Pelo fato da maioria das mortes e morbidades associadas à gravidez ocorrerem em torno do parto, a qualidade do cuidado nesse período é crucial para as mães e seus bebês. Para acompanhar as mulheres nessa etapa, o partograma tem sido a ferramenta mais utilizada nas últimas décadas e, devido à sua simplicidade, é frequentemente usado em países com baixa e média renda. No entanto, sua utilização é altamente questionada devido à ausência de evidências que justifiquem uma contribuição ao parto. Para melhorar a qualidade do parto nessas circunstâncias, o projeto BOLD tem sido desenvolvido com o intuito de reduzir a ocorrência de problemas indesejados e com a finalidade desenvolver uma ferramenta moderna, chamada de SELMA, que projetase como uma alternativa ao partograma. Com a finalidade de associar características fixas e dinâmicas avaliadas no parto e identificar quais elementos intra parto podem ser utilizados como gatilhos para realização de uma intervenção e, assim, prevenir um desfecho indesejado, propomos nesta tese a utilização de modelos de sobrevivência com covariáveis dependentes do tempo. Inicialmente, consideramos a modelagem de dados longitudinais e de sobrevivência utilizando funções de risco paramétricas flexíveis. Nesse caso, propomos a utilização de cinco generalizações da distribuição Weibull, da distribuição Nagakami e utilizamos um procedimento geral de seleção de modelos paramétricos usuais via distribuição Gamma generalizada, inédito na modelagem conjunta. Realizamos um extenso estudo de simulação para avaliar as estimativas de máxima verossimilhança e os critérios de discriminação. Além disso, a própria natureza do parto nos leva a um contexto de eventos múltiplos, nos remetendo à utilização dos modelos multiestados. Eles são definidos como modelos para um processo estocástico que a qualquer momento ocupa um conjunto discreto de estados. De uma forma geral, são os modelos mais comuns para descrever o desenvolvimento de dados de tempo de falha longitudinais e são frequentemente utilizados em aplicações médicas. Considerando o contexto de eventos múltiplos, propomos a inclusão de uma covariável dependente do tempo no modelo multiestados a partir de uma modificação dos dados, o que nos trouxe resultados satisfatórios e similares ao esperado na prática clínica. / As most pregnancy-related deaths and morbidities are clustered around the time of child birth, the quality of care during this period is crucial for mothers and their babies. To monitor the women at this stage, the partograph has been the central tool used in recent decades and, motivated by its simplicity, is frequently used in low-and middle-income countries. However, its use is highly questioned due to lack of evidence to justify a contribution to labor. To improve the quality of labor in these circumstances, the BOLD project has been developed in order to reduce the occurrence of pregnancy-related problems and in order to develop a modern tool, called SELMA, which is projected as an alternative to partograph. Aiming to associate fixed and dynamic characteristics evaluated in the delivery and to identify which elements can be used as triggers for performing an intervention, and thus preventing a bad outcome, this thesis proposes the use of survival models with time dependent covariates. Initially, we consider the joint modeling of survival and longitudinal data using flexible parametric hazard functions. In this sense, we propose the use of five generalizations of Weibull distribution, the Nagakami model and an inedited framework to discriminate usual parametric models via the generalized Gamma distribution, performing an extensive simulation study to evaluate the maximum likelihood estimations and the proposed discrimination criteria. Indeed, by its own nature, the birth leads us to a context of multiple events, referring to the use of multi-state models. These are models for a stochastic process which at any time occupies one of a few possible states. In general, they are the most common models to describe the development of longitudinal failure time data and are often used in medical applications. Considering this context, we proposed the inclusion of a time dependent covariate in the multi-state model using a modified version of the input data, which gave us satisfactory results similar to those expected in clinical practice.

Nous présentons une nouvelle méthode pour étudier les relations entre la structure génétique des populations et l'environnement. Cette méthode repose sur des modèles hiérarchiques bayésiens qui utilisent conjointement des données génétiques multi-locus et des données spatiales, environnementales et/ou culturelles. Elle permet d'estimer la structure génétique des populations, d'évaluer ses liens avec des covariables non génétiques, et de projeter la structure génétique des populations en fonction de ces covariables. Dans un premier temps, nous avons appliqué notre approche à des données de génétique humaine pour évaluer le rôle de la géographie et des langages dans la structure génétique des populations amérindiennes. Dans un deuxième temps, nous avons étudié la structure génétique des populations pour 20 espèces de plantes alpines et nous avons projeté les modifications intra spécifiques qui pourront être causées par le réchauffement climatique. / We introduce a new method to study the relationships between population genetic structure and environment. This method is based on Bayesian hierarchical models which use both multi-loci genetic data, and spatial, environmental, and/or cultural data. Our method provides the inference of population genetic structure, the evaluation of the relationships between the structure and non-genetic covariates, and the prediction of population genetic structure based on these covariates. We present two applications of our Bayesian method. First, we used human genetic data to evaluate the role of geography and languages in shaping Native American population structure. Second, we studied the population genetic structure of 20 Alpine plant species and we forecasted intra-specific changes in response to global warming. STAR

In test score equating the focus is on the problem of finding the relationship between the scales of different test forms. This can be done only if data are collected in such a way that the effect of differences in ability between groups taking different test forms can be separated from the effect of differences in test form difficulty. In standard equating procedures this problem has been solved by using common examinees or common items. With common examinees, as in the equivalent groups design, the single group design, and the counterbalanced design, the examinees taking the test forms are either exactly the same, i.e., each examinee takes both test forms, or random samples from the same population. Common items (anchor items) are usually used when the samples taking the different test forms are assumed to come from different populations. The thesis consists of four papers and the main theme in three of these papers is the use of covariates, i.e., background variables correlated with the test scores, in observed score equating. We show how covariates can be used to adjust for systematic differences between samples in a non-equivalent groups design when there are no anchor items. We also show how covariates can be used to decrease the equating error in an equivalent groups design or in a non-equivalent groups design. The first paper, Paper I, is the only paper where the focus is on something else than the incorporation of covariates in equating. The paper is an introduction to test score equating, and the author's thoughts on the foundation of test score equating. There are a number of different definitions of test score equating in the literature. Some of these definitions are presented and the similarities and differences between them are discussed. An attempt is also made to clarify the connection between the definitions and the most commonly used equating functions. In Paper II a model is proposed for observed score linear equating with background variables. The idea presented in the paper is to adjust for systematic differences in ability between groups in a non-equivalent groups design by using information from background variables correlated with the observed test scores. It is assumed that conditional on the background variables the two samples can be seen as random samples from the same population. The background variables are used to explain the systematic differences in ability between the populations. The proposed model consists of a linear regression model connecting the observed scores with the background variables and a linear equating function connecting observed scores on one test forms to observed scores on the other test form. Maximum likelihood estimators of the model parameters are derived, using an assumption of normally distributed test scores, and data from two administrations of the Swedish Scholastic Assessment Test are used to illustrate the use of the model. In Paper III we use the model presented in Paper II with two different data collection designs: the non-equivalent groups design (with and without anchor items) and the equivalent groups design. Simulated data are used to examine the effect - in terms of bias, variance and mean squared error - on the estimators, of including covariates. With the equivalent groups design the results show that using covariates can increase the accuracy of the equating. With the non-equivalent groups design the results show that using an anchor test together with covariates is the most efficient way of reducing the mean squared error of the estimators. Furthermore, with no anchor test, the background variables can be used to adjust for the systematic differences between the populations and produce unbiased estimators of the equating relationship, provided that the “right” variables are used, i.e., the variables explaining those differences. In Paper IV we explore the idea of using covariates as a substitute for an anchor test with a non-equivalent groups design in the framework of Kernel Equating. Kernel Equating can be seen as a method including five different steps: presmoothing, estimation of score probabilities, continuization, equating, and calculating the standard error of equating. For each of these steps we give the theoretical results when observations on covariates are used as a substitute for scores on an anchor test. It is shown that we can use the method developed for Post-Stratification Equating in the non-equivalent groups with anchor test design, but with observations on the covariates instead of scores on an anchor test. The method is illustrated using data from the Swedish Scholastic Assessment Test.

No description available.

In this study we look at the problem of analysing survival data in the presence of longitudinally collected covariates. New methodology for analysing such data has been developed through the use of hidden Markov modeling. Special attention has been given to the case of large information volume, where a preliminary data reduction is necessary. Novel graphical diagnostics have been proposed to assess goodness of fit and significance of covariates. The methodology developed has been applied to the data collected on behaviors of Mexican fruit flies, which were monitored throughout their lives. It has been found that certain patterns in eating behavior may serve as an aging marker. In particular it has been established that the frequency of eating is positively correlated with survival times. / Thesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2010-04-26 18:34:01.131

Incomplete data often arise in the study of life history processes. Examples include missing responses, missing covariates, and unobservable latent processes in addition to right censoring. This thesis is on the development of statistical models and methods to address these problems as they arise in oncology and chronic disease. Methods of estimation and inference in parametric, weakly parametric and semiparametric settings are investigated. Studies of chronic diseases routinely sample individuals subject to conditions on an event time of interest. In epidemiology, for example, prevalent cohort studies aiming to evaluate risk factors for survival following onset of dementia require subjects to have survived to the point of screening. In clinical trials designed to assess the effect of experimental cancer treatments on survival, patients are required to survive from the time of cancer diagnosis to recruitment. Such conditions yield samples featuring left-truncated event time distributions. Incomplete covariate data often arise in such settings, but standard methods do not deal with the fact that the covariate distribution is also affected by left truncation. We develop a likelihood and algorithm for estimation for dealing with incomplete covariate data in such settings. An expectation-maximization algorithm deals with the left truncation by using the covariate distribution conditional on the selection criterion. An extension to deal with sub-group analyses in clinical trials is described for the case in which the stratification variable is incompletely observed. In studies of affective disorder, individuals are often observed to experience recurrent symptomatic exacerbations of symptoms warranting hospitalization. Interest lies in modeling the occurrence of such exacerbations over time and identifying associated risk factors to better understand the disease process. In some patients, recurrent exacerbations are temporally clustered following disease onset, but cease to occur after a period of time. We develop a dynamic mover-stayer model in which a canonical binary variable associated with each event indicates whether the underlying disease has resolved. An individual whose disease process has not resolved will experience events following a standard point process model governed by a latent intensity. If and when the disease process resolves, the complete data intensity becomes zero and no further events will arise. An expectation-maximization algorithm is developed for parametric and semiparametric model fitting based on a discrete time dynamic mover-stayer model and a latent intensity-based model of the underlying point process. The method is applied to a motivating dataset from a cohort of individuals with affective disorder experiencing recurrent hospitalization for their mental health disorder. Interval-censored recurrent event data arise when the event of interest is not readily observed but the cumulative event count can be recorded at periodic assessment times. Extensions on model fitting techniques for the dynamic mover-stayer model are discussed and incorporate interval censoring. The likelihood and algorithm for estimation are developed for piecewise constant baseline rate functions and are shown to yield estimators with small empirical bias in simulation studies. Data on the cumulative number of damaged joints in patients with psoriatic arthritis are analysed to provide an illustrative application.

Made available in DSpace on 2014-06-11T19:26:07Z (GMT). No. of bitstreams: 0 Previous issue date: 2009-02-16Bitstream added on 2014-06-13T19:12:58Z : No. of bitstreams: 1 leite_cds_me_jabo.pdf: 286709 bytes, checksum: 5dbd29633c088bfd8c09e91c549a77c3 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / A seleção baseada em características reprodutivas tem sido muito empregada em programas de melhoramento genético de suíno. Assim, objetivaram-se avaliar os efeitos ambientais e genéticos que influenciam o intervalo desmame-cio (IDC) e verificar sua influência no número de nascidos total (TL), nascidos vivos (NV) e mortos (NM) em fêmeas suínas. Para análise dos efeitos ambientais, utilizaram-se 8.104 dados da 1ª a 6ª ordem de parição, e, para as estimativas dos parâmetros genéticos, apenas as informações do 1º ao 3º IDC, o que resultou em 6.548 observações, que foram analisadas pelo método REML, utilizando-se modelos uni e multicaracterística. Para este último, considerou-se cada IDC (1º, 2º e 3º) como uma característica distinta. Avaliaram-se, também, as correlações genéticas entre o IDC, TL, NM e idade ao primeiro parto (IPP). Para os fatores ambientais, o modelo incluiu como efeitos fixos rebanho, linhagem, ano (AP) e estação (EP) de parto, e as covariáveis idade da porca ao parto (IDPP), TL e duração da lactação (DL). A DL, na forma linear, e a IDPP, na forma quadrática, influenciaram o IDC. Rebanho, AP e EP foram fontes de variação significativas, enquanto TL e linhagem não o foram. Não foi observada influência do IDC sobre TL, NV, nem sobre NM. A herdabilidade estimada para o IDC pelo modelo de repetibilidade foi baixa. As correlações genéticas entre os IDC (1º, 2º e 3º) foram de moderada a baixa magnitude, evidenciando que o modelo multicaracterística é mais indicado para as estimativas de parâmetro genético nessa população. As correlações genéticas entre IDC, TL e NM, assim como IDC e IPP foram favoráveis à seleção. / Selection for reproductive traits has been largely used in swine breeding programs. The aims of this study were to evaluate environmental and genetic effects that affect the weaning-to-estrus interval (WEI) in sows and to assess their influence on litter size (LS), number of live born (LP) and dead born piglets (DP). Data consisting of 8,104 WEI from the 1st to 6th farrowing recorded in two herds were used for environmental analysis, but for estimating the genetic parameters only data from the 1st to 3rd farrowing were used, totalling 6,548 records. Genetic analysis was performed using the REML method with single and multitrait models, where each WEI was considered as a different trait. Genetic correlations among WEI, LS, DP and age at first farrowing (AFF) were also estimated using a multitrait model. For the environmental analysis, the model included as fixed effects the herd, line, and year (YF) and season (SF) of farrowing, and as covariates the sow’s age at farrowing (SAF), LS, and lactation length (LL). The effects were linear for LL and quadratic for SAF. The herd, YF and SF were important sources of variation, whereas LS and line were not significant. There were no effects of WEI on the litter traits (LS, LP and DP). The heritability estimated for WEI was low, and genetic correlations among its different intervals were of moderate to low magnitude, evidencing that a multitrait model was more indicated for estimating the genetic parameters for this trait in this population. The genetic correlations between WEI and LS, DP and AFF would be favourable in a selection.

Made available in DSpace on 2016-06-02T20:06:11Z (GMT). No. of bitstreams: 1 DissMP.pdf: 748309 bytes, checksum: b6a638a5f9ec09f6622480b42f13d699 (MD5) Previous issue date: 2006-02-22 / Financiadora de Estudos e Projetos / This work has as main objective to insert covariates in the capture probability of the multiple capture-recapture method for closed animal population. Factors like climate, seasons of the year, animal size, could a¤ect the animal capture probability. We revise the methodology concepts, we make a study about the posteriori parameters sensibility, we present new parameters for the capture probability in specific situations and we insert covariates in the model used by Castledine (1981) through bayesian methods. The bayesian analysis was made through several studies of stochastic simulation through MCMC (Monte Carlo Markov Chain) with simulated and real data to obtain the population size posteriori results. / Este trabalho tem como objetivo principal a inserção de covariáveis nas probabilidades de captura do método de captura-recaptura múltipla para população fechada. No caso de população animal, por exemplo, fatores como clima, época do ano, tamanho do animal, podem afetar a probabilidade de captura do animal. Revisamos os conceitos da metodologia, fazemos um breve estudo sobre a sensibilidade das estimativas a posteriori em relação a escolha dos hiperparâmetros, apresentamos uma reparametrização para a probabilidade de captura em situações específicas e, motivados nessa reparametrização, inserimos covariáveis no modelo proposto por Castledine (1981) por meio de métodos bayesianos. A análise bayesiana foi feita através de vários estudos de simulação estocástica via MCMC (Monte Carlo Markov Chain) com dados simulados e reais para obter os resultados a posteriori do tamanho populacional.

Made available in DSpace on 2016-06-02T20:06:51Z (GMT). No. of bitstreams: 1 6689.pdf: 1539030 bytes, checksum: 72c7b3b07f3a78dcc9a7810fd8e09f9e (MD5) Previous issue date: 2015-02-25 / Universidade Federal de Minas Gerais / In survival analysis is studied the time until the occurrence of a particular event of interest and in the literature, the most common approach is parametric, where the data follow a specific probability distribution. Various known distributions maybe used to accommodate failure time data, however, most of these distributions are not able to accommodate non-monotonous hazard functions. Kumaraswamy (1980) proposed a new probability distribution and, based on that, recently Cordeiro and de Castro (2011) proposed a new family of generalized distributions, the so-called Kumaraswamy generalized (Kum-G). In addition to its flexibility, this distribution may also be considered for unimodal and tub shaped hazard functions. The objective of this dissertation is to present the family of Kum-G distributions and their particular cases to analyze lifetime data of individuals at risk, considering that part of the population will never present the event of interest, and considering that covariates may influence the survival function and the cured proportion of the population. Some properties of these models will be discussed as well as appropriate estimation methods, in the classical and Bayesian approaches. Finally, applications of such models are presented to literature data sets. / Em análise de sobrevivência estuda-se o tempo até a ocorrência de um determinado evento de interesse e na literatura, uma abordagem muito utilizada é a paramétrica, em que os dados seguem uma distribuição de probabilidade. Diversas distribuições conhecidas são utilizadas para acomodar dados de tempos de falha, porém, grande parte destas distribuições não é capaz de acomodar funções de risco não monótonas. Kumaraswamy (1980) propôs uma nova distribuição de probabilidade e, baseada nela, mais recentemente Cordeiro e de Castro (2011) propuseram uma nova família de distribuições generalizadas, a Kumaraswamy generalizada (Kum-G). Esta distribuição, além de ser flexível, contém distribuições com funções de risco unimodal e em forma de banheira. O objetivo deste trabalho é apresentar a família de distribuições Kum-G e seus casos particulares para analisar dados de tempo de vida de indivíduos em risco, considerando que uma parcela da população nunca apresentarão evento de interesse, além de considerarmos que covariáveis influenciem na função de sobrevivência e na proporção de curados da população. Algumas propriedades destes modelos serão abordadas, bem como métodos adequados de estimação, tanto na abordagem clássica quanto na bayesiana. Por fim, são apresentadas aplicações de tais modelos a conjuntos de dados existentes na literatura.

Nous présentons dans cette thèse en premier lieu la méthode de Bootstrap par permutation appliquée à la méthode des blocs maxima utilisée en théorie des valeurs extrêmes (TVE) univariée. La méthode est basée sur un échantillonnage particulier des données en utilisant les rangs des blocs maxima dont la distribution est présentée et introduite dans les simulations. Elle amène à une réduction de la variance des paramètres de la loi GEV et des quantiles estimés. En second lieu, on s’intéresse au cas où les observations sont indépendantes mais non identiquement distribuées en TVE. Cette variation dans la distribution est quantifiée en utilisant une fonction dite « skedasis function » notée c qui représente la fréquence des extrêmes. Ce modèle a été introduit par Einmahl et al. dans le papier « Statistics of heteroscedastic extremes ». On étudie plusieurs modèles paramétriques pour c (log-linéaire, linéaire, log-linéaire discret) ainsi que les résultats de consistance et de normalité asymptotique du paramètre θ représentant la tendance. Le test θ =0 contre θ ≠0 est interprété alors comme un test de détection de tendance dans les extrêmes. Nous illustrons nos résultats dans une étude par simulation qui montre en particulier que les tests paramétriques sont en général plus puissants que les tests non paramétriques pour la détection de la tendance, d’où l’utilité de notre travail. Nous discutons en plus le choix du seuil k en appliquant la méthode de Lepski. Enfin, nous appliquons la méthodologie sur les données de températures minimales et maximales dans la région de Fort Collins, Colorado durant le 20ème siècle afin de détecter la présence d’une tendance dans les extrêmes sur cette période. En troisième lieu, on dispose d’un jeu de données de précipitation journalière maximale sur 24 ans dans 40 stations. On réalise une prédiction spatio-temporelle des quantiles correspondants à un niveau de retour de 20 ans pour les précipitations mensuelles dans chaque station. Nous utilisons des modèles de GEV en introduisant des covariables dans les paramètres. Le meilleur modèle est choisi en termes d’AIC et par la méthode de validation croisée. Pour chacun des deux modèles choisis, nous estimons les quantiles extrêmes. Finalement, on applique la TVE unvariée et bivariée sur les vitesses du vent et la hauteur des vagues dans une région au Liban en vue de protéger la plateforme pétrolière qui y sera installée de ces risques environnementaux. On applique d’abord la théorie univariée sur la vitesse du vent et la hauteur des vagues séparément en utilisant la méthode des blocs maximas pour estimer les paramètres de la GEV et les niveaux de retour associés à des périodes de retour de 50, 100 et 500 années. Nous passons ensuite à l’application de la théorie bivariée afin d’estimer la dépendance entre les vents et les vagues extrêmes et d’estimer des probabilités jointes de dépassement des niveaux de retour univariés. Nous associons ces probabilités jointes de dépassement à des périodes de retour jointes et nous les comparons aux périodes de retour marginales. / We firstly present in this thesis the permutation Bootstrap method applied for the block maxima (BM) method in extreme value theory. The method is based on BM ranks whose distribution is presented and simulated. It performs well and leads to a variance reduction in the estimation of the GEV parameters and the extreme quantiles. Secondly, we build upon the heteroscedastic extremes framework by Einmahl et al. (2016) where the observations are assumed independent but not identically distributed and the variation in their tail distributions is modeled by the so-called skedasis function. While the original paper focuses on non-parametric estimation of the skedasis function, we consider here parametric models and prove the consistency and asymptotic normality of the parameter estimators. A parametric test for trend detection in the case where the skedasis function is monotone is introduced. A short simulation study shows that the parametric test can be more powerful than the non-parametric Kolmogorov-Smirnov type test, even for misspecified models. We also discuss the choice of threshold based on Lepski's method. The methodology is finally illustrated on a dataset of minimal/maximal daily temperatures in Fort Collins, Colorado, during the 20th century. Thirdly, we have a training sample data of daily maxima precipitation over 24 years in 40 stations. We make spatio-temporal prediction of quantile of level corresponding to extreme monthly precipitation over the next 20 years in every station. We use generalized extreme value models by incorporating covariates. After selecting the best model based on the Akaike information criterion and the k-fold cross validation method, we present the results of the estimated quantiles for the selected models. Finally, we study the wind speed and wave height risks in Beddawi region in the northern Lebanon during the winter season in order to protect the oil rig that will be installed. We estimate the return levels associated to return periods of 50, 100 and 500 years for each risk separately using the univariate extreme value theory. Then, by using the multivariate extreme value theory we estimate the dependence between extreme wind speed and wave height as well as joint exceedance probabilities and joint return levels to take into consideration the risk of these two environmental factors simultaneously.