A Study on the Bi-Rayleigh ROC Curve Model

Pundir, Sudesh

doi:10.9756/bijdm.1358

Cited by 3 publications

(1 citation statement)

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“…The maximum likelihood methods to estimate the area under the curve and the relevant parameters under a binormal model assumption have been widely used to estimate this area (DeLong, DeLong & Clarke-Pearson 1988). 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.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%