Optimal cutoff points for classification in diagnostic studies: new contributions and software development

Continuous diagnostic tests (biomarkers or risk markers) are often used to discriminate between healthy and diseased populations. For the clinical application of such tests, the key aspect is how to select an appropriate cutpoint or discrimination value c that defines positive and negative test results. In general, individuals with a diagnostic test value smaller than c are classified as healthy and otherwise as diseased. In the literature, several methods have been proposed to select the threshold value c in terms of different specific criteria of optimality. Among others, one of the methods most used in clinical practice is the Symmetry point that maximizes simultaneously both types of correct classifications. From a graphical viewpoint, the Symmetry point is associated to the operating point on the Receiver Operating Characteristic (ROC) curve that intersects the diagonal line passing through the points (0,1) and (1,0). However, this cutpoint is actually valid only when the error of misclassifying a diseased patient has the same severity than the error of misclassifying a healthy patient. Since this may not be the case in practice, an important issue in order to assess the clinical effectiveness of a biomarker is to take into account the costs associated with the decisions taken when selecting the threshold value. Moreover, to facilitate the task of selecting the optimal cut-off point in clinical practice, it is essential to have software that implements the existing optimal criteria in an user-friendly environment. Another interesting issue appears when the marker shows an irregular distribution, with a dominance of diseased subjects in noncontiguous regions. Using a single cutpoint, as common practice in traditional ROC analysis, would not be appropriate for these scenarios because it would lead to erroneous conclusions, not taking full advantage of the intrinsic classificatory capacity of the marke

小学教育背景下与网络暴力目击者相关的危险因素

Cyberbullying is a harmful and intentional act of an aggressor/s to a victim, through technology, which causes an imbalance of power. The role of bystanders is key for early intervention in the phenomenon. The objective of the study is to detect risk factors associated with cyberbystanders in Primary Education based on individual variables related to the use of technologies (number of technologies, type of technology, frequency, purpose of use, time slot, and place of connection) and experiences as victims or aggressors of cyberbullying. A sample of 1169 families whose children were in 5th or 6th grade of Primary Education was selected and surveyed using a self-administered questionnaire that measures all the indicated variables (α = .84). The study of the risk factors was carried out using binary logistic regression (bivariate models and multivariate model) with the software R version 4.1.0. Bivariate analyses identified: a) using a mobile phone with the Internet, b) Internet connection to talk with friends, c) cybervictimization, and d) cyberperpetration as possible individual risk factors (p < .05). The multivariate model showed joint predictors of the risk of being cyberbystanders in Primary Education: cyberperpetration, cybervictimization, number of technologies used and using the Internet to talk with friends. The interrelation between the roles of cyberbullying and the risk derived from the very frequent use of various technological devices is evidenced. Implications for educational practice are studied.El ciberacoso es un acto dañino e intencional de un agresor/es a una víctima, mediante las tecnologías, que provoca un desequilibrio de poder. El papel de los testigos es clave a la hora de intervenir de manera temprana en el fenómeno. El objetivo del estudio es detectar factores de riesgo asociados con los cibertestigos en Educación Primaria a partir de las variables individuales relacionadas con el uso de las tecnologías (número de tecnologías, tipo de tecnología, frecuencia y finalidad de uso, franja horaria y lugar de conexión) y las experiencias como víctimas o agresores de ciberacoso.  Se seleccionó una muestra de 1169 familias cuyos hijos cursaban 5º o 6º de Educación Primaria, encuestada mediante un cuestionario autoadministrado que mide todas las variables indicadas (α = .84). El estudio de los factores de riesgo se llevó a cabo mediante la regresión logística binaria (modelos bivariantes y modelo multivariante). Los análisis bivariantes identificaron: a) uso del teléfono móvil con Internet, b) conexión a Internet para hablar con amigos, c) ser víctima de ciberacoso y d) ser ciberperpetrador como posibles factores de riesgo individuales (p < .05). El modelo multivariante mostró como predictores conjuntos del riesgo de ser cibertestigos en Educación Primaria: ser ciberagresor, ser víctima de ciberacoso, número de tecnologías empleadas y usar Internet para hablar con amigos. Se evidencia la interrelación de los roles de ciberacoso y el riesgo derivado del uso muy frecuente de varios dispositivos tecnológicos. Se estudian las implicaciones para la práctica educativa.O cyberbullying é um ato prejudicial e intencional de um agressor ou uma agressora a uma vítima, através das tecnologias, que provoca um desequilíbrio de poder. O papel das testemunhas é fundamental para uma intervenção precoce no fenómeno. O objetivo do estudo é detetar fatores de risco associados ao cyberbullying no Ensino Primário a partir das variáveis individuais relacionadas com a utilização das tecnologias (número de tecnologias, tipo de tecnologia, frequência e finalidade de utilização, intervalo horário e local de ligação) e as experiências como vítimas ou agressores de cyberbullying. Selecionou-se uma amostra de 1169 famílias cujos filhos estavam no 5.º ou 6.º ano do Ensino Básico, que foram inquiridas utilizando um questionário autoadministrado que mede todas as variáveis indicadas (α = .84). O estudo dos fatores de risco foi realizado mediante a regressão logística binária (modelos bivariantes e modelo multivariante). As análises bivariantes identificaram: a) uso do telemóvel com Internet, b) ligação à Internet para falar com amigos, c) ser vítima de cyberbullying e d) ser um ciberperpetrador como possíveis fatores de risco individuais (p < .05). O modelo multivariante mostrou como preditores conjuntos do risco de ser um testemunhas de cyberbullying no Ensino Primário: ser ciberagressor, ser vítima de cyberbullying, número de tecnologias utilizadas e utilizar a Internet para falar com amigos. A inter-relação dos papéis de cyberbullying e o risco derivado da utilização muito frequente de vários dispositivos tecnológicos é evidente. As implicações para a prática educativa são estudadas.O cyberbullying é um ato prejudicial e intencional de um agressor ou uma agressora a uma vítima, através das tecnologias, que provoca um desequilíbrio de poder. O papel das testemunhas é fundamental para uma intervenção precoce no fenómeno. O objetivo do estudo é detetar fatores de risco associados ao cyberbullying no Ensino Primário a partir das variáveis individuais relacionadas com a utilização das tecnologias (número de tecnologias, tipo de tecnologia, frequência e finalidade de utilização, intervalo horário e local de ligação) e as experiências como vítimas ou agressores de cyberbullying. Selecionou-se uma amostra de 1169 famílias cujos filhos estavam no 5.º ou 6.º ano do Ensino Básico, que foram inquiridas utilizando um questionário autoadministrado que mede todas as variáveis indicadas (α = .84). O estudo dos fatores de risco foi realizado mediante a regressão logística binária (modelos bivariantes e modelo multivariante). As análises bivariantes identificaram: a) uso do telemóvel com Internet, b) ligação à Internet para falar com amigos, c) ser vítima de cyberbullying e d) ser um ciberperpetrador como possíveis fatores de risco individuais (p < .05). O modelo multivariante mostrou como preditores conjuntos do risco de ser um testemunhas de cyberbullying no Ensino Primário: ser ciberagressor, ser vítima de cyberbullying, número de tecnologias utilizadas e utilizar a Internet para falar com amigos. A inter-relação dos papéis de cyberbullying e o risco derivado da utilização muito frequente de vários dispositivos tecnológicos é evidente. As implicações para a prática educativa são estudadas.网络暴力是施暴者通过科技手段，对受害者进行的有意图的危害行径，网络暴力直接导致了权利平衡的偏失。在对网络暴力的早期干预中目击者的角色至关重要。目标：该研究的目的是通过与科技使用相关的独立变量（科技数量、科技种类、使用频率和目的、连接时间段和地址）来发现小学背景下与网络暴力目击者相关的危险因素，同时发现作为网络暴力施暴者或受害者的相关经历。我们选择了由1169个家庭组成的样本，这些家庭的共同特点是他们的孩子正处于小学的五、六年级。我们采用自编问卷对样本的上述变量进行测量（α = 0.84）。对危险因素的研究采用了二元逻辑回归（双变量模型和多变量模型）。通过双变量分析确定：（1）使用联网的手机；（2）联网与朋友沟通；（3）是网络暴力的受害者；（4）是网络暴力的施暴者，这四项可能的独立危险因素（p <0 .05）。多变量模型显示了在小学背景下作为网络暴力目击者所面临危险的共同预测因素：是网络暴力施暴者、是网络暴力的受害者、科技数量和使用网络跟朋友交流。一方面研究显示出了网络暴力不同角色的相互关系、多科技设备的频繁使用所带来的风险都是十分突出的问题，另一方面也探讨了该研究在教育实践中的应用

GsymPoint: An R Package to Estimate the Generalized Symmetry Point, an Optimal Cut-off Point for Binary Classification in Continuous Diagnostic Tests

In clinical practice, it is very useful to select an optimal cutpoint in the scale of a continuous biomarker or diagnostic test for classifying individuals as healthy or diseased. Several methods for choosing optimal cutpoints have been presented in the literature, depending on the ultimate goal. One of these methods, the generalized symmetry point, recently introduced, generalizes the symmetry point by incorporating the misclassification costs. Two statistical approaches have been proposed in the literature for estimating this optimal cutpoint and its associated sensitivity and specificity measures, a parametric method based on the generalized pivotal quantity and a nonparametric method based on empirical likelihood. In this paper, we introduce GsymPoint, an R package that implements these methods in a user-friendly environment, allowing the end-user to calculate the generalized symmetry point depending on the levels of certain categorical covariates. The practical use of this package is illustrated using three real biomedical datasetsThis research has been supported by several Grants from the Spanish Ministry of Science and Innovation. M. López-Ratón and C. Cadarso-Suárez acknowledge support to MTM2011-15849-E, MTM2011-28285-C02-00, MTM2014-52975-C2-1-R and MTM2015-69068-REDT. E.M. Molanes-López acknowledges support to MTM2011-28285-C02-02, ECO2011-25706, MTM2011-15849-E and MTM2015-69068-REDT. E. Letón acknowledges support to MTM2011-15849-E, MTM2011-28285-C02-02, PI13/02446 and MTM2015-69068-REDTS

Application of Generalized Additive Models to the Evaluation of Continuous Markers for Classification Purposes

Background: Receiver operating characteristic (ROC) curve and derived measures as the Area Under the Curve (AUC) are often used for evaluating the discriminatory capability of a continuous biomarker in distinguishing between alternative states of health. However, if the marker shows an irregular distribution, with a dominance of diseased subjects in noncontiguous regions, classification using a single cutpoint is not appropriate, and it would lead to erroneous conclusions. This study sought to describe a procedure for improving the discriminatory capacity of a continuous biomarker, by using generalized additive models (GAMs) for binary data.Methods: A new classification rule is obtained by using logistic GAM regression models to transform the original biomarker, with the predicted probabilities being the new transformed continuous biomarker. We propose using this transformed biomarker to establish optimal cut-offs or intervals on which to base the classification. This methodology is applied to different controlled scenarios, and to real data from a prospective study of patients undergoing surgery at a University Teaching Hospital, for examining plasma glucose as postoperative infection biomarker.Results: Both, theoretical scenarios and real data results show that when the risk marker-disease relationship is not monotone, using the new transformed biomarker entails an improvement in discriminatory capacity. Moreover, in these situations, an optimal interval seems more reasonable than a single cutpoint to define lower and higher disease-risk categories.Conclusions: Using statistical tools which allow for greater flexibility (e.g., GAMs) can optimize the classificatory capacity of a potential marker using ROC analysis. So, it is important to question linearity in marker-outcome relationships, in order to avoid erroneous conclusions

I-MATH map of company demand for mathematical technology: TransMATH

This document details a Spanish prospectus on the level of knowledge, use and demand for mathematical technology by commercial companies. It is aimed at detecting problems in the corporate field for which Mathematicians could provide the complementary or fundamental tools, determine the demand for mathematical training, and define where necessary new lines of research in Mathematics aimed at solving these problem

Assessment of Corneal Epithelial Thickness in Asymmetric Keratoconic Eyes and Normal Eyes Using Fourier Domain Optical Coherence Tomography

Purpose. To compare the characteristics of asymmetric keratoconic eyes and normal eyes by Fourier domain optical coherence tomography (OCT) corneal mapping. Methods. Retrospective corneal and epithelial thickness OCT data for 74 patients were compared in three groups of eyes: keratoconic (n=22) and normal fellow eyes (n=22) in patients with asymmetric keratoconus and normal eyes (n=104) in healthy subjects. Areas under the curve (AUC) of receiver operator characteristic (ROC) curves for each variable were compared across groups to indicate their discrimination capacity. Results. Three variables were found to differ significantly between fellow eyes and normal eyes (all < 0.05 ): minimum corneal thickness, thinnest corneal point, and central corneal thickness. These variables combined showed a high discrimination power to differentiate fellow eyes from normal eyes indicated by an AUC of 0.840 (95% CI: 0.762–0.918). Conclusions. Our findings indicate that topographically normal fellow eyes in patients with very asymmetric keratoconus differ from the eyes of healthy individuals in terms of their corneal epithelial and pachymetry maps. This type of information could be useful for an early diagnosis of keratoconus in topographically normal eyesS

Mapa i-MATH de demanda empresarial de tecnología matemática: Transmath

Este documento recoge una prospectiva nacional sobre el grado de conocimiento, de utilización y de demanda de tecnología matemática en la empresa. El documento trata de detectar problemas empresariales en los que las Matemáticas puedan ser una herramienta complementaria o fundamental, conocer la demanda de formación matemática y definir, si es necesario, nuevas líneas de investigación en Matemáticas orientadas a resolver estos problema

OptimalCutpoints: An R Package for Selecting Optimal Cutpoints in Diagnostic Tests

Continuous diagnostic tests are often used for discriminating between healthy and diseased populations. For the clinical application of such tests, it is useful to select a cutpoint or discrimination value c that defines positive and negative test results. In general, individuals with a diagnostic test value of c or higher are classified as diseased. Several search strategies have been proposed for choosing optimal cutpoints in diagnostic tests, depending on the underlying reason for this choice. This paper introduces an R package, known as OptimalCutpoints, for selecting optimal cutpoints in diagnostic tests. It incorporates criteria that take the costs of the different diagnostic decisions into account, as well as the prevalence of the target disease and several methods based on measures of diagnostic test accuracy. Moreover, it enables optimal levels to be calculated according to levels of given (categorical) covariates. While the numerical output includes the optimal cutpoint values and associated accuracy measures with their confidence intervals, the graphical output includes the receiver operating characteristic (ROC) and predictive ROC curves. An illustration of the use of OptimalCutpoints is provided, using a real biomedical dataset