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

Statistics in biomedical research

The discipline of biostatistics is nowadays a fundamental scientific component of biomedical, public health and health services research. Traditional and emerging areas of application include clinical trials research, observational studies, physiology, imaging, and genomics. The present article reviews the current situation of biostatistics, considering the statistical methods traditionally used in biomedical research, as well as the ongoing development of new methods in response to the new problems arising in medicine. Clearly, the successful application of statistics in biomedical research requires appropriate training of biostatisticians. This training should aim to give due consideration to emerging new areas of statistics, while at the same time retaining full coverage of the fundamentals of statistical theory and methodology. In addition, it is important that students of biostatistics receive formal training in relevant biomedical disciplines, such as epidemiology, clinical trials, molecular biology, genetics, and neuroscience.La Bioestadística es hoy en día una componente científica fundamental de la investigación en Biomedicina, salud pública y servicios de salud. Las áreas tradicionales y emergentes de aplicación incluyen ensayos clínicos, estudios observacionales, fisología, imágenes, y genómica. Este artículo repasa la situación actual de la Bioestadística, considerando los métodos estadísticos usados tradicionalmente en investigación biomédica, así como los recientes desarrollos de nuevos métodos, para dar respuesta a los nuevos problemas que surgen en Medicina. Obviamente, la aplicación fructífera de la estadística en investigación biomédica exige una formación adecuada de los bioestadísticos, formación que debería tener en cuenta las áreas emergentes en estadística, cubriendo al mismo tiempo los fundamentos de la teoría estadística y su metodología. Es importante, además, que los estudiantes de bioestadística reciban formación en otras disciplinas biomédicas relevantes, como epidemiología, ensayos clínicos, biología molecular, genética y neurociencia

Alternatives to the Cox model in multi-state models

The introduction of time-dependent covariates in the survival process can make the patients survival change from one time point to the next as the values of the covariate change. A popular choice for the analysis of this data is the timedependent Cox regression model. In the present work we present multi-state models as an alternative for the analysis of such data

Mesa redonda: La Estadística en la Investigación Médica

Este artículo es una transcripción de las conferencias dictadas en la mesa organizada en el 4º Congreso Galego de Estatistica y Investigación de Operacions que tuvo lugar en Santiago de Compostela en noviembre de 1999. Los autores discuten sobre el posible uso o abuso de la estadística en artículos científicos, sobre lo que se necesitaría para alcanzar la interdisciplinariedad y lo que se entiende por éxito profesional. Se define la disciplina (bioestadística) y se identifica a sus profesionales (bioestadísticos). Se discute sobre el papel de un bioestadístico en un equipo de investigación médico y se repasan las dificultades que tienen los médicos para realizar estudios clínico-epidemiológicos

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

Analysing visual receptive fields through generalised additive models with interactions

Visual receptive fields (RFs) are small areas of the visual field where a stimulus induces a responses of a particular neuron from the visual system. RFs can be mapped using reverse crosscorrelation technique, which produces raw matrices containing both spatial and temporal information about the RF. Though this technique is frequently used in electrophysiological experiments, it does not allow formal comparisons between RFs obtained under different experimental conditions. In this paper we propose the use of Generalised Additive Models (GAM) including complex interactions, to obtain smoothed spatio-temporal versions of RFs. Moreover, the proposed methodology also allow for the statistical comparisons of the RFs obtained across various experimental conditions. Data analysed here derive from studies of neurons' activity in the visual cortex of behaving monkeys. Our results suggest that the GAM-based technique proposed in this paper can be a flexible and powerful tool for assessing receptive field properties

Flexible geoadditive survival analysis of non-Hodgkin lymphoma in Peru

Knowledge of prognostic factors is an important task for the clinical management of Non Hodgkin Lymphoma (NHL). In this work, we study the variables affecting survival of NHL in Peru by means of geoadditive Cox-type structured hazard regression models while accounting for potential spatial correlations in the survival times. We identified eight covariates with significant effect for overall survival. Some of them are widely known such as age, performance status, clinical stage and lactic dehydrogenase, but we also identified hemoglobin, leukocytes and lymphocytes as covariates with a significant effect on the overall survival of patients with NHL. Besides, the effect of continuous covariates is clearly nonlinear and hence impossible to detect with the classical Cox method. Although the spatial component does not show a significant effect, the results show a trend of low risk in certain areas

Flexible quantile regression models: application to the study of the purple sea urchin

In many applications, it is often of interest to assess the possible relationships between covariates and quantiles of a response variable through a regression model. In some instances, the effects of continuous covariates on the outcome are highly nonlinear. Consequently, appropriate modelling has to take such flexible smooth effects into account. In this work, various flexible quantile regression techniques were reviewed and compared by simulation. Finally, all the techniques were used to construct the overall zone specific reference curves of morphologic measures of sea urchin Paracentrotus lividus (Lamarck, 1816) located in NW SpainPeer Reviewe

Flexible quantile regression models : application to the study of the purple sea urchin

In many applications, it is often of interest to assess the possible relationships between covariates and quantiles of a response variable through a regression model. In some instances, the effects of continuous covariates on the outcome are highly nonlinear. Consequently, appropriate modelling has to take such flexible smooth effects into account. In this work, various flexible quantile regression techniques were reviewed and compared by simulation. Finally, all the techniques were used to construct the overall zone specific reference curves of morphologic measures of sea urchin Paracentrotus lividus (Lamarck, 1816) located in NW Spain

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