary characteristic. The area under the curve (AUC) has been proposed as a summarized accuracy index. Higher values of the marker are usually associated with higher probabilities of having the characteristic under study. However, there are other situations where both, higher and lower marker scores, are associated with a positive result. The generalized ROC (gROC) curve has been proposed as a proper extension of the ROC curve to fit these situations. Of course, the corresponding area under the gROC curve, gAUC, has also been introduced as a global measure of the classification capacity. In this paper, we study in deep the gAUC properties. The weak convergence of its empirical estimator is provided while deriving an explicit and useful expression for the asymptotic variance. We also obtain the expression for the asymptotic covariance of related gAUCs and propose a non-parametric procedure to compare them. The finite-samples behavior is studied through Monte Carlo simulations under different scenarios, presenting a real-world problem in order to illustrate its practical application. The R code functions implementing the procedures are provided as Supplementary Material.

Genome-wide study of salivary microRNAs as potential noninvasive biomarkers for detection of nasopharyngeal carcinoma.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e17535-e17535
Author(s):  
Lirong Wu ◽  
Xia He
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Target Genes ◽  
Characteristic Curve ◽  
Detection Methods ◽  
Healthy Controls ◽  
Label Free ◽  
Discriminatory Accuracy ◽  
Receiver Operating

e17535 Background: Since recent studies reported that blood-based microRNAs (miRNAs) could detect cancers and predict prognosis, a new field has been opened for circulating miRNAs as potential biomarkers in cancers. In this pilot study, we evaluated to our knowledge for the first time whether salivary miRNAs might be applicable as innovative biomarkers for Nasopharyngeal carcinoma (NPC) detection. Methods: By high throughput label-free microarray contained 2025 human miRNA probes, 12 down-regulated miRNAs from saliva samples from 22 newly diagnosed NPC patients and 25 healthy controls were selected. Then, their target genes enriched by gene ontology and pathway were used to construct a regulatory and interaction networks. The receiver operating characteristic analyses (ROC) and logistic regression were calculated to assess discriminatory accuracy. Results: Advanced bioinformatics analysis was conducted to understand the most significant hub gene is TP53 that probably regulated by the 12 dysregulated miRNAs. The ROC including the 12 miRNAs as well as the 6 significant deregulated miRNAs separated NPC patients from healthy controls with a very high (areas under the receiver operating characteristic curve [AUC] = 0.999, sensitivity = 100.00%, specificity = 96.00%) and high accuracy (AUC = 0.941, sensitivity = 95.45%, specificity = 80.00%), respectively. Conclusions: Differentially expressed miRNAs might play critical roles in NPC by regulating their target genes, and may have the potential to become diagnostic biomarkers.

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