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Benjamin Reiser - One of the best experts on this subject based on the ideXlab platform.
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construction of confidence intervals for the maximum of the Youden Index and the corresponding cutoff point of a continuous biomarker
Biometrical Journal, 2019Co-Authors: Leonidas E Bantis, Christos T Nakas, Benjamin ReiserAbstract:: Evaluation of the overall accuracy of biomarkers might be based on average measures of the sensitivity for all possible specificities -and vice versa- or equivalently the area under the receiver operating characteristic (ROC) curve that is typically used in such settings. In practice clinicians are in need of a cutoff point to determine whether intervention is required after establishing the utility of a continuous biomarker. The Youden Index can serve both purposes as an overall Index of a biomarker's accuracy, that also corresponds to an optimal, in terms of maximizing the Youden Index, cutoff point that in turn can be utilized for decision making. In this paper, we provide new methods for constructing confidence intervals for both the Youden Index and its corresponding cutoff point. We explore approaches based on the delta approximation under the normality assumption, as well as power transformations to normality and nonparametric kernel- and spline-based approaches. We compare our methods to existing techniques through simulations in terms of coverage and width. We then apply the proposed methods to serum-based markers of a prospective observational study involving diagnosis of late-onset sepsis in neonates.
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construction of confidence regions in the roc space after the estimation of the optimal Youden Index based cut off point
Biometrics, 2014Co-Authors: Leonidas E Bantis, Christos T Nakas, Benjamin ReiserAbstract:Summary After establishing the utility of a continuous diagnostic marker investigators will typically address the question of determining a cut-off point which will be used for diagnostic purposes in clinical decision making. The most commonly used optimality criterion for cut-off point selection in the context of ROC curve analysis is the maximum of the Youden Index. The pair of sensitivity and specificity proportions that correspond to the Youden Index-based cut-off point characterize the performance of the diagnostic marker. Confidence intervals for sensitivity and specificity are routinely estimated based on the assumption that sensitivity and specificity are independent binomial proportions as they arise from the independent populations of diseased and healthy subjects, respectively. The Youden Index-based cut-off point is estimated from the data and as such the resulting sensitivity and specificity proportions are in fact correlated. This correlation needs to be taken into account in order to calculate confidence intervals that result in the anticipated coverage. In this article we study parametric and non-parametric approaches for the construction of confidence intervals for the pair of sensitivity and specificity proportions that correspond to the Youden Index-based optimal cut-off point. These approaches result in the anticipated coverage under different scenarios for the distributions of the healthy and diseased subjects. We find that a parametric approach based on a Box–Cox transformation to normality often works well. For biomarkers following more complex distributions a non-parametric procedure using logspline density estimation can be used.
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Construction of confidence regions in the ROC space after the estimation of the optimal Youden Index‐based cut‐off point
Biometrics, 2013Co-Authors: Leonidas E Bantis, Christos T Nakas, Benjamin ReiserAbstract:Summary After establishing the utility of a continuous diagnostic marker investigators will typically address the question of determining a cut-off point which will be used for diagnostic purposes in clinical decision making. The most commonly used optimality criterion for cut-off point selection in the context of ROC curve analysis is the maximum of the Youden Index. The pair of sensitivity and specificity proportions that correspond to the Youden Index-based cut-off point characterize the performance of the diagnostic marker. Confidence intervals for sensitivity and specificity are routinely estimated based on the assumption that sensitivity and specificity are independent binomial proportions as they arise from the independent populations of diseased and healthy subjects, respectively. The Youden Index-based cut-off point is estimated from the data and as such the resulting sensitivity and specificity proportions are in fact correlated. This correlation needs to be taken into account in order to calculate confidence intervals that result in the anticipated coverage. In this article we study parametric and non-parametric approaches for the construction of confidence intervals for the pair of sensitivity and specificity proportions that correspond to the Youden Index-based optimal cut-off point. These approaches result in the anticipated coverage under different scenarios for the distributions of the healthy and diseased subjects. We find that a parametric approach based on a Box–Cox transformation to normality often works well. For biomarkers following more complex distributions a non-parametric procedure using logspline density estimation can be used.
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Youden Index and the optimal threshold for markers with mass at zero
Statistics in Medicine, 2008Co-Authors: Enrique F Schisterman, David Faraggi, Benjamin Reiser, Jessica HuAbstract:The Youden Index is often used as a summary measure of the receiver operating characteristic curve. It measures the effectiveness of a diagnostic marker and permits the selection of an optimal threshold value or cutoff point for the biomarker of interest. Some markers, while basically continuous and positive, have a spike or positive mass of probability at the value zero. We provide a flexible modeling approach for estimating the Youden Index and its associated cutoff point for such spiked data and compare it with the standard empirical approach. We show how this modeling approach can be adjusted to take covariate information into account. This approach is applied to data on the Coronary Calcium Score, a marker for atherosclerosis. Published in 2007 by John Wiley & Sons, Ltd.
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estimation of the Youden Index and its associated cutoff point
Biometrical Journal, 2005Co-Authors: Ronen Fluss, David Faraggi, Benjamin ReiserAbstract:The Youden Index is a frequently used summary measure of the ROC (Receiver Operating Characteristic) curve. It both, measures the effectiveness of a diagnostic marker and enables the selection of an optimal threshold value (cutoff point) for the marker. In this paper we compare several estimation procedures for the Youden Index and its associated cutoff point. These are based on (1) normal assumptions; (2) transformations to normality; (3) the empirical distribution function; (4) kernel smoothing. These are compared in terms of bias and root mean square error in a large variety of scenarios by means of an extensive simulation study. We find that the empirical method which is the most commonly used has the overall worst performance. In the estimation of the Youden Index the kernel is generally the best unless the data can be well transformed to achieve normality whereas in estimation of the optimal threshold value results are more variable. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
Christos T Nakas - One of the best experts on this subject based on the ideXlab platform.
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construction of confidence intervals for the maximum of the Youden Index and the corresponding cutoff point of a continuous biomarker
Biometrical Journal, 2019Co-Authors: Leonidas E Bantis, Christos T Nakas, Benjamin ReiserAbstract:: Evaluation of the overall accuracy of biomarkers might be based on average measures of the sensitivity for all possible specificities -and vice versa- or equivalently the area under the receiver operating characteristic (ROC) curve that is typically used in such settings. In practice clinicians are in need of a cutoff point to determine whether intervention is required after establishing the utility of a continuous biomarker. The Youden Index can serve both purposes as an overall Index of a biomarker's accuracy, that also corresponds to an optimal, in terms of maximizing the Youden Index, cutoff point that in turn can be utilized for decision making. In this paper, we provide new methods for constructing confidence intervals for both the Youden Index and its corresponding cutoff point. We explore approaches based on the delta approximation under the normality assumption, as well as power transformations to normality and nonparametric kernel- and spline-based approaches. We compare our methods to existing techniques through simulations in terms of coverage and width. We then apply the proposed methods to serum-based markers of a prospective observational study involving diagnosis of late-onset sepsis in neonates.
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construction of confidence regions in the roc space after the estimation of the optimal Youden Index based cut off point
Biometrics, 2014Co-Authors: Leonidas E Bantis, Christos T Nakas, Benjamin ReiserAbstract:Summary After establishing the utility of a continuous diagnostic marker investigators will typically address the question of determining a cut-off point which will be used for diagnostic purposes in clinical decision making. The most commonly used optimality criterion for cut-off point selection in the context of ROC curve analysis is the maximum of the Youden Index. The pair of sensitivity and specificity proportions that correspond to the Youden Index-based cut-off point characterize the performance of the diagnostic marker. Confidence intervals for sensitivity and specificity are routinely estimated based on the assumption that sensitivity and specificity are independent binomial proportions as they arise from the independent populations of diseased and healthy subjects, respectively. The Youden Index-based cut-off point is estimated from the data and as such the resulting sensitivity and specificity proportions are in fact correlated. This correlation needs to be taken into account in order to calculate confidence intervals that result in the anticipated coverage. In this article we study parametric and non-parametric approaches for the construction of confidence intervals for the pair of sensitivity and specificity proportions that correspond to the Youden Index-based optimal cut-off point. These approaches result in the anticipated coverage under different scenarios for the distributions of the healthy and diseased subjects. We find that a parametric approach based on a Box–Cox transformation to normality often works well. For biomarkers following more complex distributions a non-parametric procedure using logspline density estimation can be used.
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Construction of confidence regions in the ROC space after the estimation of the optimal Youden Index‐based cut‐off point
Biometrics, 2013Co-Authors: Leonidas E Bantis, Christos T Nakas, Benjamin ReiserAbstract:Summary After establishing the utility of a continuous diagnostic marker investigators will typically address the question of determining a cut-off point which will be used for diagnostic purposes in clinical decision making. The most commonly used optimality criterion for cut-off point selection in the context of ROC curve analysis is the maximum of the Youden Index. The pair of sensitivity and specificity proportions that correspond to the Youden Index-based cut-off point characterize the performance of the diagnostic marker. Confidence intervals for sensitivity and specificity are routinely estimated based on the assumption that sensitivity and specificity are independent binomial proportions as they arise from the independent populations of diseased and healthy subjects, respectively. The Youden Index-based cut-off point is estimated from the data and as such the resulting sensitivity and specificity proportions are in fact correlated. This correlation needs to be taken into account in order to calculate confidence intervals that result in the anticipated coverage. In this article we study parametric and non-parametric approaches for the construction of confidence intervals for the pair of sensitivity and specificity proportions that correspond to the Youden Index-based optimal cut-off point. These approaches result in the anticipated coverage under different scenarios for the distributions of the healthy and diseased subjects. We find that a parametric approach based on a Box–Cox transformation to normality often works well. For biomarkers following more complex distributions a non-parametric procedure using logspline density estimation can be used.
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generalization of Youden Index for multiple class classification problems applied to the assessment of externally validated cognition in parkinson disease screening
Statistics in Medicine, 2013Co-Authors: Christos T Nakas, John C Dalrymplealford, Tim J Anderson, Todd A AlonzoAbstract:Routine cognitive screening in Parkinson disease (PD) has become essential for management, to track progression and to assess clinical status in therapeutic trials. Patients with mild cognitive impairment (PD-MCI) are more likely to progress to dementia and therefore need to be distinguished from patients with normal cognition and those with dementia. A three-class Youden Index has been recently proposed to select cut-off points in three-class classification problems. In this article, we examine properties of a modification of the three-class Youden Index and propose a generalization to k-class classification problems. Geometric and theoretical properties of the modified Index Jk are examined. It is shown that Jk is equivalent to the sum of the k − 1 two-class Youden indices for the adjacent classes of the ordered alternative problem given that the ordering holds. Methods are applied in the assessment of the Montreal Cognitive Assessment test when screening cognition in PD. Copyright © 2012 John Wiley & Sons, Ltd.
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accuracy and cut off point selection in three class classification problems using a generalization of the Youden Index
Statistics in Medicine, 2010Co-Authors: Christos T Nakas, Todd A Alonzo, Constantin T YiannoutsosAbstract:We study properties of the Index J3, defined as the accuracy, or the maximum correct classification, for a given three-class classification problem. Specifically, using J3 one can assess the discrimination between the three distributions and obtain an optimal pair of cut-off points c1
Youden Index in three-class problems. Parametric and non-parametric approaches for estimation and testing are considered and methods are applied to data from an MRS study on human immunodeficiency virus (HIV) patients. Copyright © 2010 John Wiley & Sons, Ltd.
Martin Schumacher - One of the best experts on this subject based on the ideXlab platform.
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summary roc curve based on a weighted Youden Index for selecting an optimal cutpoint in meta analysis of diagnostic accuracy
Statistics in Medicine, 2010Co-Authors: Gerta Rucker, Martin SchumacherAbstract:Established approaches for analyzing meta-analyses of diagnostic accuracy model the bivariate distribution of the observed pairs of specificity Sp and sensitivity Se, thus accounting for across-study correlation. However, it is still a matter of debate how to define a summary ROC (SROC) curve. It was recently pointed out that the SROC curve is in principle unidentifiable if only one (Sp, Se) pair per study is known. We evaluate an alternative approach, modeling the study-specific ROC curves based on the assumption of linearity in logit space. A setting is considered in which the pair (Sp, Se) that is selected for publication in a particular study maximizes a weighted Youden Index λSe + (1-λ)Sp with a given weight λ. This leads to a fixed slope (1-λ)/λ of the ROC curve in (I-Sp, Se), equivalent to a slope of (1-λ)Sp(1-Sp)/(λSe(1-Se)) for the corresponding straight line in logit space. While the slope depends on the variance ratio of the underlying distributions, the intercept is a function of the mean difference. Our approach leads in a natural way to a new, model-based proposal for a summary ROC curve. It is illustrated using an example from the literature.
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Summary ROC curve based on a weighted Youden Index for selecting an optimal cutpoint in meta‐analysis of diagnostic accuracy
Statistics in Medicine, 2010Co-Authors: Gerta Rucker, Martin SchumacherAbstract:Established approaches for analyzing meta-analyses of diagnostic accuracy model the bivariate distribution of the observed pairs of specificity Sp and sensitivity Se, thus accounting for across-study correlation. However, it is still a matter of debate how to define a summary ROC (SROC) curve. It was recently pointed out that the SROC curve is in principle unidentifiable if only one (Sp, Se) pair per study is known. We evaluate an alternative approach, modeling the study-specific ROC curves based on the assumption of linearity in logit space. A setting is considered in which the pair (Sp, Se) that is selected for publication in a particular study maximizes a weighted Youden Index λSe + (1-λ)Sp with a given weight λ. This leads to a fixed slope (1-λ)/λ of the ROC curve in (I-Sp, Se), equivalent to a slope of (1-λ)Sp(1-Sp)/(λSe(1-Se)) for the corresponding straight line in logit space. While the slope depends on the variance ratio of the underlying distributions, the intercept is a function of the mean difference. Our approach leads in a natural way to a new, model-based proposal for a summary ROC curve. It is illustrated using an example from the literature.
Lili Tian - One of the best experts on this subject based on the ideXlab platform.
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confidence interval estimation of the Youden Index and corresponding cut point for a combination of biomarkers under normality
Communications in Statistics-theory and Methods, 2020Co-Authors: Kristopher Attwood, Lili TianAbstract:In prognostic/diagnostic medical research, it is often the goal to identify a biomarker that differentiates between patients with and without a condition, or patients that will have good or poor re...
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smoothed empirical likelihood for the Youden Index
Computational Statistics & Data Analysis, 2017Co-Authors: Dongliang Wang, Lili Tian, Y ZhaoAbstract:For a continuous scale biomarker of binary disease status, the Youden Index is a frequently used measurement of diagnostic accuracy in the context of the receiver operating characteristic curve and provides an optimal threshold for making diagnosis. The majority of existing inference methods for the Youden Index are either parametric or bootstrap based. In the current paper, the empirical likelihood method for the Youden Index is derived via defining novel smoothed estimating equations, and Wilks’ theorem for the empirical likelihood ratio statistic is established. Extensive simulation studies suggest that the chi-square calibrated empirical likelihood interval estimators are robust to model assumptions, enjoy computational efficiency and perform better than the bootstrap procedure almost uniformly across a variety of scenarios in terms of coverage probabilities.
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joint inference about sensitivity and specificity at the optimal cut off point associated with Youden Index
Computational Statistics & Data Analysis, 2014Co-Authors: Lili TianAbstract:In diagnostic studies, both sensitivity and specificity depend on cut-off point and they are well-known measures for diagnostic accuracy. The diagnostic cut-off point is mostly unknown and needs to be determined by some optimization criteria out of which the one based on the Youden Index has been widely adopted in practice. The estimation of the optimal cut-off point associated with Youden Index depends on both diseased and healthy samples, henceforth, sensitivity and specificity at the estimated cut-off point are correlated. Therefore, it is desirable to make joint inference on both sensitivity and specificity at the estimated cut-off point. Several parametric and non-parametric approaches are proposed to estimate the joint confidence region of sensitivity and specificity at the cut-off point determined by the Youden Index. A real data set is analyzed using the proposed approaches.
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optimal linear combinations of multiple diagnostic biomarkers based on Youden Index
Statistics in Medicine, 2014Co-Authors: Lili TianAbstract:: In practice, usually multiple biomarkers are measured on the same subject for disease diagnosis. Combining these biomarkers into a single score could improve diagnostic accuracy. Many researchers have addressed the problem of finding the optimal linear combination based on maximizing the area under ROC curve (AUC). Actually, such combined score might have less than optimal property at the diagnostic threshold. In this paper, we propose the idea of using Youden Index as an objective function for searching the optimal linear combination. The combined score directly achieves the maximum overall correct classification rate at the diagnostic threshold corresponding to Youden Index; in other words, it is the optimal linear combination score for making the disease diagnosis. We present both empirical and numerical searching methods for the optimal linear combination. We carry out extensive simulation study to investigate the performance of the proposed methods. Additionally, we empirically compare the optimal overall classification rates between the proposed combination based on Youden Index and the traditional one based on AUC and demonstrate a significant gain in diagnostic accuracy for the proposed combination. In the end, we apply the proposed methods to a real data set.
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joint confidence region estimation for area under roc curve and Youden Index
Statistics in Medicine, 2014Co-Authors: Lili TianAbstract:In the field of diagnostic studies, the area under the ROC curve (AUC) serves as an overall measure of a biomarker/diagnostic test's accuracy. Youden Index, defined as the overall correct classification rate minus one at the optimal cut-off point, is another popular Index. For continuous biomarkers of binary disease status, although researchers mainly evaluate the diagnostic accuracy using AUC, for the purpose of making diagnosis, Youden Index provides an important and direct measure of the diagnostic accuracy at the optimal threshold and hence should be taken into consideration in addition to AUC. Furthermore, AUC and Youden Index are generally correlated. In this paper, we initiate the idea of evaluating diagnostic accuracy based on AUC and Youden Index simultaneously. As the first step toward this direction, this paper only focuses on the confidence region estimation of AUC and Youden Index for a single marker. We present both parametric and non-parametric approaches for estimating joint confidence region of AUC and Youden Index. We carry out extensive simulation study to evaluate the performance of the proposed methods. In the end, we apply the proposed methods to a real data set. Copyright © 2013 John Wiley & Sons, Ltd.
Gerta Rucker - One of the best experts on this subject based on the ideXlab platform.
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summary roc curve based on a weighted Youden Index for selecting an optimal cutpoint in meta analysis of diagnostic accuracy
Statistics in Medicine, 2010Co-Authors: Gerta Rucker, Martin SchumacherAbstract:Established approaches for analyzing meta-analyses of diagnostic accuracy model the bivariate distribution of the observed pairs of specificity Sp and sensitivity Se, thus accounting for across-study correlation. However, it is still a matter of debate how to define a summary ROC (SROC) curve. It was recently pointed out that the SROC curve is in principle unidentifiable if only one (Sp, Se) pair per study is known. We evaluate an alternative approach, modeling the study-specific ROC curves based on the assumption of linearity in logit space. A setting is considered in which the pair (Sp, Se) that is selected for publication in a particular study maximizes a weighted Youden Index λSe + (1-λ)Sp with a given weight λ. This leads to a fixed slope (1-λ)/λ of the ROC curve in (I-Sp, Se), equivalent to a slope of (1-λ)Sp(1-Sp)/(λSe(1-Se)) for the corresponding straight line in logit space. While the slope depends on the variance ratio of the underlying distributions, the intercept is a function of the mean difference. Our approach leads in a natural way to a new, model-based proposal for a summary ROC curve. It is illustrated using an example from the literature.
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Summary ROC curve based on a weighted Youden Index for selecting an optimal cutpoint in meta‐analysis of diagnostic accuracy
Statistics in Medicine, 2010Co-Authors: Gerta Rucker, Martin SchumacherAbstract:Established approaches for analyzing meta-analyses of diagnostic accuracy model the bivariate distribution of the observed pairs of specificity Sp and sensitivity Se, thus accounting for across-study correlation. However, it is still a matter of debate how to define a summary ROC (SROC) curve. It was recently pointed out that the SROC curve is in principle unidentifiable if only one (Sp, Se) pair per study is known. We evaluate an alternative approach, modeling the study-specific ROC curves based on the assumption of linearity in logit space. A setting is considered in which the pair (Sp, Se) that is selected for publication in a particular study maximizes a weighted Youden Index λSe + (1-λ)Sp with a given weight λ. This leads to a fixed slope (1-λ)/λ of the ROC curve in (I-Sp, Se), equivalent to a slope of (1-λ)Sp(1-Sp)/(λSe(1-Se)) for the corresponding straight line in logit space. While the slope depends on the variance ratio of the underlying distributions, the intercept is a function of the mean difference. Our approach leads in a natural way to a new, model-based proposal for a summary ROC curve. It is illustrated using an example from the literature.