Analysing Qualitative Data

Slides and Handouts for class introducing some of the concepts associated with the analysis of qualitative data

Existen diferencias farmacocinéticas y tiempos de despertar entre mujeres y hombres con anestesia total intravenosa

Introducción: La anestesia total intravenosa (TIVA) es ampliamente usada y reportada en la literatura como técnica para disminuir la respuesta a la laringoscopia e intubación, en la inducción y mantenimiento de una adecuada anestesia, además de una mejor estabilidad hemodinámica y recuperación pos anestésica; sin embargo no existen un gran número de estudios que comparen el uso de TIVA, determinando si existen diferencias en el perfil farmacocinético según el género del paciente. Objetivo: Describir diferencias farmacocinéticas y de los tiempos de despertar y salida a la unidad de cuidados pos anestésicos (descarga), según el género; en pacientes que reciben TIVA, con remifentanil y propofol, orientado por Stangraf. Metodología: Estudio observacional analítico de corte transversal, en pacientes llevados a cirugía bajo TIVA en el Hospital Occidente de Kennedy en el periodo de junio de 2013 a Enero de 2014.Usando SPSS versión 20 Windows, se analizaron los datos mediante pruebas Kolmogorov-Smirnov y Shapiro-Wilk y U de Mann Withney. Un valor de p menor 0.05 fue aceptado como estadísticamente significativo. Resultados: Se aplicaron pruebas de normalidad y no se encontraron diferencias estadísticamente significativas entre género. El tiempo de despertar fue 9.36 minutos para mujeres y 11.26 minutos para hombres. Los tiempos de descarga fueron 10.71 minutos para mujeres y 12.82 minutos para hombres. Discusión. El tiempo de despertar y descarga no es diferente entre mujeres y hombres en los pacientes analizados. Se requieren estudios adicionales entre grupos poblacionales de diversas condiciones farmacocineticas para corroborar los datos.

Análisis predictivo de los eventos adversos relacionados con los indicadores de eficiencia hospitalaria de una IPS de Bogotá

En este estudio se realizó un análisis predictivo de la aparición de eventos adversos de los pacientes de una IPS de Bogotá, Mederi Hospital Universitario de Barrios Unidos (HUBU) durante el año 2013; relacionados con los indicadores de eficiencia hospitalaria (Porcentaje de ocupación hospitalaria, número de egresos hospitalarios, promedio de estancia hospitalaria, número de egresos de urgencias, promedio de estancia en urgencias). Los datos fueron exportados a una matriz de análisis de las variables cualitativas; fueron presentadas con frecuencias absolutas y relativas, las variables cuantitativas (edad, tiempos de estancia) fueron presentadas con media, desviaciones estándar. Se agruparon los datos de eventos adversos y de eficiencia hospitalaria en una nueva matriz que permitiera el análisis predictivo la nueva matriz fue exportada al software de modelación estadístico Eviews 6.5; se especificaron modelos predictivos multivariados para la variable número de eventos adversos, respecto de los indicadores de eficiencia hospitalaria y se estimaron las probabilidades de ocurrencia, análisis de correlación y multicolinealidad; los resultados se presentaron en tablas de estimación para cada modelo, se restringieron los eventos adversos prevenibles y no prevenibles información obtenida a través de un sistema de información que registra los factores relacionados con la ocurrencia de eventos adversos en salud, a través del sistema de reporte de eventos en salud, reporte en las historias clínicas, reporte individual, reporte por servicio, análisis de datos y estudios de caso, de la misma forma fueron extraídos los datos de eficiencia hospitalaria para el mismo periodo. El análisis y gestión de eventos adversos pretende establecer estrategias de mejoramiento continuo y análisis de resultados frente a los indicadores de eficiencia que permitan intervención de los factores de riesgo operativo de los servicios del Hospital Universitario de Barrios Unidos (HUBU), relacionados con eventos adversos en la atención de los pacientes en especial se debe enfocar en la gestión de los egresos de pacientes de acuerdo a los resultados obtenidos con el fin de alinearse y fortalecer las políticas de seguridad del paciente para brindar una atención integral con calidad y eficiencia, disminuyendo las quejas en la atención, las glosas, los riesgos jurídicos, de acuerdo al modelo predictivo estudiado.

Implementación de cópulas para la estimación del valor en riesgo

La dependencia entre las series financieras, es un parámetro fundamental para la estimación de modelos de Riesgo. El Valor en Riesgo (VaR) es una de las medidas más importantes utilizadas para la administración y gestión de Riesgos Financieros, en la actualidad existen diferentes métodos para su estimación, como el método por simulación histórica, el cual no asume ninguna distribución sobre los retornos de los factores de riesgo o activos, o los métodos paramétricos que asumen normalidad sobre las distribuciones. En este documento se introduce la teoría de cópulas, como medida de dependencia entre las series, se estima un modelo ARMA-GARCH-Cópula para el cálculo del Valor en Riesgo de un portafolio compuesto por dos series financiera, la tasa de cambio Dólar-Peso y Euro-Peso. Los resultados obtenidos muestran que la estimación del VaR por medio de copulas es más preciso en relación a los métodos tradicionales.

Eye tracking data representation and visualization : on information and communication studies at CETAC.MEDIA

Eye tracking has become a preponderant technique in the evaluation of user interaction and behaviour with study objects in defined contexts. Common eye tracking related data representation techniques offer valuable input regarding user interaction and eye gaze behaviour, namely through fixations and saccades measurement. However, these and other techniques may be insufficient for the representation of acquired data in specific studies, namely because of the complexity of the study object being analysed. This paper intends to contribute with a summary of data representation and information visualization techniques used in data analysis within different contexts (advertising, websites, television news and video games). Additionally, several methodological approaches are presented in this paper, which resulted from several studies developed and under development at CETAC.MEDIA - Communication Sciences and Technologies Research Centre. In the studies described, traditional data representation techniques were insufficient. As a result, new approaches were necessary and therefore, new forms of representing data, based on common techniques were developed with the objective of improving communication and information strategies. In each of these studies, a brief summary of the contribution to their respective area will be presented, as well as the data representation techniques used and some of the acquired results.

Sharing and visualizing environmental data using Virtual Globes

Virtual globe technology holds many exciting possibilities for environmental science. These easy-to-use, intuitive systems provide means for simultaneously visualizing four-dimensional environmental data from many different sources, enabling the generation of new hypotheses and driving greater understanding of the Earth system. Through the use of simple markup languages, scientists can publish and consume data in interoperable formats without the need for technical assistance. In this paper we give, with examples from our own work, a number of scientific uses for virtual globes, demonstrating their particular advantages. We explain how we have used Web Services to connect virtual globes with diverse data sources and enable more sophisticated usage such as data analysis and collaborative visualization. We also discuss the current limitations of the technology, with particular regard to the visualization of subsurface data and vertical sections.

An extension of an over-dispersion test for count data

While over-dispersion in capture–recapture studies is well known to lead to poor estimation of population size, current diagnostic tools to detect the presence of heterogeneity have not been specifically developed for capture–recapture studies. To address this, a simple and efficient method of testing for over-dispersion in zero-truncated count data is developed and evaluated. The proposed method generalizes an over-dispersion test previously suggested for un-truncated count data and may also be used for testing residual over-dispersion in zero-inflation data. Simulations suggest that the asymptotic distribution of the test statistic is standard normal and that this approximation is also reasonable for small sample sizes. The method is also shown to be more efficient than an existing test for over-dispersion adapted for the capture–recapture setting. Studies with zero-truncated and zero-inflated count data are used to illustrate the test procedures.

Bayesian analysis of an inverse Gaussian correlated frailty model

In survival analysis frailty is often used to model heterogeneity between individuals or correlation within clusters. Typically frailty is taken to be a continuous random effect, yielding a continuous mixture distribution for survival times. A Bayesian analysis of a correlated frailty model is discussed in the context of inverse Gaussian frailty. An MCMC approach is adopted and the deviance information criterion is used to compare models. As an illustration of the approach a bivariate data set of corneal graft survival times is analysed. (C) 2006 Elsevier B.V. All rights reserved.

Understanding the indoor environment through mining sensory data - A case study

A wireless sensor network (WSN) is a group of sensors linked by wireless medium to perform distributed sensing tasks. WSNs have attracted a wide interest from academia and industry alike due to their diversity of applications, including home automation, smart environment, and emergency services, in various buildings. The primary goal of a WSN is to collect data sensed by sensors. These data are characteristic of being heavily noisy, exhibiting temporal and spatial correlation. In order to extract useful information from such data, as this paper will demonstrate, people need to utilise various techniques to analyse the data. Data mining is a process in which a wide spectrum of data analysis methods is used. It is applied in the paper to analyse data collected from WSNs monitoring an indoor environment in a building. A case study is given to demonstrate how data mining can be used to optimise the use of the office space in a building.

Efficient modeling and inference for event-related fMRI data

Event-related functional magnetic resonance imaging (efMRI) has emerged as a powerful technique for detecting brains' responses to presented stimuli. A primary goal in efMRI data analysis is to estimate the Hemodynamic Response Function (HRF) and to locate activated regions in human brains when specific tasks are performed. This paper develops new methodologies that are important improvements not only to parametric but also to nonparametric estimation and hypothesis testing of the HRF. First, an effective and computationally fast scheme for estimating the error covariance matrix for efMRI is proposed. Second, methodologies for estimation and hypothesis testing of the HRF are developed. Simulations support the effectiveness of our proposed methods. When applied to an efMRI dataset from an emotional control study, our method reveals more meaningful findings than the popular methods offered by AFNI and FSL. (C) 2008 Elsevier B.V. All rights reserved.

Obesity and health in Europeans aged 50 years and older

Background: Obesity is increasing globally across all population groups. Limited data are available on how obesity patterns differ across countries. Objective: To document the prevalence of obesity and related health conditions for Europeans aged 50 years and older, and to estimate the association between obesity and health outcomes across 10 European countries. Methods: Data were obtained from the 2004 Survey of Health, Ageing and Retirement in Europe, a cross-national survey of 22 777 Continental Europeans over the age of 50 years. The health outcomes included self-reported health, disability, doctor-diagnosed chronic health conditions and depression. Multivariate regression analysis was used to predict health outcomes across weight classes (defined by body mass index [BMI] from self-reported weight and height) in the pooled sample and individually in each country. Results: The prevalence of obesity (BMI >= 30) ranged from 12.8% in Sweden to 20.2% in Spain for men and from 12.3% in Switzerland to 25.6% in Spain for women. Adjusting for compositional differences across countries changed little in the observed large heterogeneity in obesity rates throughout Europe. Compared with normal weight individuals, men and women with greater BMI had significantly higher risks for all chronic health conditions examined except heart disease in overweight men. Depression was linked to obesity in women only. Particularly pronounced risks of impaired health and chronic health conditions were found among severely obese people. The effects of obesity on health did not vary significantly across countries. Conclusions: Cross-country differences in the prevalence of obesity in older Europeans are substantial and exceed socio-demographic differentials in excessive body weight. Obesity is associated with significantly poorer health outcomes among Europeans aged 50 years and over, with effects similar across countries. Large heterogeneity in obesity throughout Europe should be investigated further to identify areas for effective public policy. (C) 2007 Published by Elsevier Ltd on behalf of The Royal Institute of Public Health.

Jpowder: a Java-based program for the display and examination of powder diffraction data

The ability to display and inspect powder diffraction data quickly and efficiently is a central part of the data analysis process. Whilst many computer programs are capable of displaying powder data, their focus is typically on advanced operations such as structure solution or Rietveld refinement. This article describes a lightweight software package, Jpowder, whose focus is fast and convenient visualization and comparison of powder data sets in a variety of formats from computers with network access. Jpowder is written in Java and uses its associated Web Start technology to allow ‘single-click deployment’ from a web page, http://www.jpowder.org. Jpowder is open source, free and available for use by anyone.

Energy and protein interactions and their effect on nitrogen excretion in dairy cows

The principal driver of nitrogen (N) losses from the body including excretion and secretion in milk is N intake. However, other covariates may also play a role in modifying the partitioning of N. This study tests the hypothesis that N partitioning in dairy cows is affected by energy and protein interactions. A database containing 470 dairy cow observations was collated from calorimetry experiments. The data include N and energy parameters of the diet and N utilization by the animal. Univariate and multivariate meta-analyses that considered both within and between study effects were conducted to generate prediction equations based on N intake alone or with an energy component. The univariate models showed that there was a strong positive linear relationships between N intake and N excretion in faeces, urine and milk. The slopes were 0.28 faeces N, 0.38 urine N and 0.20 milk N. Multivariate model analysis did not improve the fit. Metabolizable energy intake had a significant positive effect on the amount of milk N in proportion to faeces and urine N, which is also supported by other studies. Another measure of energy considered as a covariate to N intake was diet quality or metabolizability (the concentration of metabolizable energy relative to gross energy of the diet). Diet quality also had a positive linear relationship with the proportion of milk N relative to N excreted in faeces and urine. Metabolizability had the largest effect on faeces N due to lower protein digestibility of low quality diets. Urine N was also affected by diet quality and the magnitude of the effect was higher than for milk N. This research shows that including a measure of diet quality as a covariate with N intake in a model of N execration can enhance our understanding of the effects of diet composition on N losses from dairy cows. The new prediction equations developed in this study could be used to monitor N losses from dairy systems.

Extracting quantitative structural parameters for disordered polymers from neutron scattering data

The organization of non-crystalline polymeric materials at a local level, namely on a spatial scale between a few and 100 a, is still unclear in many respects. The determination of the local structure in terms of the configuration and conformation of the polymer chain and of the packing characteristics of the chain in the bulk material represents a challenging problem. Data from wide-angle diffraction experiments are very difficult to interpret due to the very large amount of information that they carry, that is the large number of correlations present in the diffraction patterns.We describe new approaches that permit a detailed analysis of the complex neutron diffraction patterns characterizing polymer melts and glasses. The coupling of different computer modelling strategies with neutron scattering data over a wide Q range allows the extraction of detailed quantitative information on the structural arrangements of the materials of interest. Proceeding from modelling routes as diverse as force field calculations, single-chain modelling and reverse Monte Carlo, we show the successes and pitfalls of each approach in describing model systems, which illustrate the need to attack the data analysis problem simultaneously from several fronts.