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Margaret S Pepe - One of the best experts on this subject based on the ideXlab platform.
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estimating the receiver operating characteristic Curve in studies that match controls to cases on covariates
Academic Radiology, 2013Co-Authors: Margaret S Pepe, Christopher W SeymourAbstract:Rationale and Objectives Studies evaluating a new diagnostic imaging test may select control subjects without disease who are similar to case subjects with disease in regard to factors potentially related to the imaging result. Selecting one or more controls that are matched to each case on factors such as age, comorbidities, or study site improves study validity by eliminating potential biases due to differential characteristics of readings for cases versus controls. However, it is not widely appreciated that valid analysis requires that the receiver operating characteristic (ROC) Curve be adjusted for covariates. We propose a new computationally simple method for estimating the covariate-adjusted ROC Curve that is appropriate in matched case-control studies. Materials and Methods We provide theoretical arguments for the validity of the estimator and demonstrate its application to data. We compare the statistical properties of the estimator with those of a previously proposed estimator of the covariate-adjusted ROC Curve. We demonstrate an application of the estimator to data derived from a study of emergency medical services encounters where the goal is to diagnose critical illness in nontrauma, non–cardiac arrest patients. A novel bootstrap method is proposed for calculating confidence intervals. Results The new estimator is computationally very simple, yet we show it yields values that approximate the existing, more complicated estimator in simulated data sets. We found that the new estimator has excellent statistical properties, with bias and efficiency comparable with the existing method. Conclusions In matched case-control studies, the ROC Curve should be adjusted for matching covariates and can be estimated with the new computationally simple approach.
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adjusting for covariate effects on classification accuracy using the covariate adjusted receiver operating characteristic Curve
Biometrika, 2009Co-Authors: Holly Janes, Margaret S PepeAbstract:Recent scientific and technological innovations have produced an abundance of potential markers that are being investigated for their use in disease screening and diagnosis. In evaluating these markers, it is often necessary to account for covariates associated with the marker of interest. Covariates may include subject characteristics, expertise of the test operator, test procedures or aspects of specimen handling. In this paper, we propose the covariate-adjusted receiver operating characteristic Curve, a measure of covariate-adjusted classification accuracy. Nonparametric and semiparametric estimators are proposed, asymptotic distribution theory is provided and finite sample performance is investigated. For illustration we characterize the age-adjusted discriminatory accuracy of prostate-specific antigen as a biomarker for prostate cancer. Copyright 2009, Oxford University Press.
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Adjusting for covariate effects on classification accuracy using the covariate-adjusted receiver operating characteristic Curve.
Biometrika, 2009Co-Authors: Holly Janes, Margaret S PepeAbstract:Recent scientific and technological innovations have produced an abundance of potential markers that are being investigated for their use in disease screening and diagnosis. In evaluating these markers, it is often necessary to account for covariates associated with the marker of interest. Covariates may include subject characteristics, expertise of the test operator, test procedures or aspects of specimen handling. In this paper, we propose the covariate-adjusted receiver operating characteristic Curve, a measure of covariate-adjusted classification accuracy. Nonparametric and semiparametric estimators are proposed, asymptotic distribution theory is provided and finite sample performance is investigated. For illustration we characterize the age-adjusted discriminatory accuracy of prostate-specific antigen as a biomarker for prostate cancer.
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letter by pepe et al regarding article use and misuse of the receiver operating characteristic Curve in risk prediction
Circulation, 2007Co-Authors: Margaret S Pepe, Holly Janes, Jessie Wen GuAbstract:To the Editor: Current statistical approaches for evaluation of risk prediction markers are unsatisfactory. We applaud Cook’s criticisms of the c-index, or area under the receiver operating characteristic Curve. This index is based on the notion of pairing subjects, one with poor outcome (eg, cardiovascular event within 10 years) and one without, and determination of whether the risk for the former (ie, the case) is larger than the risk for the latter (ie, the control). This probability of correct ordering of risks is not a relevant measure of …
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combining predictors for classification using the area under the receiver operating characteristic Curve
Biometrics, 2006Co-Authors: Margaret S Pepe, Gary LongtonAbstract:Summary No single biomarker for cancer is considered adequately sensitive and specific for cancer screening. It is expected that the results of multiple markers will need to be combined in order to yield adequately accurate classification. Typically, the objective function that is optimized for combining markers is the likelihood function. In this article, we consider an alternative objective function—the area under the empirical receiver operating characteristic Curve (AUC). We note that it yields consistent estimates of parameters in a generalized linear model for the risk score but does not require specifying the link function. Like logistic regression, it yields consistent estimation with case–control or cohort data. Simulation studies suggest that AUC-based classification scores have performance comparable with logistic likelihood-based scores when the logistic regression model holds. Analysis of data from a proteomics biomarker study shows that performance can be far superior to logistic regression derived scores when the logistic regression model does not hold. Model fitting by maximizing the AUC rather than the likelihood should be considered when the goal is to derive a marker combination score for classification or prediction.
Pablo Martinezcamblor - One of the best experts on this subject based on the ideXlab platform.
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parametric estimates for the receiver operating characteristic Curve generalization for non monotone relationships
Statistical Methods in Medical Research, 2019Co-Authors: Pablo Martinezcamblor, Juan Carlos PardofernandezAbstract:Diagnostic procedures are based on establishing certain conditions and then checking if those conditions are satisfied by a given individual. When the diagnostic procedure is based on a continuous marker, this is equivalent to fix a region or classification subset and then check if the observed value of the marker belongs to that region. Receiver operating characteristic Curve is a valuable and popular tool to study and compare the diagnostic ability of a given marker. Besides, the area under the receiver operating characteristic Curve is frequently used as an index of the global discrimination ability. This paper revises and widens the scope of the receiver operating characteristic Curve definition by setting the classification subsets in which the final decision is based in the spotlight of the analysis. We revise the definition of the receiver operating characteristic Curve in terms of particular classes of classification subsets and then focus on a receiver operating characteristic Curve generalization for situations in which both low and high values of the marker are associated with more probability of having the studied characteristic. Parametric and non-parametric estimators of the receiver operating characteristic Curve generalization are investigated. Monte Carlo studies and real data examples illustrate their practical performance.
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the youden index in the generalized receiver operating characteristic Curve context
The International Journal of Biostatistics, 2019Co-Authors: Pablo Martinezcamblor, Juan Carlos PardofernandezAbstract:: The receiver operating characteristic (ROC) Curve and their associated summary indices, such as the Youden index, are statistical tools commonly used to analyze the discrimination ability of a (bio)marker to distinguish between two populations. This paper presents the concept of Youden index in the context of the generalized ROC (gROC) Curve for non-monotone relationships. The interval estimation of the Youden index and the associated cutoff points in a parametric (binormal) and a non-parametric setting is considered. Monte Carlo simulations and a real-world application illustrate the proposed methodology.
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receiver operating characteristic Curve generalization for non monotone relationships
Statistical Methods in Medical Research, 2017Co-Authors: Pablo Martinezcamblor, Norberto Corral, Julio Pascual, Eva CernudamorollonAbstract:The receiver operating characteristic Curve is a popular graphical method frequently used in order to study the diagnostic capacity of continuous markers. It represents in a plot true-positive rates against the false-positive ones. Both the practical and theoretical aspects of the receiver operating characteristic Curve have been extensively studied. Conventionally, it is assumed that the considered marker has a monotone relationship with the studied characteristic; i.e., the upper (lower) values of the (bio)marker are associated with a higher probability of a positive result. However, there exist real situations where both the lower and the upper values of the marker are associated with higher probability of a positive result. We propose a receiver operating characteristic Curve generalization, g, useful in this context. All pairs of possible cut-off points, one for the lower and another one for the upper marker values, are taken into account and the best of them are selected. The natural empirical estim...
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fully non parametric receiver operating characteristic Curve estimation for random effects meta analysis
Statistical Methods in Medical Research, 2017Co-Authors: Pablo MartinezcamblorAbstract:Meta-analyses, broadly defined as the quantitative review and synthesis of the results of related but independent comparable studies, allow to know the state of the art of one considered topic. Since the amount of available bibliography has enhanced in almost all fields and, specifically, in biomedical research, its popularity has drastically increased during the last decades. In particular, different methodologies have been developed in order to perform meta-analytic studies of diagnostic tests for both fixed- and random-effects models. From a parametric point of view, these techniques often compute a bivariate estimation for the sensitivity and the specificity by using only one threshold per included study. Frequently, an overall receiver operating characteristic Curve based on a bivariate normal distribution is also provided. In this work, the author deals with the problem of estimating an overall receiver operating characteristic Curve from a fully non-parametric approach when the data come from a met...
James J Hudziak - One of the best experts on this subject based on the ideXlab platform.
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assessment of dysregulated children using the child behavior checklist a receiver operating characteristic Curve analysis
Psychological Assessment, 2010Co-Authors: Robert R Althoff, Lynsay Ayer, David C Rettew, James J HudziakAbstract:Disorders of self-regulatory behavior are common reasons for referral to child and adolescent clinicians. Here, the authors sought to compare 2 methods of empirically based assessment of children with problems in self-regulatory behavior. Using parental reports on 2,028 children (53% boys) from a U.S. national probability sample of the Child Behavior Checklist (CBCL; T. M. Achenbach & L. A. Rescorla, 2001), the receiver operating characteristic Curve analysis was applied to compare scores on the Posttraumatic Stress Problems Scale (PTSP) of the CBCL with the CBCL Dysregulation Profile (DP), identified using latent class analysis of the Attention Problems, Aggressive Behavior, and Anxious/Depressed scales of the CBCL. The CBCL-PTSP score demonstrated an area under the Curve of between .88 and .91 for predicting membership in the CBCL-DP profile for boys and for girls. These findings suggest that the CBCL-PTSP, which others have shown does not uniquely identify children who have been traumatized, does identify the same profile of behavior as the CBCL-DP. Therefore, the authors recommend renaming the CBCL-PTSP the Dysregulation Short Scale and provide some guidelines for the use of the CBCL-DP scale and the CBCL-PTSP in clinical practice.
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assessment of dysregulated children using the child behavior checklist a receiver operating characteristic Curve analysis
Psychological Assessment, 2010Co-Authors: Robert R Althoff, Lynsay Ayer, David C Rettew, James J HudziakAbstract:Disorders of self-regulatory behavior are common reasons for referral to child and adolescent clinicians. Here, the authors sought to compare 2 methods of empirically based assessment of children with problems in self-regulatory behavior. Using parental reports on 2,028 children (53% boys) from a U.S. national probability sample of the Child Behavior Checklist (CBCL; T. M. Achenbach & L. A. Rescorla, 2001), the receiver operating characteristic Curve analysis was applied to compare scores on the Posttraumatic Stress Problems Scale (PTSP) of the CBCL with the CBCL Dysregulation Profile (DP), identified using latent class analysis of the Attention Problems, Aggressive Behavior, and Anxious/Depressed scales of the CBCL. The CBCL-PTSP score demonstrated an area under the Curve of between .88 and .91 for predicting membership in the CBCL-DP profile for boys and for girls. These findings suggest that the CBCL-PTSP, which others have shown does not uniquely identify children who have been traumatized, does identify the same profile of behavior as the CBCL-DP. Therefore, the authors recommend renaming the CBCL-PTSP the Dysregulation Short Scale and provide some guidelines for the use of the CBCL-DP scale and the CBCL-PTSP in clinical practice. (PsycINFO Database Record (c) 2010 APA, all rights reserved). Language: en
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the cbcl predicts dsm bipolar disorder in children a receiver operating characteristic Curve analysis
Bipolar Disorders, 2005Co-Authors: Stephen V Faraone, Robert R Althoff, James J Hudziak, Michael C Monuteaux, Joseph BiedermanAbstract:Background: No clear consensus has been reached yet on how best to characterize children who suffer from pediatric bipolar disorder (PBD). The CBCL-PBD profile on the Child Behavior Checklist (CBCL) has been consistently reported showing deviant findings on the Attention Problems, Aggressive Behavior, and Anxious-Depressed subscales. Aim: To examine the sensitivity and specificity of the proposed CBCL-PBD profile for determining DSM diagnosis of PBD. Methods: We applied receiver operating characteristic (ROC) Curve analysis to data from 471 probands from two family studies of attention-deficit hyperactivity disorder and their 410 siblings. Results: The CBCL-PBD score demonstrated an area under the Curve (AUC) of 0.97 for probands and 0.82 for siblings for current diagnosis of PBD, suggesting that the CBCL-PBD provided a highly efficient way of identifying subjects with a current diagnosis of PBD in this sample. Conclusions: These findings suggest that the CBCL-PBD may provide a highly efficient way of screening for childhood bipolar disorder.
Thajasvarie Naicker - One of the best experts on this subject based on the ideXlab platform.
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Area under receiver operating characteristic Curve (AUC) showing the performance of sFlt-1/PIGF ratio in predicting the administration of ≥ 3 slow- and/or a rapid-acting antihypertensive drug on postpartum Day 0, Day 1, Day 2 and Day 3 in women with preeclampsia with severe features.
2019Co-Authors: Nnabuike Chibuoke Ngene, Jagidesa Moodley, Thajasvarie NaickerAbstract:Area under receiver operating characteristic Curve (AUC) showing the performance of sFlt-1/PIGF ratio in predicting the administration of ≥ 3 slow- and/or a rapid-acting antihypertensive drug on postpartum Day 0, Day 1, Day 2 and Day 3 in women with preeclampsia with severe features.
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Area under receiver operating characteristic Curve (AUC) showing the performance of sFlt-1/PIGF ratio in predicting the administration of ≥ 3 slow- and/or a rapid-acting antihypertensive drug on postpartum Day 0, Day 1, Day 2 and Day 3 in both groups of women with preeclampsia with severe features and normotensive pregnancy.
2019Co-Authors: Nnabuike Chibuoke Ngene, Jagidesa Moodley, Thajasvarie NaickerAbstract:Area under receiver operating characteristic Curve (AUC) showing the performance of sFlt-1/PIGF ratio in predicting the administration of ≥ 3 slow- and/or a rapid-acting antihypertensive drug on postpartum Day 0, Day 1, Day 2 and Day 3 in both groups of women with preeclampsia with severe features and normotensive pregnancy.
Robert R Althoff - One of the best experts on this subject based on the ideXlab platform.
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assessment of dysregulated children using the child behavior checklist a receiver operating characteristic Curve analysis
Psychological Assessment, 2010Co-Authors: Robert R Althoff, Lynsay Ayer, David C Rettew, James J HudziakAbstract:Disorders of self-regulatory behavior are common reasons for referral to child and adolescent clinicians. Here, the authors sought to compare 2 methods of empirically based assessment of children with problems in self-regulatory behavior. Using parental reports on 2,028 children (53% boys) from a U.S. national probability sample of the Child Behavior Checklist (CBCL; T. M. Achenbach & L. A. Rescorla, 2001), the receiver operating characteristic Curve analysis was applied to compare scores on the Posttraumatic Stress Problems Scale (PTSP) of the CBCL with the CBCL Dysregulation Profile (DP), identified using latent class analysis of the Attention Problems, Aggressive Behavior, and Anxious/Depressed scales of the CBCL. The CBCL-PTSP score demonstrated an area under the Curve of between .88 and .91 for predicting membership in the CBCL-DP profile for boys and for girls. These findings suggest that the CBCL-PTSP, which others have shown does not uniquely identify children who have been traumatized, does identify the same profile of behavior as the CBCL-DP. Therefore, the authors recommend renaming the CBCL-PTSP the Dysregulation Short Scale and provide some guidelines for the use of the CBCL-DP scale and the CBCL-PTSP in clinical practice.
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assessment of dysregulated children using the child behavior checklist a receiver operating characteristic Curve analysis
Psychological Assessment, 2010Co-Authors: Robert R Althoff, Lynsay Ayer, David C Rettew, James J HudziakAbstract:Disorders of self-regulatory behavior are common reasons for referral to child and adolescent clinicians. Here, the authors sought to compare 2 methods of empirically based assessment of children with problems in self-regulatory behavior. Using parental reports on 2,028 children (53% boys) from a U.S. national probability sample of the Child Behavior Checklist (CBCL; T. M. Achenbach & L. A. Rescorla, 2001), the receiver operating characteristic Curve analysis was applied to compare scores on the Posttraumatic Stress Problems Scale (PTSP) of the CBCL with the CBCL Dysregulation Profile (DP), identified using latent class analysis of the Attention Problems, Aggressive Behavior, and Anxious/Depressed scales of the CBCL. The CBCL-PTSP score demonstrated an area under the Curve of between .88 and .91 for predicting membership in the CBCL-DP profile for boys and for girls. These findings suggest that the CBCL-PTSP, which others have shown does not uniquely identify children who have been traumatized, does identify the same profile of behavior as the CBCL-DP. Therefore, the authors recommend renaming the CBCL-PTSP the Dysregulation Short Scale and provide some guidelines for the use of the CBCL-DP scale and the CBCL-PTSP in clinical practice. (PsycINFO Database Record (c) 2010 APA, all rights reserved). Language: en
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the cbcl predicts dsm bipolar disorder in children a receiver operating characteristic Curve analysis
Bipolar Disorders, 2005Co-Authors: Stephen V Faraone, Robert R Althoff, James J Hudziak, Michael C Monuteaux, Joseph BiedermanAbstract:Background: No clear consensus has been reached yet on how best to characterize children who suffer from pediatric bipolar disorder (PBD). The CBCL-PBD profile on the Child Behavior Checklist (CBCL) has been consistently reported showing deviant findings on the Attention Problems, Aggressive Behavior, and Anxious-Depressed subscales. Aim: To examine the sensitivity and specificity of the proposed CBCL-PBD profile for determining DSM diagnosis of PBD. Methods: We applied receiver operating characteristic (ROC) Curve analysis to data from 471 probands from two family studies of attention-deficit hyperactivity disorder and their 410 siblings. Results: The CBCL-PBD score demonstrated an area under the Curve (AUC) of 0.97 for probands and 0.82 for siblings for current diagnosis of PBD, suggesting that the CBCL-PBD provided a highly efficient way of identifying subjects with a current diagnosis of PBD in this sample. Conclusions: These findings suggest that the CBCL-PBD may provide a highly efficient way of screening for childhood bipolar disorder.