<p>Postpartum depression has become a major public health concern for women within a specific time period after delivery. Depression is possibly associated with some risk factors such as socioeconomic status, social support, maternal mental and physical health, and history of anxiety. TOMIS III, funded by the Canadian Institutes of Health Research, is a prospective cohort to study the associations between delivery method and health and health resource utilization.</p> <p>Clinically, we investigated the associations between mode of delivery and outcome of postnatal depression, maternal and infant health, and we implied the risk predictors for outcomes by statistical methodology of marginal model with generalized estimating equations (GEE). Statistically, a variety of regression models, namely, generalized linear mixed effect model (GLMM), hierarchical generalized linear model (HGLM) and Bayesian hierarchical model were applied for this analysis and results were compared with GEEs. Some imputation strategies, namely, mean imputation, last observation carrying forward (LOCF), hot-deck imputation and multiple imputation were employed for handling missing values in this study.</p> <p>Analysis results demonstrated that there was no statistically significant association between mode of delivery and postpartum depression [OR 0.99, 95% CI (0.73, 1.34)]. However, the development of postpartum depression was found to be associated with low income, low mental and physical health functioning, lack of social support, the low number of unmet learning needs in hospital, and English or French spoken at home. Results were consistent for all regression models but GEE provided the best fit and an excellent discriminative ability. GEE models were constructed on different datasets imputed by mean, LOCF, hot-deck and multiple imputation, and LOCF was recommended to handle the missing data in this longitudinal study.</p> <p>Analyses on the outcome of maternal health and infant health stated that method of delivery had a statistically significant influence on maternal health but no significant impact on infant health. Risks of maternal health problems were associated with cesarean delivery, good/fair/poor infant health, low maternal mental and physical health functioning, lack of care for maternal mental health, and good/fair/poor health before pregnancy. Risks of infant health problems were associated with good/fair/poor maternal health before pregnancy and after discharge, inadequate care or help for infant health, fair/poor community services after discharge, low maternal mental health functioning, non-English or non-French spoken at home, and mothers born outside of Canada.</p> / Master of Science (MSc)

A variety of complex data has broadened in many research fields such as epidemiology, genomics, and analytical chemistry with the development of science, technologies, and design scheme over the past few decades. For example, in epidemiology, the matched case-crossover study design is used to investigate the association between the clustered binary outcomes of disease and a measurement error in covariate within a certain period by stratifying subjects' conditions. In genomics, high-correlated and high-dimensional(HCHD) data are required to identify important genes and their interaction effect over diseases. In analytical chemistry, multiple time series data are generated to recognize the complex patterns among multiple classes. Due to the great diversity, we encounter three problems in analyzing those complex data in this dissertation. We have then provided several contributions to semiparametric and nonparametric methods for dealing with the following problems: the first is to propose a method for testing the significance of a functional association under the matched study; the second is to develop a method to simultaneously identify important variables and build a network in HDHC data; the third is to propose a multi-class dynamic model for recognizing a pattern in the time-trend analysis. For the first topic, we propose a semiparametric omnibus test for testing the significance of a functional association between the clustered binary outcomes and covariates with measurement error by taking into account the effect modification of matching covariates. We develop a flexible omnibus test for testing purposes without a specific alternative form of a hypothesis. The advantages of our omnibus test are demonstrated through simulation studies and 1-4 bidirectional matched data analyses from an epidemiology study. For the second topic, we propose a joint semiparametric kernel machine network approach to provide a connection between variable selection and network estimation. Our approach is a unified and integrated method that can simultaneously identify important variables and build a network among them. We develop our approach under a semiparametric kernel machine regression framework, which can allow for the possibility that each variable might be nonlinear and is likely to interact with each other in a complicated way. We demonstrate our approach using simulation studies and real application on genetic pathway analysis. Lastly, for the third project, we propose a Bayesian focal-area detection method for a multi-class dynamic model under a Bayesian hierarchical framework. Two-step Bayesian sequential procedures are developed to estimate patterns and detect focal intervals, which can be used for gas chromatography. We demonstrate the performance of our proposed method using a simulation study and real application on gas chromatography on Fast Odor Chromatographic Sniffer (FOX) system. / Doctor of Philosophy / A variety of complex data has broadened in many research fields such as epidemiology, genomics, and analytical chemistry with the development of science, technologies, and design scheme over the past few decades. For example, in epidemiology, the matched case-crossover study design is used to investigate the association between the clustered binary outcomes of disease and a measurement error in covariate within a certain period by stratifying subjects' conditions. In genomics, high-correlated and high-dimensional(HCHD) data are required to identify important genes and their interaction effect over diseases. In analytical chemistry, multiple time series data are generated to recognize the complex patterns among multiple classes. Due to the great diversity, we encounter three problems in analyzing the following three types of data: (1) matched case-crossover data, (2) HCHD data, and (3) Time-series data. We contribute to the development of statistical methods to deal with such complex data. First, under the matched study, we discuss an idea about hypothesis testing to effectively determine the association between observed factors and risk of interested disease. Because, in practice, we do not know the specific form of the association, it might be challenging to set a specific alternative hypothesis. By reflecting the reality, we consider the possibility that some observations are measured with errors. By considering these measurement errors, we develop a testing procedure under the matched case-crossover framework. This testing procedure has the flexibility to make inferences on various hypothesis settings. Second, we consider the data where the number of variables is very large compared to the sample size, and the variables are correlated to each other. In this case, our goal is to identify important variables for outcome among a large amount of the variables and build their network. For example, identifying few genes among whole genomics associated with diabetes can be used to develop biomarkers. By our proposed approach in the second project, we can identify differentially expressed and important genes and their network structure with consideration for the outcome. Lastly, we consider the scenario of changing patterns of interest over time with application to gas chromatography. We propose an efficient detection method to effectively distinguish the patterns of multi-level subjects in time-trend analysis. We suggest that our proposed method can give precious information on efficient search for the distinguishable patterns so as to reduce the burden of examining all observations in the data.

In dieser Arbeit wird eine neue Methode für den skalenunabhängigen Vergleich von LD-Strukturen in unterschiedlichen genomischen Regionen vorgeschlagen. Verschiedene Aspekte durch Skalen verursachter Probleme – von der Präzision der Schätzung der Marke-reffekte bis zur Genauigkeit der Vorhersage für neue Individuen - wurden untersucht. Darüber hinaus, basierend auf den Leistungsvergleichen von unterschiedlichen statistischen Methoden, wurden Empfehlungen für die Verwendungen der untersuchten Methoden gege-ben. / In dieser Arbeit wird eine neue Methode für den skalenunabhängigen Vergleich von LD-Strukturen in unterschiedlichen genomischen Regionen vorgeschlagen. Verschiedene Aspekte durch Skalen verursachter Probleme – von der Präzision der Schätzung der Marke-reffekte bis zur Genauigkeit der Vorhersage für neue Individuen - wurden untersucht. Darüber hinaus, basierend auf den Leistungsvergleichen von unterschiedlichen statistischen Methoden, wurden Empfehlungen für die Verwendungen der untersuchten Methoden gegeben

Submitted by JOÉL VILLAVICENCIO GASTELÚ null (tear_295@hotmail.com) on 2016-03-30T17:36:01Z No. of bitstreams: 1 Dissertação_Rev1_13 - Joel Gastelu.pdf: 3335802 bytes, checksum: 93fbe0689da0072cc77a9120a8e24b02 (MD5) / Rejected by Juliano Benedito Ferreira (julianoferreira@reitoria.unesp.br), reason: Solicitamos que realize uma nova submissão seguindo as orientações abaixo: O arquivo submetido está sem a ficha catalográfica. A versão submetida por você é considerada a versão final da dissertação/tese, portanto não poderá ocorrer qualquer alteração em seu conteúdo após a aprovação. Corrija estas informações e realize uma nova submissão contendo o arquivo correto. Agradecemos a compreensão. on 2016-04-01T13:14:50Z (GMT) / Submitted by JOÉL VILLAVICENCIO GASTELÚ null (tear_295@hotmail.com) on 2016-04-01T19:04:22Z No. of bitstreams: 1 Dissertação_Joel.pdf: 4253690 bytes, checksum: 75d9921d8416eec7341f8bf0e2182766 (MD5) / Rejected by Ana Paula Grisoto (grisotoana@reitoria.unesp.br), reason: Solicitamos que realize uma nova submissão seguindo as orientações abaixo: A data informada na capa do documento está diferente da data de defesa que consta na ficha catalográfica e folha de aprovação. Corrija esta informação no arquivo PDF e realize uma nova submissão contendo o arquivo correto. Agradecemos a compreensão. on 2016-04-05T13:53:33Z (GMT) / Submitted by JOÉL VILLAVICENCIO GASTELÚ null (tear_295@hotmail.com) on 2016-04-06T22:35:57Z No. of bitstreams: 1 Dissertação_Joel.pdf: 4231140 bytes, checksum: 4bd6143a52dc3a6846abd4f996ba9306 (MD5) / Approved for entry into archive by Juliano Benedito Ferreira (julianoferreira@reitoria.unesp.br) on 2016-04-07T12:21:23Z (GMT) No. of bitstreams: 1 gastelu_jv_me_ilha.pdf: 4231140 bytes, checksum: 4bd6143a52dc3a6846abd4f996ba9306 (MD5) / Made available in DSpace on 2016-04-07T12:21:23Z (GMT). No. of bitstreams: 1 gastelu_jv_me_ilha.pdf: 4231140 bytes, checksum: 4bd6143a52dc3a6846abd4f996ba9306 (MD5) Previous issue date: 2016-03-01 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / No presente trabalho tem-se como objetivo estimar o potencial fotovoltaico devido à instalação de sistemas fotovoltaicos em telhados de áreas residenciais. Na estimação desse potencial foram consideradas quatro grandezas: o nível de irradiação solar, a área aproveitável de telhado para a instalação dos sistemas fotovoltaicos, a eficiência de conversão dos sistemas fotovoltaicos e as probabilidades de instalação dos sistemas fotovoltaicos, que caracterizam as preferências dos habitantes à instalação desses sistemas. Um modelo hierárquico bayesiano foi proposto para o cálculo das probabilidades de instalação dos sistemas fotovoltaicos. Nesse modelo bayesiano é estabelecida uma relação entre as probabilidades de instalação, as variáveis socioeconômicas e as interações entre as subáreas, através de um modelo linear generalizado misto. O cálculo do valor esperado das probabilidades de instalação foi realizado usando o método de Monte Carlo via cadeias de Markov. Os resultados do potencial fotovoltaico são apresentados através de mapas temáticos, que permitem a visualização da distribuição espacial do seu valor esperado. Esta informação pode ajudar as concessionárias de distribuição no planejamento e expansão de suas redes elétricas em regiões com maior potencial de geração fotovoltaica. / The present work aims to estimate the photovoltaic potential for installing solar panel on the rooftop of residential areas. The estimation of this potential considers four quantities: the solar radiation level, rooftop availability for installation of photovoltaic systems, conversion efficiency of the photovoltaic systems and the probabilities for the installation of photovoltaic systems that characterize the preferences of the inhabitants to the installation of such systems. A bayesian hierarchical model is proposed to calculate the installation probabilities of photovoltaic systems. This bayesian model establishes a relation among the installation probabilities, socioeconomic variables and interactions between subareas, through a generalized linear mixed model. The calculation of expected value of installation probabilities in each subarea is performed using the Markov Chain Monte Carlo method. Photovoltaic potential results are presented through thematic maps that allow the visualization of the spatial distribution of its expected value. This information can help to distribution utilities for planning and expansion of their networks in regions with the greatest potential for photovoltaic generation.

Orientador: Antônio Padilha Feltrin / Resumo: No presente trabalho tem-se como objetivo estimar o potencial fotovoltaico devido à instalação de sistemas fotovoltaicos em telhados de áreas residenciais. Na estimação desse potencial foram consideradas quatro grandezas: o nível de irradiação solar, a área aproveitável de telhado para a instalação dos sistemas fotovoltaicos, a eficiência de conversão dos sistemas fotovoltaicos e as probabilidades de instalação dos sistemas fotovoltaicos, que caracterizam as preferências dos habitantes à instalação desses sistemas. Um modelo hierárquico bayesiano foi proposto para o cálculo das probabilidades de instalação dos sistemas fotovoltaicos. Nesse modelo bayesiano é estabelecida uma relação entre as probabilidades de instalação, as variáveis socioeconômicas e as interações entre as subáreas, através de um modelo linear generalizado misto. O cálculo do valor esperado das probabilidades de instalação foi realizado usando o método de Monte Carlo via cadeias de Markov. Os resultados do potencial fotovoltaico são apresentados através de mapas temáticos, que permitem a visualização da distribuição espacial do seu valor esperado. Esta informação pode ajudar as concessionárias de distribuição no planejamento e expansão de suas redes elétricas em regiões com maior potencial de geração fotovoltaica. / Abstract: The present work aims to estimate the photovoltaic potential for installing solar panel on the rooftop of residential areas. The estimation of this potential considers four quantities: the solar radiation level, rooftop availability for installation of photovoltaic systems, conversion efficiency of the photovoltaic systems and the probabilities for the installation of photovoltaic systems that characterize the preferences of the inhabitants to the installation of such systems. A bayesian hierarchical model is proposed to calculate the installation probabilities of photovoltaic systems. This bayesian model establishes a relation among the installation probabilities, socioeconomic variables and interactions between subareas, through a generalized linear mixed model. The calculation of expected value of installation probabilities in each subarea is performed using the Markov Chain Monte Carlo method. Photovoltaic potential results are presented through thematic maps that allow the visualization of the spatial distribution of its expected value. This information can help to distribution utilities for planning and expansion of their networks in regions with the greatest potential for photovoltaic generation. / Mestre

No description available.

The field of medicine is regularly faced with the challenge of utilizing information that is complicated or difficult to characterize. Physicians often must use their best judgment in reaching decisions or recommendations for treatment in the clinical setting. The goal of this thesis is to use innovative statistical tools in tackling three specific challenges of this nature from current healthcare applications. The first aim focuses on developing a novel approach to meta-analysis when combining binary data from multiple studies of paired design, particularly in cases of high heterogeneity between studies. The challenge is in properly accounting for heterogeneity when dealing with a low or moderate number of studies, and with a rarely occurring outcome. The proposed approach uses a Rasch model for translating data from multiple paired studies into a unified structure that allows for properly handling variability associated with both pair effects and study effects. Analysis is then performed using a Bayesian hierarchical structure, which accounts for heterogeneity in a direct way within the variances of the separate generating distributions for each model parameter. This approach is applied to the debated topic within the dental community of the comparative effectiveness of materials used for pit-and-fissure sealants. The second and third aims of this research both have applications in early detection of breast cancer. The interpretation of a mammogram is often difficult since signs of early disease are often minuscule, and the appearance of even normal tissue can be highly variable and complex. Physicians often have to consider many important pieces of the whole picture when trying to assess next steps. The final two aims focus on improving the interpretation of findings in mammograms to aid in early cancer detection. When dealing with high frequency and irregular data, as is seen in most medical images, the behaviors of these complex structures are often difficult or impossible to quantify by standard modeling techniques. But a commonly occurring phenomenon in high-frequency data is that of regular scaling. The second aim in this thesis is to develop and evaluate a wavelet-based scaling estimator that reduces the information in a mammogram down to an informative and low-dimensional quantification of the innate scaling behavior, optimized for use in classifying the tissue as cancerous or non-cancerous. The specific demands for this estimator are that it be robust with respect to distributional assumptions on the data, and with respect to outlier levels in the frequency domain representation of the data. The final aim in this research focuses on enhancing the visualization of microcalcifications that are too small to capture well on screening mammograms. Using scale-mixing discrete wavelet transform methods, the existing detail information contained in a very small and course image will be used to impute scaled details at finer levels. These "informed" finer details will then be used to produce an image of much higher resolution than the original, improving the visualization of the object. The goal is to also produce a confidence area for the true location of the shape's borders, allowing for more accurate feature assessment. Through the more accurate assessment of these very small shapes, physicians may be more confident in deciding next steps.

近來地理資訊系統(GIS)之資料庫受到不同領域的統計學家廣泛的研究，以期建立及分析可描述空間聚集效應及變異之模型，而描述空間相關存活資料之統計模式為公共衛生及流行病學上新興的研究議題。本文擬建立多維度半參數的貝氏階層模型，並結合空間及非空間隨機效應以描述存活資料中的空間變異。此模式將利用多變量條件自回歸(MCAR)模型以檢驗在不同地理區域中是否存有空間聚集效應。而基準風險函數之生成為分析貝氏半參數階層模型的重要步驟，本研究將利用混合Polya樹之方式生成基準風險函數。美國國家癌症研究院之「流行病監測及最終結果」(Surveillance Epidemiology and End Results, SEER)資料庫為目前美國最完整的癌症病人長期追蹤資料，包含癌症病人存活狀況、多重癌症史、居住地區及其他分析所需之個人資料。本文將自此資料庫擷取美國愛荷華州之癌症病人資料為例作實證分析，並以貝氏統計分析中常用之模型比較標準如條件預測指標(CPO)、平均對數擬邊際概似函數值(ALMPL)、離差訊息準則(DIC)分別測試其可靠度。 / The databases of Geographic Information System (GIS) have gained attention among different fields of statisticians to develop and analyze models which account for spatial clustering and variation. There is an emerging interest in modeling spatially correlated survival data in public health and epidemiologic studies. In this article, we develop Bayesian multivariate semiparametric hierarchical models to incorporate both spatially correlated and uncorrelated frailties to answer the question of spatial variation in the survival patterns, and we use multivariate conditionally autoregressive (MCAR) model to detect that whether there exists the spatial cluster across different areas. The baseline hazard function will be modeled semiparametrically using mixtures of finite Polya trees. The SEER (Surveillance Epidemiology and End Results) database from the National Cancer Institute (NCI) provides comprehensive cancer data about patient’s survival time, regional information, and others demographic information. We implement our Bayesian hierarchical spatial models on Iowa cancer data extracted from SEER database. We illustrate how to compute the conditional predictive ordinate (CPO), the average log-marginal pseudo-likelihood (ALMPL), and deviance information criterion (DIC), which are Bayesian criterions for model checking and comparison among competing models.