This thesis is based on longitudinal data of the Swedish population provided byStatistics Sweden and is conducted on behalf of the Institute for Analytical Sociology.The focus is on investigating the effectiveness of a frequent sequence miningmethod called constrained Sequential PAttern Discovery using Equivalence classes(cSPADE). The method is applied to data on segregation within workplaces, specificallyreasons for Swedish employees moving to more segregated workplaces. Thethesis found that no unique pattern of age, gender, education, unemployment, income,workplace size or foreignness index explain why a Swedish employee movesto a more segregated workplace. Evaluating the algorithm, it was found that thenumber of observations need to be smaller or an alteration of the algorithm needsto be done to reduce the process time for this specific data set.

OBJECTIVE: To measure urinary triclosan levels and their variability across pregnancy, and to identify sources of triclosan exposure among Canadian pregnant women. METHODS: Single spot and serial urine samples, as well as consumer product use information were collected across pregnancy and post-partum from 80 healthy pregnant women in Ottawa. Analyses included descriptives, linear mixed effects and parametric trend modeling, and surrogate category analysis. RESULTS: Triclosan was detected in 87% of maternal urine samples (LOD=3.0 µg/L). Triclosan concentrations varied by time of day of urine collection (p=0.0006), season of sampling (p=0.019), and parity (p=0.038). Triclosan was included in 4% of all personal care products used by participants; 89% of these triclosan products were varying brands of toothpaste and hand soaps. CONCLUSION: This study provided the first data on temporal variability urinary triclosan levels, and on source attribution data in Canadian pregnant women. Results will assist with population-specific exposure assessment strategies.

Longitudinal studies, where data on study subjects are collected over time, is increasingly involving multivariate longitudinal responses. Frequently, the heterogeneity observed in a multivariate longitudinal response can be attributed to underlying unobserved disease states in addition to any between-subject differences. We propose modeling such disease states using a hidden Markov model (HMM) approach and expand upon previous work, which incorporated random effects into HMMs for the analysis of univariate longitudinal data, to the setting of a multivariate longitudinal response. Multivariate longitudinal data are modeled jointly using separate but correlated random effects between longitudinal responses of mixed data types in addition to a shared underlying hidden process. We use a computationally efficient Bayesian approach via Markov chain Monte Carlo (MCMC) to fit such models. We apply this methodology to bivariate longitudinal response data from a smoking cessation clinical trial. Under these models, we examine how to incorporate a treatment effect on the disease states, as well as develop methods to classify observations by disease state and to attempt to understand patient dropout. Simulation studies were performed to evaluate the properties of such models and their applications under a variety of realistic situations.

In Australia, women with breast cancer comprise one of the largest groups of cancer survivors. As a consequence of this, and improved survival rates, the interest in programs to enhance the recovery of cancer survivors is growing. Exercise during and after treatment has been identified as a potential strategy to assist women throughout their treatment and positively influence the recovery and health-related quality of life (HRQoL) of breast cancer survivors. Through the use of an existing data source, this study investigated physical activity rates, explored the factors associated with low levels of physical activity participation, and assessed the relationship between levels of activity and HRQoL among women diagnosed with breast cancer. The population-based sample, obtained in 2002 was comprised of 287 women newly diagnosed with breast cancer, residing in South-East Queensland. Women were followed-up (via subjective questionnaire and objective physical testing) every three months over a 12-month period, from six months post-diagnosis. Physical activity was assessed using the Behavioural Risk Factor Surveillance System (BRFSS) while HRQoL was assessed using the Functional Assessment of Cancer Therapy for breast cancer (FACTB+4). Based on National Physical Activity Guidelines, women were categorised as being sufficiently active, insufficiently active or sedentary at each of the five testing phases (specifically at 6-, 9-, 12-, 15- and 18-months post-diagnosis). Rates of participation in physical activity were relatively stable over the testing period. At 18 months post-diagnosis, 44%, 43% and 13% of women, respectively, were categorised as being sufficiently active, insufficiently active or sedentary. The sedentary or insufficiently active women were more likely to be older, obese or overweight, lack private health insurance, and have received both chemotherapy and radiotherapy, compared with sufficiently active women. Sedentary women consistently reported a lower HRQoL compared to active women (sufficiently or insufficiently active) over the 12-month testing period. This was especially apparent amongst the group of younger women (aged less than 50 years at diagnosis) (p=0.02). This work is among the first to explore physical activity rates specifically among Australian breast cancer survivors, and highlights the potential importance of participating in physical activity to optimise HRQoL during recovery from breast cancer. Specific attention to promote physical activity to the identified group of sedentary and insufficiently active survivors is of particular importance.

Made available in DSpace on 2015-03-26T13:32:12Z (GMT). No. of bitstreams: 1 texto completo.pdf: 4537549 bytes, checksum: 620653542527fa7feaefde5219b6a878 (MD5) Previous issue date: 2011-06-20 / The main contribution of molecular genetics to the benefit of applied genetic breeding is the direct use of the DNA data in genomic selection, allowing high selective efficiency and speed in the acquisition of genetic gains in selection and low costs. A practical and consistent way of analyzing the productive efficiency of beef animals subjected to selection is through the study of growth curves, as these represent a longitudinal trajectory of the weights of the animals in function of time. Thus, firstly, growth models (non-linear models) are adjusted to the weight-age data of each animal submitted to selection and the parameters estimated as phenotypes are considered. This procedure permits to determine genetic parameter estimates for any growth trajectory point, and to understand the genetic architecture of the entire trajectory, since all the weighing information is condensed by these few biologically interpretable parameters. The parameters estimated from the growth models are used to predict the Genomic Breeding Value (GBV) by means of specific statistical methods for the Genome Wide Selection (GWS). The general objective of this work was to apply statistical methods used in the Genome Wide Selection, mainly RRBLUP/ GWS and the Bayesian LASSO on the study of animal growth curves, considering as phenotypic variables the estimates of the parameters of non-linear regression models. The specific objectives were: to estimate the genomic breeding values for each individual evaluated; to estimate the effect of SNP markers and to identify those with the greatest effects; to select, via grouping techniques, groups of individuals genetically superior, in relation to the growth curve; and to validate all the methodology used via simulation study and apply it to real data of an F2 population of swine originated from the cross of two males from the naturalized Brazilian race Piau with 18 females of a commercial line (Landrace × Large White × Pietrain).The results indicated that the Genome Wide Selection statistical methods were efficient in studying the growth curves, considering simulated and real swine weight-age data. GWS presented high accuracy in the selection of the growth curve trajectory, allowing the detection of the QTLs (Quantitative Trait Loci) for the curve parameters of the individuals studied. In the absence of genes of significant effect, the methods RR-BLUP/GWS and Bayesian LASSO showed similar results but the latter showed more efficiency when the halothane gene, characterized as of significant effect, was included as a marker in the analyses. / O principal atrativo da genética molecular em benefício do melhoramento genético aplicado é a utilização direta das informações do DNA na seleção genômica, de modo a permitir alta eficiência seletiva, rapidez na obtenção de ganhos genéticos com a seleção e baixo custo. Uma forma prática e consistente de analisar a eficiência produtiva de animais de corte sujeitos à seleção é por meio dos estudos de curvas de crescimento, pois estas representam uma trajetória longitudinal dos pesos dos animais em função do tempo. Para isso, primeiramente ajustam-se modelos de crescimento (modelos não lineares) aos dados de peso-idade de cada animal submetido à seleção e consideram-se os parâmetros estimados como fenótipos. Este procedimento permite a obtenção de estimativas de parâmetros genéticos para qualquer ponto da trajetória de crescimento e possibilita a compreensão da arquitetura genética de toda a trajetória, uma vez que as informações de todas as pesagens são condensadas por esses poucos parâmetros interpretáveis biologicamente. Em seguida, os parâmetros estimados dos modelos de crescimento são utilizados para predizer os Valores Genéticos Genômicos (Genomic Breeding Value – GBV) por meio de métodos estatísticos específicos para a Seleção Genômica ix Ampla (Genome Wide Selection – GWS). O objetivo geral do presente trabalho foi empregar métodos estatísticos usados na Seleção Genômica Ampla, especificamente o RR-BLUP/GWS e o LASSO Bayesiano, no estudo de curvas de crescimento animal, considerando como variáveis fenotípicas as estimativas dos parâmetros de modelos de regressão não linear. Os objetivos específicos foram: estimar valores genéticos genômicos para cada indivíduo avaliado; estimar efeitos de marcadores SNPs e identificar os de maiores efeitos; selecionar, via técnicas de agrupamento, grupos de indivíduos geneticamente superiores em relação à curva de crescimento; e validar toda metodologia utilizada via estudo de simulação e aplicá-la a dados reais de uma população F2 de suínos proveniente do cruzamento de dois varrões da raça naturalizada brasileira Piau com 18 fêmeas de linhagem comercial (Landrace × Large White × Pietrain). Os resultados indicaram que os métodos estatísticos na Seleção Genômica Ampla foram eficientes no estudo de curvas de crescimento, considerando dados simulados e dados reais de peso-idade de suínos. A GWS apresentou alta acurácia na seleção para a trajetória das curvas de crescimento e possibilitou a detecção de QTLs (Quantitative Trait Loci) para os parâmetros da curva dos indivíduos considerados. Na ausência de genes de grande efeito, os métodos RRBLUP/ GWS e LASSO Bayesiano produziram resultados semelhantes, no entanto o método LASSO Bayesiano apresentou maior eficiência quando o gene halotano, caracterizado como de grande efeito, foi incluído como marcador nas análises.

Made available in DSpace on 2014-06-11T19:26:06Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-02-16Bitstream added on 2014-06-13T18:54:00Z : No. of bitstreams: 1 pessoa_mc_me_jabo.pdf: 827062 bytes, checksum: 8c25855980377e802529f8cbf88c8ec4 (MD5) / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / O Objetivo deste trabalho foi avaliar o valor adaptativo anual para possível utilização como critério de seleção para a eficiência reprodutiva de fêmeas da raça Nelore. Foram estudadas medidas de valor adaptativo do 4º ao 13º ano de permanência no rebanho de 21.610 fêmeas. Os valores adaptativos anuais foram calculados com base na capacidade de sobrevivência e no número de crias deixado ano após ano. O modelo de melhor ajuste aos dados, segundo os critérios adotados, foi o de 5ª ordem para a tendência média da população, 5ª ordem para o efeito genético aditivo direto e 3ª ordem para efeito de ambiente permanente de animal. O modelo heterogêneo com 10 classes foi o mais adequado na modelagem da das variâncias residuais. As herdabilidades para valor adaptativo anual aumentaram com a idade dos animais (0,05 a 0,55). As correlações entre os valores adaptativos em diferentes idades foram baixas nas idades menores e altas entre as idades adultas. A tendência genética para valor adaptativo anual foi realizada com base nos valores genéticos preditos referentes às medidas adaptativas do 4º (Pti4), 8º(Pti8) e 13º(Pti13) ano de idade. Como critério de comparação foram utilizadas as características idade ao primeiro parto (Ipp) e stayability (Stay). As associações entre os valores genéticos preditos das características foram feitas utilizando a correlação de Pearson e porcentagem de touros coincidentes. Estimativas de herdabilidade para Ipp, Stay1 e Stay2 foram respectivamente 0,12, 0,33 e 0,40. As tendências genéticas indicaram que houve ganhos para Pti4 e Pti13 e, para Pti8 as médias dos valores genéticos se mantiveram quase que constantes com o passar dos anos. As associações entre os valores genéticos indicaram maior associação entre valores genéticos preditos para valor adaptativo medido no 4º ano e valores genéticos preditos para as características Ipp e Stay / The objective of this study was to evaluate the annual Fitness as selection criteria for reproductive performance of Nelore cows. We studied measures of fitness of the 4th to the 13th year of stayability of 21,610 females. The annual fitness was calculated based on survivability and the number of offspring left year after year. The most appropriate model, according to criteria adopted, was a 5th order for the average trend of the population, 5th order for the direct genetic effect and 3th order for the permanent environmental effect of animal. The heterogeneous model with 10 classes was the most appropriated in modeling of residual variances. Heritability estimates for annual fitness increased with age of animals (0.05 to 0.55). The correlations between fitness at ages different were lower in younger ages, and high among the adult ages. The genetic trend for annual fitness was based on predicted breeding values to adaptive measures relating to the 4th (Pti4), 8th (Pti8) and 13th (Pti13) years of age. As criterion for comparison were used the traits age at first calving (IPP) and stayability (Stay). The associations between predicted breeding values of traits were made using Pearson correlation and percentage of bulls coincide. Heritability estimates for Ipp, and Stay1 Stay2 were respectively 0.12, 0.33 and 0.40. The genetic trends indicated that there were gains for Pti4 and Pti13, however for Pti8, the average genetic values remained almost constant over the years. The associations between breeding values indicate greater association between breeding values for annual fitness measured in year 4th year and the breeding values for the traits Ipp and Stay

Made available in DSpace on 2014-06-11T19:31:38Z (GMT). No. of bitstreams: 0 Previous issue date: 2005-03-31Bitstream added on 2014-06-13T19:02:05Z : No. of bitstreams: 1 oshiiwa_m_dr_botfca.pdf: 743927 bytes, checksum: 92c9badf9e4a0f2bb0d1e39cf24e9284 (MD5) / O presente trabalho discutiu aspectos teóricos e práticos do comportamento da variável resposta nos diferentes grupos e condições de avaliação utilizando o Ajuste de Curvas de Crescimento, procedimento multivariado de análise de dados experimentais que possibilita fazer previsões sobre o comportamento médio da resposta para situações diferentes daquelas para as quais o estudo foi planejado, além de propiciar análise comparativa das curvas dos grupos de interesse. Considerando a dificuldade existente quanto a programas computacionais acessíveis a pesquisadores das áreas agronômicas, biológicas e da saúde, e a falta de entendimento da complexidade da estrutura de análise dos dados longitudinais, elaborou-se programa computacional em linguagem que permita ao usuário facilidade de manuseio, e torná-lo disponível, para pesquisadores das áreas aplicadas e, finalmente, discutir as vantagens do procedimento multivariado na preservação da estrutura de dependência dos dados em relação aos procedimentos convencionais utilizados na experimentação agronômica. / The purpose of the present paper is to discuss theoretical and practical aspects of the behavior of response variables in different groups and evaluation conditions by using Growth Curves methodology. This methodology refers to a multivariate procedure of experimental data analysis that makes forecasts about the average behavior of the response variable for different situations from that for which the study was planned. In additien, the methodology enables comparative analysis of the curves between each of the experimental groups. Considering the lack of easy-use computer programs for researchers in the agronomical, biological and health fields, and the difficulty to understand the complexity of the data structure in longitudinal studies, a computer program will be proposed and written using high level language. The software will be of simple handling, easy access to researchers of applied areas and available. This work will also discuss the advantages of using the multivariate procedure of analysis compared to the conventional procedures commonly used in agronomic experimentation, related to the preservation of the structure of data dependence.

abstract: Fall accident is a significant problem associated with our society both in terms of economic losses and human suffering [1]. In 2016, more than 800,000 people were hospitalized and over 33,000 deaths resulted from falling. Health costs associated with falling in 2016 yielded at 33% of total medical expenses in the US- mounting to approximately $31 billion per year. As such, it is imperative to find intervention strategies to mitigate deaths and injuries associated with fall accidents. In order for this goal to be realized, it is necessary to understand the mechanisms associated with fall accidents and more specifically, the movement profiles that may represent the cogent behavior of the locomotor system that may be amendable to rehabilitation and intervention strategies. In this light, this Thesis is focused on better understanding the factors influencing dynamic stability measure (as measured by Lyapunov exponents) during over-ground ambulation utilizing wireless Inertial Measurement Unit (IMU). Four pilot studies were conducted: the First study was carried out to verify if IMU system was sophisticated enough to determine different load-carrying conditions. Second, to test the effects of walking inclinations, three incline levels on gait dynamic stability were examined. Third, tested whether different sections from the total gait cycle can be stitched together to assess LDS using the laboratory collected data. Finally, the fourth study examines the effect of “stitching” the data on dynamic stability measure from a longitudinally assessed (3-day continuous data collection) data to assess the effects of free-range data on assessment of dynamic stability. Results indicated that load carrying significantly influenced dynamic stability measure but not for the floor inclination levels – indicating that future use of such measure should further implicate normalization of dynamic stability measures associated with different activities and terrain conditions. Additionally, stitching method was successful in obtaining dynamic stability measure utilizing free-living IMU data. / Dissertation/Thesis / Masters Thesis Biomedical Engineering 2017

We propose a new method for analyzing longitudinal data which contain responses that are missing at random. This method consists in solving the generalized estimating equation (GEE) of [7] in which the incomplete responses are replaced by values adjusted using the inverse probability weights proposed in [14]. We show that the root estimator is consistent and asymptotically normal, essentially under some conditions on the marginal distribution and the surrogate correlation matrix as those presented in [12] in the case of complete data, and under minimal assumptions on the missingness probabilities. This method is applied to a real-life dataset taken from [10], which examines the incidence of respiratory disease in a sample of 250 pre-school age Indonesian children which were examined every 3 months for 18 months, using as covariates the age, gender, and vitamin A deficiency.

Em ensaios clínicos é muito comum a ocorrência de dados longitudinais discretos. Para sua análise é necessário levar em consideração que dados observados na mesma unidade experimental ao longo do tempo possam ser correlacionados. Além dessa correlação inerente aos dados é comum ocorrer o fenômeno de superdispersão (ou sobredispersão), em que, existe uma variabilidade nos dados além daquela captada pelo modelo. Um caso que pode acarretar a superdispersão é o excesso de zeros, podendo também a superdispersão ocorrer em valores não nulos, ou ainda, em ambos os casos. Molenberghs, Verbeke e Demétrio (2007) propuseram uma classe de modelos para acomodar simultaneamente a superdispersão e a correlação em dados de contagens: modelo Poisson, modelo Poisson-gama, modelo Poisson-normal e modelo Poisson-normal-gama (ou modelo combinado). Rizzato (2011) apresentou a abordagem bayesiana para o ajuste desses modelos por meio do Método de Monte Carlo com Cadeias de Markov (MCMC). Este trabalho, para modelar a incerteza relativa aos parâmetros desses modelos, considerou a abordagem bayesiana por meio de um método determinístico para a solução de integrais, INLA (do inglês, Integrated Nested Laplace Approximations). Além dessa classe de modelos, como objetivo, foram propostos outros quatros modelos que também consideram a correlação entre medidas longitudinais e a ocorrência de superdispersão, além da ocorrência de zeros estruturais e não estruturais (amostrais): modelo Poisson inacionado de zeros (ZIP), modelo binomial negativo inacionado de zeros (ZINB), modelo Poisson inacionado de zeros - normal (ZIP-normal) e modelo binomial negativo inacionado de zeros - normal (ZINB-normal). Para ilustrar a metodologia desenvolvida, um conjunto de dados reais referentes à contagens de ataques epilépticos sofridos por pacientes portadores de epilepsia submetidos a dois tratamentos (um placebo e uma nova droga) ao longo de 27 semanas foi considerado. A seleção de modelos foi realizada utilizando-se medidas preditivas baseadas em validação cruzada. Sob essas medidas, o modelo selecionado foi o modelo ZIP-normal, sob o modelo corrente na literatura, modelo combinado. As rotinas computacionais foram implementadas no programa R e são parte deste trabalho. / Discrete and longitudinal structures naturally arise in clinical trial data. Such data are usually correlated, particularly when the observations are made within the same experimental unit over time and, thus, statistical analyses must take this situation into account. Besides this typical correlation, overdispersion is another common phenomenon in discrete data, defined as a greater observed variability than that nominated by the statistical model. The causes of overdispersion are usually related to an excess of observed zeros (zero-ination), or an excess of observed positive specific values or even both. Molenberghs, Verbeke e Demétrio (2007) have developed a class of models that encompasses both overdispersion and correlation in count data: Poisson, Poisson-gama, Poisson-normal, Poissonnormal- gama (combined model) models. A Bayesian approach was presented by Rizzato (2011) to fit these models using the Markov Chain Monte Carlo method (MCMC). In this work, a Bayesian framework was adopted as well and, in order to consider the uncertainty related to the model parameters, the Integrated Nested Laplace Approximations (INLA) method was used. Along with the models considered in Rizzato (2011), another four new models were proposed including longitudinal correlation, overdispersion and zero-ination by structural and random zeros, namely: zero-inated Poisson (ZIP), zero-inated negative binomial (ZINB), zero-inated Poisson-normal (ZIP-normal) and the zero-inated negative binomial-normal (ZINB-normal) models. In order to illustrate the developed methodology, the models were fit to a real dataset, in which the response variable was taken to be the number of epileptic events per week in each individual. These individuals were split into two groups, one taking placebo and the other taking an experimental drug, and they observed up to 27 weeks. The model selection criteria were given by different predictive measures based on cross validation. In this setting, the ZIP-normal model was selected instead the usual model in the literature (combined model). The computational routines were implemented in R language and constitute a part of this work.