The Experts below are selected from a list of 117633 Experts worldwide ranked by ideXlab platform
Pablo Martinezcamblor - One of the best experts on this subject based on the ideXlab platform.
-
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.
-
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.
-
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...
-
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...
G. Schneider - One of the best experts on this subject based on the ideXlab platform.
-
a program for computing the prediction probability and the related Receiver Operating Characteristic graph
Anesthesia & Analgesia, 2010Co-Authors: Denis Jordan, Marcel Steiner, Eberhard Kochs, G. SchneiderAbstract:Prediction probability (PK) and the area under the Receiver Operating Characteristic curve (AUC) are statistical measures to assess the performance of anesthetic depth indicators, to more precisely quantify the correlation between observed anesthetic depth and corresponding values of a monitor or in
-
a program for computing the prediction probability and the related Receiver Operating Characteristic graph
Anesthesia & Analgesia, 2010Co-Authors: Denis Jordan, Marcel Steiner, Eberhard Kochs, G. SchneiderAbstract:Prediction probability (P(K)) and the area under the Receiver Operating Characteristic curve (AUC) are statistical measures to assess the performance of anesthetic depth indicators, to more precisely quantify the correlation between observed anesthetic depth and corresponding values of a monitor or indicator. In contrast to many other statistical tests, they offer several advantages. First, P(K) and AUC are independent from scale units and assumptions on underlying distributions. Second, the calculation can be performed without any knowledge about particular indicator threshold values, which makes the test more independent from specific test data. Third, recent approaches using resampling methods allow a reliable comparison of P(K) or AUC of different indicators of anesthetic depth. Furthermore, both tests allow simple interpretation, whereby results between 0 and 1 are related to the probability, how good an indicator separates the observed levels of anesthesia. For these reasons, P(K) and AUC have become popular in medical decision making. P(K) is intended for polytomous patient states (i.e., >2 anesthetic levels) and can be considered as a generalization of the AUC, which was basically introduced to assess a predictor of dichotomous classes (e.g., consciousness and unconsciousness in anesthesia). Dichotomous paradigms provide equal values of P(K) and AUC test statistics. In the present investigation, we introduce a user-friendly computer program for computing P(K) and estimating reliable bootstrap confidence intervals. It is designed for multiple comparisons of the performance of depth of anesthesia indicators. Additionally, for dichotomous classes, the program plots the Receiver Operating Characteristic graph completing information obtained from P(K) or AUC, respectively. In clinical investigations, both measures are applied for indicator assessment, where ambiguous usage and interpretation may be a consequence. Therefore, a summary of the concepts of P(K) and AUC including brief and easily understandable proof of their equality is presented in the text. The exposure introduces readers to the algorithms of the provided computer program and is intended to make standardized performance tests of depth of anesthesia indicators available to medical researchers.
Andriy I Bandos - One of the best experts on this subject based on the ideXlab platform.
-
jackknife variance of the partial area under the empirical Receiver Operating Characteristic curve
Statistical Methods in Medical Research, 2017Co-Authors: Andriy I BandosAbstract:Receiver Operating Characteristic analysis provides an important methodology for assessing traditional (e.g., imaging technologies and clinical practices) and new (e.g., genomic studies, biomarker development) diagnostic problems. The area under the clinically/practically relevant part of the Receiver Operating Characteristic curve (partial area or partial area under the Receiver Operating Characteristic curve) is an important performance index summarizing diagnostic accuracy at multiple Operating points (decision thresholds) that are relevant to actual clinical practice. A robust estimate of the partial area under the Receiver Operating Characteristic curve is provided by the area under the corresponding part of the empirical Receiver Operating Characteristic curve. We derive a closed-form expression for the jackknife variance of the partial area under the empirical Receiver Operating Characteristic curve. Using the derived analytical expression, we investigate the differences between the jackknife varia...
-
jackknife variance of the partial area under the empirical Receiver Operating Characteristic curve
Statistical Methods in Medical Research, 2017Co-Authors: Andriy I Bandos, Ben Guo, David GurAbstract:Receiver Operating Characteristic analysis provides an important methodology for assessing traditional (e.g., imaging technologies and clinical practices) and new (e.g., genomic studies, biomarker ...
Denis Jordan - One of the best experts on this subject based on the ideXlab platform.
-
a program for computing the prediction probability and the related Receiver Operating Characteristic graph
Anesthesia & Analgesia, 2010Co-Authors: Denis Jordan, Marcel Steiner, Eberhard Kochs, G. SchneiderAbstract:Prediction probability (PK) and the area under the Receiver Operating Characteristic curve (AUC) are statistical measures to assess the performance of anesthetic depth indicators, to more precisely quantify the correlation between observed anesthetic depth and corresponding values of a monitor or in
-
a program for computing the prediction probability and the related Receiver Operating Characteristic graph
Anesthesia & Analgesia, 2010Co-Authors: Denis Jordan, Marcel Steiner, Eberhard Kochs, G. SchneiderAbstract:Prediction probability (P(K)) and the area under the Receiver Operating Characteristic curve (AUC) are statistical measures to assess the performance of anesthetic depth indicators, to more precisely quantify the correlation between observed anesthetic depth and corresponding values of a monitor or indicator. In contrast to many other statistical tests, they offer several advantages. First, P(K) and AUC are independent from scale units and assumptions on underlying distributions. Second, the calculation can be performed without any knowledge about particular indicator threshold values, which makes the test more independent from specific test data. Third, recent approaches using resampling methods allow a reliable comparison of P(K) or AUC of different indicators of anesthetic depth. Furthermore, both tests allow simple interpretation, whereby results between 0 and 1 are related to the probability, how good an indicator separates the observed levels of anesthesia. For these reasons, P(K) and AUC have become popular in medical decision making. P(K) is intended for polytomous patient states (i.e., >2 anesthetic levels) and can be considered as a generalization of the AUC, which was basically introduced to assess a predictor of dichotomous classes (e.g., consciousness and unconsciousness in anesthesia). Dichotomous paradigms provide equal values of P(K) and AUC test statistics. In the present investigation, we introduce a user-friendly computer program for computing P(K) and estimating reliable bootstrap confidence intervals. It is designed for multiple comparisons of the performance of depth of anesthesia indicators. Additionally, for dichotomous classes, the program plots the Receiver Operating Characteristic graph completing information obtained from P(K) or AUC, respectively. In clinical investigations, both measures are applied for indicator assessment, where ambiguous usage and interpretation may be a consequence. Therefore, a summary of the concepts of P(K) and AUC including brief and easily understandable proof of their equality is presented in the text. The exposure introduces readers to the algorithms of the provided computer program and is intended to make standardized performance tests of depth of anesthesia indicators available to medical researchers.
Margaret S Pepe - One of the best experts on this subject based on the ideXlab platform.
-
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.
-
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.
-
accommodating covariates in Receiver Operating Characteristic analysis
Stata Journal, 2009Co-Authors: Holly Janes, Gary Longton, Margaret S PepeAbstract:Classification accuracy is the ability of a marker or diagnostic test to discriminate between two groups of individuals, cases and controls, and is com- monly summarized by using the Receiver Operating Characteristic (ROC )c urve. In studies of classification accuracy, there are often covariates that should be incor- porated into the ROC analysis. We describe three ways of using covariate infor- mation. For factors that affect marker observations among controls, we present a method for covariate adjustment. For factors that affect discrimination (i.e., the ROC curve), we describe methods for modeling the ROC curve as a function of covariates. Finally, for factors that contribute to discrimination, we propose combining the marker and covariate information, and we ask how much discrimi- natory accuracy improves (in incremental value) with the addition of the marker to the covariates. These methods follow naturally when representing the ROC curve as a summary of the distribution of case marker observations, standardized with respect to the control distribution.
-
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 …
-
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.
