USING THE R PACKAGE crlmm FOR GENOTYPING AND COPY NUMBER ESTIMATION

Genotyping platforms such as Affymetrix can be used to assess genotype-phenotype as well as copy number-phenotype associations at millions of markers. While genotyping algorithms are largely concordant when assessed on HapMap samples, tools to assess copy number changes are more variable and often discordant. One explanation for the discordance is that copy number estimates are susceptible to systematic differences between groups of samples that were processed at different times or by different labs. Analysis algorithms that do not adjust for batch effects are prone to spurious measures of association. The R package crlmm implements a multilevel model that adjusts for batch effects and provides allele-specific estimates of copy number. This paper illustrates a workflow for the estimation of allele-specific copy number, develops markerand study-level summaries of batch effects, and demonstrates how the marker-level estimates can be integrated with complimentary Bioconductor software for inferring regions of copy number gain or loss. All analyses are performed in the statistical environment R. A compendium for reproducing the analysis is available from the author’s website (http://www.biostat.jhsph.edu/~rscharpf/crlmmCompendium/index.html).

La divulgación científica de análisis sensorial de vinos en soporte web

En esta tesis se trabaja sobre la hipótesis de que el componente didáctico del discurso divulgativo queda delimitado por estrategias discursivas originadas en el tratamiento modal y actualizadas en los niveles funcional, situacional, semántico y formal-gramatical. El objetivo es caracterizar tales estrategias para identificar tendencias en la realización lingüísticodiscursiva del componente didáctico. El corpus se ha formado teniendo en cuenta soporte (web), formato (hipertexto) y dominio disciplinar (Análisis Sensorial de Vinos). La metodología es, fundamentalmente, cualitativo-ejemplar, basada en el modelo multinivel propuesto por Ciapuscio (2003) para el análisis de textos especializados. Los resultados sugieren que en el nivel funcional, el componente didáctico se distingue por el predominio de los términos positivos de las categorías modales epistémica (función informar) y ética (función dirigir); en el nivel situacional, por tres tipos de construcciones discursivas: la del enunciador experto, la del enunciatario lego y la de la pertenencia del lego a la comunidad especializada; en el nivel semántico, por la estandarización de partes textuales y por el predominio tanto de axiologización eufórica ética y cognoscitiva, como de secuencias expositivas y de procedimientos explicativos causales, descriptivos e ilustrativos; en el nivel formal, por recursos paratextuales e hipertextuales que refuerzan la actualización del componente didáctico.

Testing alternative models for the conservation of koalas in fragmented rural-urban landscapes

Predicting the various responses of different species to changes in landscape structure is a formidable challenge to landscape ecology. Based on expert knowledge and landscape ecological theory, we develop five competing a priori models for predicting the presence/absence of the Koala (Phascolarctos cinereus) in Noosa Shire, south-east Queensland (Australia). A priori predictions were nested within three levels of ecological organization: in situ (site level) habitat (< 1 ha), patch level (100 ha) and landscape level (100-1000 ha). To test the models, Koala surveys and habitat surveys (n = 245) were conducted across the habitat mosaic. After taking into account tree species preferences, the patch and landscape context, and the neighbourhood effect of adjacent present sites, we applied logistic regression and hierarchical partitioning analyses to rank the alternative models and the explanatory variables. The strongest support was for a multilevel model, with Koala presence best predicted by the proportion of the landscape occupied by high quality habitat, the neighbourhood effect, the mean nearest neighbour distance between forest patches, the density of forest patches and the density of sealed roads. When tested against independent data (n = 105) using a receiver operator characteristic curve, the multilevel model performed moderately well. The study is consistent with recent assertions that habitat loss is the major driver of population decline, however, landscape configuration and roads have an important effect that needs to be incorporated into Koala conservation strategies.

O DESEMPENHO DAS EMPRESAS DO GRANDE ABC DE 2001 A 2008

Essa pesquisa investiga empiricamente o desempenho das empresas do Grande ABC, região industrializada e cada vez mais representativa economicamente para o país. As sete cidades que representam a região, Santo André, São Bernardo do Campo, São Caetano do Sul, Diadema, Mauá, Ribeirão Pires e Rio Grande da Serra, tiverem nos últimos anos um crescimento econômico consideravelmente acima do crescimento do país e seu desenvolvimento tem impulsionado o crescimento do país. A análise empírica utiliza dados em painel e investiga o desempenho das firmas das sete cidades que compõe o Grande ABC durante os anos de 2001 a 2008 utilizando a metodologia multinível e três medidas de desempenho: ROA, OROA e ROE. A metodologia multinível possibilitou a identificação dos principais efeitos que estão associados ou não ao desempenho das empresas, entre esses efeitos estão o ano, a própria empresa, o subsetor, o setor e a cidade que a empresa se localiza. Entre as três medidas de desempenho utilizadas houve significativa convergência e, além disso, o estudo identificou que há um significativo efeito no desempenho das empresas associado ao ano e à própria empresa, além de mostrar que os setores, os subsetores e a cidade que a empresa se localiza não apresentam um efeito significativo associado ao desempenho dessas firmas.

EXAMINING THE IMPACT OF TEACHER PRACTICE ON STUDENT LEARNING IN INTERACTIVE RADIO INSTRUCTION (IRI) CLASSROOMS: LESSONS FROM NEW DELHI AND RAJASTHAN (INDIA)

This quantitative study examines the impact of teacher practices on student achievement in classrooms where the English is Fun Interactive Radio Instruction (IRI) programs were being used. A contemporary IRI design using a dual-audience approach, the English is Fun IRI programs delivered daily English language instruction to students in grades 1 and 2 in Delhi and Rajasthan through 120 30-minute programs via broadcast radio (the first audience) while modeling pedagogical techniques and behaviors for their teachers (the second audience). Few studies have examined how the dual-audience approach influences student learning. Using existing data from 32 teachers and 696 students, this study utilizes a multivariate multilevel model to examine the role of the primary expectations for teachers (e.g., setting up the IRI classroom, following instructions from the radio characters and ensuring students are participating) and the role of secondary expectations for teachers (e.g., modeling pedagogies and facilitating learning beyond the instructions) in promoting students’ learning in English listening skills, knowledge of vocabulary and use of sentences. The study finds that teacher practice on both sets of expectations mattered, but that practice in the secondary expectations mattered more. As expected, students made the smallest gains in the most difficult linguistic task (sentence use). The extent to which teachers satisfied the primary and secondary expectations was associated with gains in all three skills – confirming the relationship between students’ English proficiency and teacher practice in a dual-audience program. When it came to gains in students’ scores in sentence use, a teacher whose focus was greater on primary expectations had a negative effect on student performance in both states. In all, teacher practice clearly mattered but not in the same way for all three skills. An optimal scenario for teacher practice is presented in which gains in all three skills are maximized. These findings have important implications for the way the classroom teacher is cast in IRI programs that utilize a dual-audience approach and in the way IRI programs are contracted insofar as the role of the teacher in instruction is minimized and access is limited to instructional support from the IRI lessons alone.

Réussite et sélection au premier cycle universitaire au Québec : une analyse multiniveau

L’objectif de ce mémoire est d’étudier les formes de sélectivité scolaire et les facteurs de la réussite dans les programmes de premier cycle universitaire au Québec. En s’appuyant sur les différents écrits sur le sujet, cette recherche présente les différents déterminants de la réussite scolaire ainsi que la sélectivité dans les programmes universitaires québécois. D’un point de vue méthodologique, ce mémoire utilise une base administrative de données longitudinales de l’Université de Montréal constituée de deux générations de cohortes d’étudiants de premier cycle. Sur le plan empirique, nous analysons d’abord la différenciation des programmes de premier cycle en matière de sélectivité pour en dériver un indicateur agrégé de sélectivité. Ensuite, nous étudions les facteurs individuels de réussite en contrôlant l’effet de la cote R dans un modèle multiniveau. L’analyse explicative fait apparaitre deux principaux constats originaux sur les facteurs de réussite, l’un relatif à la cote R et l’autre à l’appartenance de sexe. La cote R influence les chances de réussite des étudiants, mais en raison inverse du niveau de sélectivité à l’entrée. Dans les programmes où la sélection à l’entrée est la plus forte, les taux de diplomation sont les plus élevés et le lien entre la cote R et la note finale est très faible. En outre, le fait d’être un garçon tend à réduire les chances de réussite, mais cet effet négatif disparait quand on tient compte de la cote R ainsi que du programme d’appartenance dans un modèle multiniveau. Si les étudiantes réussissent mieux au niveau du premier cycle universitaire c’est donc surtout parce qu’elles ont eu de meilleures performances scolaires antérieurement et qu’elles ne sont pas dans les mêmes programmes que leurs homologues masculins.

Réussite et sélection au premier cycle universitaire au Québec : une analyse multiniveau

L’objectif de ce mémoire est d’étudier les formes de sélectivité scolaire et les facteurs de la réussite dans les programmes de premier cycle universitaire au Québec. En s’appuyant sur les différents écrits sur le sujet, cette recherche présente les différents déterminants de la réussite scolaire ainsi que la sélectivité dans les programmes universitaires québécois. D’un point de vue méthodologique, ce mémoire utilise une base administrative de données longitudinales de l’Université de Montréal constituée de deux générations de cohortes d’étudiants de premier cycle. Sur le plan empirique, nous analysons d’abord la différenciation des programmes de premier cycle en matière de sélectivité pour en dériver un indicateur agrégé de sélectivité. Ensuite, nous étudions les facteurs individuels de réussite en contrôlant l’effet de la cote R dans un modèle multiniveau. L’analyse explicative fait apparaitre deux principaux constats originaux sur les facteurs de réussite, l’un relatif à la cote R et l’autre à l’appartenance de sexe. La cote R influence les chances de réussite des étudiants, mais en raison inverse du niveau de sélectivité à l’entrée. Dans les programmes où la sélection à l’entrée est la plus forte, les taux de diplomation sont les plus élevés et le lien entre la cote R et la note finale est très faible. En outre, le fait d’être un garçon tend à réduire les chances de réussite, mais cet effet négatif disparait quand on tient compte de la cote R ainsi que du programme d’appartenance dans un modèle multiniveau. Si les étudiantes réussissent mieux au niveau du premier cycle universitaire c’est donc surtout parce qu’elles ont eu de meilleures performances scolaires antérieurement et qu’elles ne sont pas dans les mêmes programmes que leurs homologues masculins.

Relación entre los riesgos percibidos por los trabajadores en minería subterránea y los accidentes de trabajo y enfermedades laborales

Introducción: La minería es considerada uno de los sectores económicos más importantes por su capacidad para generar recursos en su propio sector y en otros sectores como metalmecánica, agricultura e informática entre otros, y por su contribución al desarrollo socioeconómico sostenible de las poblaciones. Objetivo: Determinar la relación entre los riesgos percibidos por los trabajadores que laboran en minería subterránea en 3 departamentos de Colombia y los Accidentes de Trabajo (AT) y Enfermedades Laborales (EL). Materiales y Métodos: Estudio de corte transversal en 476 trabajadores de minería subterránea. Se incluyeron variables independientes (características sociodemográficas y laborales y percepción del riesgo) y variables dependientes (enfermedad laboral y accidente de trabajo), obtenidas a través de una entrevista directa aplicada por profesionales de la salud previamente capacitados. Para el análisis estadístico se utilizó la Prueba Exacta de Fisher, el Odds Ratio (OR) con el Intervalo de Confianza (IC) del 95%. Resultados: En los trabajadores de minería subterránea en los departamentos de Boyacá, Cundinamarca y Santander, se encontró relación estadística significativa entre la accidentalidad con la percepción de riesgo por iluminación (OR= 2.059, IC= 95%: 1.116, 3.798, p=0.013), percepción de riesgo por movimientos repetitivos (OR= 1.951, IC= 95%: 0.998, 3.815, p=0.034), percepción de riesgo por ruido (OR= 2.275, IC= 95%: 0.974, 5.312, p=0.039) y percepción de riesgo por manejo de cargas (OR= 1.778, IC= 95%: 0.969, 3.264, p=0.041). Conclusión: se encontró que existe una relación significativa entre la percepción de riesgo de los trabajadores de minería subterránea con accidentes de trabajo y que no existe relación entre esta percepción y las enfermedades laborales.

Multilevel multitrait-multimethod model: application to the measurement of egocentered social networks

Our goal in this paper is to assess reliability and validity of egocentered network data using multilevel analysis (Muthen, 1989, Hox, 1993) under the multitrait-multimethod approach. The confirmatory factor analysis model for multitrait-multimethod data (Werts & Linn, 1970; Andrews, 1984) is used for our analyses. In this study we reanalyse a part of data of another study (Kogovšek et al., 2002) done on a representative sample of the inhabitants of Ljubljana. The traits used in our article are the name interpreters. We consider egocentered network data as hierarchical; therefore a multilevel analysis is required. We use Muthen's partial maximum likelihood approach, called pseudobalanced solution (Muthen, 1989, 1990, 1994) which produces estimations close to maximum likelihood for large ego sample sizes (Hox & Mass, 2001). Several analyses will be done in order to compare this multilevel analysis to classic methods of analysis such as the ones made in Kogovšek et al. (2002), who analysed the data only at group (ego) level considering averages of all alters within the ego. We show that some of the results obtained by classic methods are biased and that multilevel analysis provides more detailed information that much enriches the interpretation of reliability and validity of hierarchical data. Within and between-ego reliabilities and validities and other related quality measures are defined, computed and interpreted

Bayesian estimation of multilevel item response model with missing data

This study proposed a novel statistical method that modeled the multiple outcomes and missing data process jointly using item response theory. This method follows the "intent-to-treat" principle in clinical trials and accounts for the correlation between outcomes and missing data process. This method may provide a good solution to chronic mental disorder study. ^ The simulation study demonstrated that if the true model is the proposed model with moderate or strong correlation, ignoring the within correlation may lead to overestimate of the treatment effect and result in more type I error than specified level. Even if the within correlation is small, the performance of proposed model is as good as naïve response model. Thus, the proposed model is robust for different correlation settings if the data is generated by the proposed model.^

Use of posterior predictive assessments to evaluate model fit in multilevel logistic regression

Assessing the fit of a model is an important final step in any statistical analysis, but this is not straightforward when complex discrete response models are used. Cross validation and posterior predictions have been suggested as methods to aid model criticism. In this paper a comparison is made between four methods of model predictive assessment in the context of a three level logistic regression model for clinical mastitis in dairy cattle; cross validation, a prediction using the full posterior predictive distribution and two “mixed” predictive methods that incorporate higher level random effects simulated from the underlying model distribution. Cross validation is considered a gold standard method but is computationally intensive and thus a comparison is made between posterior predictive assessments and cross validation. The analyses revealed that mixed prediction methods produced results close to cross validation whilst the full posterior predictive assessment gave predictions that were over-optimistic (closer to the observed disease rates) compared with cross validation. A mixed prediction method that simulated random effects from both higher levels was best at identifying the outlying level two (farm-year) units of interest. It is concluded that this mixed prediction method, simulating random effects from both higher levels, is straightforward and may be of value in model criticism of multilevel logistic regression, a technique commonly used for animal health data with a hierarchical structure.

Tooth loss in Brazilian middle-aged adults: multilevel effects

Objective. To examine the link between tooth loss and multilevel factors in a national sample of middle-aged adults in Brazil. Material and methods. Analyses were based on the 2003 cross-sectional national epidemiological survey of the oral health of the Brazilian population, which covered 13 431 individuals (age 35-44 years). Multistage cluster sampling was used. The dependent variable was tooth loss and the independent variables were classified according to the individual or contextual level. A multilevel negative binomial regression model was adopted. Results. The average tooth loss was 14 (standard deviation 9.5) teeth. Half of the individuals had lost 12 teeth. The contextual variables showed independent effects on tooth loss. It was found that having 9 years or more of schooling was associated with protection against tooth loss (means ratio range 0.68-0.76). Not having visited the dentist and not having visited in the last >= 3 years accounted for increases of 33.5% and 21.3%, respectively, in the risk of tooth loss (P < 0.05). The increase in tooth extraction ratio showed a strong contextual effect on increased risk of tooth loss, besides changing the effect of protective variables. Conclusions. Tooth loss in middle-aged adults has important associations with social determinants of health. This study points to the importance of the social context as the main cause of oral health injuries suffered by most middle-aged Brazilian adults.

Modeling in-Hospital Patient Survival During the First 28 Days After Intensive Care Unit Admission: a Prognostic Model for Clinical Trials in General Critically Ill Patients

OBJECTIVE: The objective of the study was to develop a model for estimating patient 28-day in-hospital mortality using 2 different statistical approaches. DESIGN: The study was designed to develop an outcome prediction model for 28-day in-hospital mortality using (a) logistic regression with random effects and (b) a multilevel Cox proportional hazards model. SETTING: The study involved 305 intensive care units (ICUs) from the basic Simplified Acute Physiology Score (SAPS) 3 cohort. PATIENTS AND PARTICIPANTS: Patients (n = 17138) were from the SAPS 3 database with follow-up data pertaining to the first 28 days in hospital after ICU admission. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The database was divided randomly into 5 roughly equal-sized parts (at the ICU level). It was thus possible to run the model-building procedure 5 times, each time taking four fifths of the sample as a development set and the remaining fifth as the validation set. At 28 days after ICU admission, 19.98% of the patients were still in the hospital. Because of the different sampling space and outcome variables, both models presented a better fit in this sample than did the SAPS 3 admission score calibrated to vital status at hospital discharge, both on the general population and in major subgroups. CONCLUSIONS: Both statistical methods can be used to model the 28-day in-hospital mortality better than the SAPS 3 admission model. However, because the logistic regression approach is specifically designed to forecast 28-day mortality, and given the high uncertainty associated with the assumption of the proportionality of risks in the Cox model, the logistic regression approach proved to be superior.