Detecting diagnostic accuracy of two biomarkers through a bivariate log-normal ROC curve

Pundir, Sudesh; Amala, R.

doi:10.1080/02664763.2015.1046823

Cited by 4 publications

(9 citation statements)

References 24 publications

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“…However, an ROC curve that does not cross the diagonal may still be improper [ 11 ]. From the literature, Scenario 2 provides a proper ROC curve [ 9 ], and it appears from [ 10 ] that Scenario 1 provides an improper curve. We found no information relating to the curves for Scenarios 3 and 4.…”

Section: Methodsmentioning

confidence: 99%

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Mutual Information as a Performance Measure for Binary Predictors Characterized by Both ROC Curve and PROC Curve Analysis

Hughes

Kopetzky

McRoberts

2020

Entropy

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The predictive receiver operating characteristic (PROC) curve differs from the morewell-known receiver operating characteristic (ROC) curve in that it provides a basis for theevaluation of binary diagnostic tests using metrics defined conditionally on the outcome of the testrather than metrics defined conditionally on the actual disease status. Application of PROC curveanalysis may be hindered by the complex graphical patterns that are sometimes generated. Herewe present an information theoretic analysis that allows concurrent evaluation of PROC curves andROC curves together in a simple graphical format. The analysis is based on the observation thatmutual information may be viewed both as a function of ROC curve summary statistics (sensitivityand specificity) and prevalence, and as a function of predictive values and prevalence. Mutualinformation calculated from a 2 × 2 prediction-realization table for a specified risk score thresholdon an ROC curve is the same as the mutual information calculated at the same risk score thresholdon a corresponding PROC curve. Thus, for a given value of prevalence, the risk score threshold thatmaximizes mutual information is the same on both the ROC curve and the corresponding PROCcurve. Phytopathologists and clinicians who have previously relied solely on ROC curve summarystatistics when formulating risk thresholds for application in practical agricultural or clinicaldecision-making contexts are thus presented with a methodology that brings predictive valueswithin the scope of that formulation.

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Section: Methodsmentioning

confidence: 99%

“…Each distribution was plotted over the range from 1 to 30 on the horizontal axis. iii See [ 9 ] for further discussion of the bi-lognormal receiver operating characteristic (ROC) curve. iv See [ 10 ] for further discussion of the bi-chi-squared ROC curve.…”

Section: Figurementioning

confidence: 99%

Mutual Information as a Performance Measure for Binary Predictors Characterized by Both ROC Curve and PROC Curve Analysis

Hughes

Kopetzky

McRoberts

2020

Entropy

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“…which cannot be expressed in a closed form, its can be estimated using numerical methods, such as the trapezoidal rule or Simpson's rule (Pundir & Amala 2015).…”

Section: Positive Negativementioning

confidence: 99%

“…Section 3 presents the estimates obtained using the proposed method and the results of a simulation study. In addition, a comparison is shown with the estimates obtained with the Pudir and Amala method (Pundir & Amala 2015). Section 4 we provide a practical example with real data on dengue detection.…”

Section: Introductionmentioning

confidence: 99%

“…The normality assumption on the biomarkers or on monotonic transformations of them in both diseased and non diseased populations, in some situations is not true because there exist many biomarkers expressed in a continuous form (Pundir & Amala 2012). Pundir & Amala (2015) consider the use of two continuous biomarkers as clinical diagnostic tests, and they develop a method to estimate AUC under the assumption of correlated tests using a log-normal distribution and the Pearson's correlation coefficient. On the other hand, DeLong et al (1988) addresses a nonparametric comparison of areas under correlated ROC curves using the theory of generalized U-statistics which takes advantage of the properties of Mann-Whitney statistic to generate an estimated covariance matrix.…”

Section: Introductionmentioning

confidence: 99%

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Using Copula Functions to Estimate The AUC for Two Dependent Diagnostic Tests

Melo

Yela

Cuevas

2020

Rev. colomb. estad.

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When performing validation studies on diagnostic classification procedures, one or more biomarkers are typically measured in individuals. Some of these biomarkers may provide better information; moreover, more than one biomarker may be significant and may exhibit dependence between them. This proposal intends to estimate the Area Under the Receiver Operating Characteristic Curve (AUC) for classifying individuals in a screening study. We analyze the dependence between the results of the tests by means of copula-type dependence (using FGM and Gumbel-Barnett copula functions), and studying the respective AUC under this type of dependence. Three different dependence-level values were evaluated for each copula function considered. In most of the reviewed literature, the authors assume a normal model to represent the performance of the biomarkers used for clinical diagnosis. There are situations in which assuming normality is not possible because that model is not suitable for one or both biomarkers. The proposed statistical model does not depend on some distributional assumption for the biomarkers used for diagnosis procedure, and additionally, it is not necessary to observe a strong or moderate linear dependence between them.

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Evaluation of biomarker using two parameter bi-exponential ROC curve

Pundir¹,

Amala²

2015

Pak.j.stat.oper.res.

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Receiver Operating Characteristic (ROC) Curve is used for assessing the ability of a biomarker/screening test to discriminate between non-diseased and diseased subject. In this paper, the parametric ROC curve is studied by assuming two-parameter exponential distribution to the biomarker values. The ROC model developed under this assumption is called bi-exponential ROC (EROC) model. Here, the research interest is to know how far the biomarker will make a distinction between diseased and non-diseased subjects when the gold standard is available using parametric EROC curve and its Area Under the EROC Curve (AUC). Here, the standard error is used as an estimate of the precision of the accuracy measure AUC. The properties of EROC curve that explains the behavior of the EROC curve are also discussed. The AUC along with its asymptotic variance and confidence interval are derived.

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Detecting diagnostic accuracy of two biomarkers through a bivariate log-normal ROC curve

Cited by 4 publications

References 24 publications

Mutual Information as a Performance Measure for Binary Predictors Characterized by Both ROC Curve and PROC Curve Analysis

Mutual Information as a Performance Measure for Binary Predictors Characterized by Both ROC Curve and PROC Curve Analysis

Using Copula Functions to Estimate The AUC for Two Dependent Diagnostic Tests

Evaluation of biomarker using two parameter bi-exponential ROC curve

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