Biomarker assessment in ROC curve analysis using the length of the curve as an index of diagnostic accuracy: the binormal model framework

Franco-Pereira, Alba M.; Nakas, Christos T.; Pardo, M. C.

doi:10.1007/s10182-020-00371-8

Cited by 10 publications

(10 citation statements)

References 29 publications

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“…The simulations conducted in the current research used the bivariate log‐normal distribution as an example of non‐normal data for evaluating the use of the Box‐Cox transformation, the results are satisfactory. The Box‐Cox transformation generally work well for uni‐modal distributions, including the gamma distribution, which does not belong to the Box‐Cox family 28,41 . However, we admit the limitation of the Box‐Cox transformation that it is more likely to fail for mixture distributions 29,32 .…”

Section: Conclusion and Discussionmentioning

confidence: 90%

“…The Box-Cox transformation generally work well for uni-modal distributions, including the gamma distribution, which does not belong to the Box-Cox family. 28,41 However, we admit the limitation of the Box-Cox transformation that it is more likely to fail for mixture distributions. 29,32 Hence, the parametric methods are not as robust as the nonparametric counterparts (common methods were given in Section 3.3) for all distributional settings.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

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Joint inference about the AUC and Youden index for paired biomarkers

Yin

Samawi

Tian

2021

Statistics in Medicine

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It is common to compare biomarkers' diagnostic or prognostic performance using some summary ROC measures such as the area under the ROC curve (AUC) or the Youden index. We propose to compare two paired biomarkers using both the AUC and the Youden index since the two indices describe different aspects of the ROC curve. This comparison can be made by estimating the joint confidence region (an elliptical area) of the differences of the paired AUCs and the Youden indices. Furthermore, for deciding if one marker is better than the other in terms of both the AUC and the Youden index (J), we can test H0:AUCa≤AUCb or Ja≤Jb against Ha:AUCa>AUCb and Ja>Jb using the paired differences. The construction of such a joint hypothesis is an example of the multivariate order‐restricted hypotheses. For such a hypothesis, we propose and compare three testing procedures: (1) the intersection‐union test (IUT); (2) the conditional test; and (3) the joint test. The performance of the proposed inference methods was evaluated and compared through simulations. The simulation results demonstrate that the proposed joint confidence region maintains the desired confidence level, and all three tests maintain the type I error under the null. Furthermore, among the three proposed testing methods, the conditional test is the preferred approach with markedly larger power consistently than the other two competing methods.

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Section: Conclusion and Discussionmentioning

confidence: 90%

Section: Conclusion and Discussionmentioning

confidence: 99%

Joint inference about the AUC and Youden index for paired biomarkers

Yin

Samawi

Tian

2021

Statistics in Medicine

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show abstract

“…The area under the curve (AUC) is unquestionably the most extensively used metric of diagnostic accuracy in receiver operating characteristic (ROC) curve analysis for determining the usefulness of a biomarker or comparing competing biomarkers. Along with the AUC, the maximum of the Youden index, J, is often employed to measure diagnostic accuracy and as a tool for determining the appropriate cutoff point for diagnostic purposes depending on the biomarker in question [45].…”

Section: Discussionmentioning

confidence: 99%

Co-expression network analysis illustrates the importance of driver genes in colorectal cancer diagnosis, prevention, and therapy

Yari

MotieGhader

Samadzadeh

et al. 2022

Preprint

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Background Colorectal cancer (CRC) is the third most frequent kind of cancer globally, making it a significant public health problem. This project aims to construct a network to uncover potential biomarkers for diagnostic reasons, prospective EDCs for preventative applications, and potential drugs for therapeutic purposes in early-stage CRC. Results To this end, we identified driver genes linked with early-stage colorectal cancer and used the R package WGCNA to design a co-expression network. Following that, modules were chosen, and possible biomarkers were identified using the Cytoscape software and the TCGA database. Then, using the comptox database, we identified probable EDCs and created an EDC-GENE network. Finally, we introduced EDCs with a high degree of risk. The drug-gene network was then reconstructed to find viable therapies for colorectal cancer in its early stages. The co-expression network was constructed using the 1108 driver Genes’ mRNA expression values of 70 early-stage CRC and 12 healthy control samples. The clustering results show that the overlapping gene set is divided into 27 modules. Our study identified five modules (indicated in dark green, dark orange, light cyan, royal blue, and purple) according to the average linkage hierarchical clustering and Zsummary less than 2. Then we find 17 high degree genes of these modules as potential biomarkers for diagnostic issues. Moreover, we explored 25 possible high degrees of Endocrine Disrupting Chemicals that affect the main genes of each module for preventing purposes. Finally, we identified 27 potential high degree drugs that affect the primary genes of each module as treating aims. Then, these biomarkers, EDCs, and drugs that may be tested as a basis for future research were introduced. Conclusions This research aimed to identify potential biomarkers for diagnostic purposes, prospective EDCs for prevention, and potential therapeutic agents in colorectal cancer in its early stages. These biomarkers, EDCs, and medications will aid in diagnosing, preventing, and treating colorectal cancer in its early stages.

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“…One measure that was very recently studied under a binormal setting is the length of the ROC curve. 13 Therein, the utility of the length of the binormal model-based ROC curve as an accuracy index is discussed. In this paper, we study its use as a measure for ranking biomarkers in a general setting that involves discrimination of cases and controls that includes nonmonotone biomarkers under general parametric as well as nonparametric frameworks.…”

Section: Roc(t)=tprmentioning

confidence: 99%

The length of the receiver operating characteristic curve and the two cutoff Youden index within a robust framework for discovery, evaluation, and cutoff estimation in biomarker studies involving improper receiver operating characteristic curves

Bantis

Tsimikas

Chambers

et al. 2021

Statistics in Medicine

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During the early stage of biomarker discovery, high throughput technologies allow for simultaneous input of thousands of biomarkers that attempt to discriminate between healthy and diseased subjects. In such cases, proper ranking of biomarkers is highly important. Common measures, such as the area under the receiver operating characteristic (ROC) curve (AUC), as well as affordable sensitivity and specificity levels, are often taken into consideration. Strictly speaking, such measures are appropriate under a stochastic ordering assumption, which implies, without loss of generality, that higher measurements are more indicative for the disease. Such an assumption is not always plausible and may lead to rejection of extremely useful biomarkers at this early discovery stage. We explore the length of a smooth ROC curve as a measure for biomarker ranking, which is not subject to directionality. We show that the length corresponds to a divergence, is identical to the corresponding length of the optimal (likelihood ratio) ROC curve, and is an appropriate measure for ranking biomarkers. We explore the relationship between the length measure and the AUC of the optimal ROC curve. We then provide a complete framework for the evaluation of a biomarker in terms of sensitivity and specificity through a proposed ROC analogue for use in improper settings. In the absence of any clinical insight regarding the appropriate cutoffs, we estimate the sensitivity and specificity under a two-cutoff extension of the Youden index and we further take into account the implied costs. We apply our approaches on two biomarker studies that relate to pancreatic and esophageal cancer.

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Biomarker assessment in ROC curve analysis using the length of the curve as an index of diagnostic accuracy: the binormal model framework

Cited by 10 publications

References 29 publications

Joint inference about the AUC and Youden index for paired biomarkers

Joint inference about the AUC and Youden index for paired biomarkers

Co-expression network analysis illustrates the importance of driver genes in colorectal cancer diagnosis, prevention, and therapy

The length of the receiver operating characteristic curve and the two cutoff Youden index within a robust framework for discovery, evaluation, and cutoff estimation in biomarker studies involving improper receiver operating characteristic curves

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