Orientador: Maria Coleta Ferreira Albino de Oliveira / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Filosofia e Ciências Humanas / Made available in DSpace on 2018-08-19T20:38:07Z (GMT). No. of bitstreams: 1 Pereira_PauloJose_D.pdf: 5437049 bytes, checksum: 8d1bb8135f9a3c5871d30cf359e8c115 (MD5) Previous issue date: 2012 / Resumo: O conceito de adoção varia de acordo com a época e com as tradições. E o tema, além de invadir a discussão de ordem moral, atinge diversas áreas do conhecimento, entre elas a Demografia. Com a evolução da legislação brasileira sobre adoção, principalmente no final do século XX, nota-se que a prioridade é a qualidade de vida da criança ou adolescente, dando-lhe o direito de ter uma família para protegê-lo e que seja capaz de propiciar seu desenvolvimento. Esta tese se debruça sobre essa questão, focalizando especificamente a transferência legal da parentalidade de crianças e adolescentes para adultos outros que não seus pais biológicos. Sua motivação central é a de responder ao seguinte questionamento: o perfil da criança ou adolescente declarado como disponível para adoção influencia no tempo de espera para que seja incorporado a uma nova família? É em torno dessa questão central que são abordados a interferência do Estado no ambiente familiar, a evolução da legislação, as mudanças dos níveis de fecundidade, as preferências dos candidatos a adotantes, o perfil das crianças e dos adolescentes que aguardam adoção e as características dos diversos tipos de famílias que já adotaram. Ainda com a finalidade de responder a questão central da tese, foram aplicadas técnicas de Análise de Sobrevivência para identificar, estatisticamente, a importância de variáveis como sexo, idade, cor da pele, entre outras, na determinação do tempo de espera para adoção de crianças e adolescentes que foram cadastrados nos Juizados de Infância e Juventude dos municípios de Recife e São Paulo. Fica evidente, ao final deste estudo, que além da criação do Cadastro Nacional de Adoção e ações que derrubem certos preconceitos existentes na sociedade, é necessário que haja políticas públicas abrangentes voltadas para o indivíduo e para a família. Só a união desses fatores pode levar o país a conviver com um número cada vez mais reduzido de crianças e adolescentes excluídos de uma convivência familiar, e também garantir uma vida com dignidade e oportunidades para aqueles que, inevitavelmente, crescerão e se formarão sob a tutela do Estado. Estudos futuros devem focalizar, cada vez mais com um olhar demográfico, as informações oficiais, em níveis nacionais, que envolvam não só as crianças e adolescentes aptos à adoção, mas também aqueles que vivem em abrigos com a situação jurídica indefinida, os pretendentes à adoção, os egressos que não foram adotados, as Varas de Infância e Juventude. Uma análise do fenômeno com um horizonte maior faz-se necessário, e com o efetivo funcionamento do Cadastro Nacional de Adoção e do Sistema de Informação para Infância e Adolescência, em um curto espaço de tempo isso poderá ser realizado / Abstract: The adoption varies according to the time, the traditions and the theme as well as discussing moral issues. The problematic reaches different areas of social science, including the Demography. With the evolution of Brazilian legislation on adoption, especially in the late 20th century, the priority is the quality of life of the adopted person, giving her the right of having a family for protection. Furthermore, a family would be able to foster the development to a full member of society. This thesis focuses on the legal transfer of parenting children and teens to adults other than their biological parents in the context of adoption. The main motivation is to clarify if the profile of the person available for adoption influences the waiting time to be incorporated into a new family. The central issues that are addressed are the interference in family environment, legislative developments, changes in fertility levels, preferences of prospective adopters, the profile of the person waiting for adoption as well as the characteristics of different types of families that have adopted a child. To investigate the described problematic and influences, techniques of survival analysis were applied to identify statistically the importance of variables such as sex, age, skin color by determining the waiting time for the adopted person which were enrolled in the Child and Youth Courts of the cities of Recife and São Paulo. This study reveals, that the creation of the National Register of Adoption as well as actions that bring down certain prejudices in society, must be aimed at the individual as well as the family. Only the union of these factors gives rise to a country with an increasingly small number of children and adolescents excluded from a family. Furthermore, this guarantees a life with dignity and opportunities for those who will inevitably grow and form under the tutelage of the State. Future studies should focus more on official demographic information at national levels, involving not only children and adolescents that are able to be adopted, but also those living in shelters with uncertain legal status, applicants for adoption grown up at the Children and Youth Courts. An analysis of the phenomenon with a longer time horizon needs to be done, and could be done with a more effective operation of the National Adoption Register and Information System for Children and Adolescents / Doutorado / Demografia / Doutor em Demografia

Orientador: Luiz Koodi Hotta / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica / Made available in DSpace on 2018-08-21T13:55:30Z (GMT). No. of bitstreams: 1 Tsai_Rodrigo_D.pdf: 3859687 bytes, checksum: 1064b1fa05b98307d97763bb79e95de4 (MD5) Previous issue date: 2012 / Resumo: Nesse trabalho discutimos aplicações de cópulas a modelos de riscos múltiplos com dependência e modelos de misturas de distribuições. Numa primeira parte analisamos a inclusão de dependência entre os fatores de risco do modelo de riscos múltiplos. Os modelos de riscos múltiplos são uma família de modelos flexíveis para representar dados de tempos de vida. Suas maiores vantagens sobre os modelos de risco simples incluem a habilidade de representar funções de taxa de falha com formas não usuais e a facilidade de incluir covariáveis. O objetivo principal dessa parte é modelar a dependência existente entre as causas latentes de falha do modelo de riscos múltiplos por meio de funções de cópulas. A escolha da função de cópulas bem como das funções de distribuição dos tempos latentes de falha resultam numa classe flexível de distribuições de sobrevivência que é capaz de representar funções de taxa de falha de formas multimodais, forma de banheira e contendo efeitos locais dados pela concorrência dos riscos. A identificação e estimação do modelo proposto também são discutidas. Ao eliminar a restrição de suporte positivo para as variáveis latentes, o método pode ser utilizado para gerar uma família rica de distribuições univariadas contendo assimetrias e múltiplas modas. Na segunda parte propomos um modelo de mistura de distribuições generalizado utilizando cópulas. O parâmetro da cópula é útil para definir formas de assimetria e ponderar com maior ou menor peso determinadas regiões do suporte das distribuições componentes para compor a mistura. pesos das distribuições componentes variam no suporte da distribuição e não são restritos à soma unitária. A modelagem resultante acrescenta uma maior flexibilidade aos modelos de misturas na representação de dados com densidades de várias formas multimodais e assimétricas. O modelo tem como casos particulares o modelo de mistura tradicional, o modelo de riscos múltiplos e o modelo de fração de cura. Os modelos são aplicados a dados simulados e reais da literatura. Foram utilizados os métodos de estimação de máxima verossimilhança e os critérios de ajuste de Akaike e Bayesiano para a seleção dos modelos. Os modelos representaram bem os conjuntos de dados analisados em comparação com metodologias propostas na literatura / Abstract: In this work, we discuss the application of copula to polyhazard and mixture models. First we analyse the inclusion of dependence among failure causes in the polyhazard models. The polyhazard models constitute a family of flexible models to represent lifetime data. Their main advantages over single hazard models include the ability to represent hazard rate functions with unusual shapes and the ease of including covariates. The main purpose in this first part is to model the dependence that exists among the latent causes of failure in the polyhazard model by copula functions. The choice of the copula function as well as the latent failure distributions produces a flexible class of survival distributions that is able to model hazard functions with unusual shapes such as bathtub or multimodal curves, while also modelling local effects given by the competing risks. The model identification and estimation are also discussed. Dropping the restriction of positive support for the latent variables, the method can be used to generate a rich family of univariate distributions with asymmetries and multiple modes. In the second part a generalized mixture model using copula functions is proposed. To assemble the mixture model, the parameter of the copula function is used to define asymmetry shapes and to attribute more or less weight to chosen regions of the component distributions. The weights of the component distributions vary on the support of the distribution and are not restricted to the unitary sum. The resulting model increases the flexibility of the mixture models to represent data with densities with several multimodal and asymmetric shapes. Special cases of the model are the traditional mixture models, the polyhazard model, and the cure fraction model. Simulated and empirical data from the literature are analysed by the proposed models. The estimation was done by maximum likelihood methods and the selection of the models used the Akaike and Bayesian criteria. The proposed models exhibited very good fit to the data sets in comparison to other methodologies presented in the literature / Doutorado / Estatistica / Doutor em Estatística

Survival Time, what exactly does the term imply and what is the best method to measure it? Several experts within the field of Internet security have used the term; some has gone further and presented statistical facts on the survival time throughout the years. This bachelor thesis aim to present a universal definition of the term and further on measure the current survival time for a given unprotected system. By the deployment of a decoy, data will be captured and collected through port monitoring. Mainly focus will lie on building a time curve presenting the estimated time for an unprotected public system to get detected on the Internet and the elapsed time hence the system gets attacked.

Pharmacokinetic and pharmacodynamic models can be applied to clinical study data using various modelling approaches depending on the aim of the analysis. In population pharmacokinetics for instance, simple compartmental models can be employed to describe concentration-time data, identify prognostic factors and interpolate within well-defined experimental conditions. The first objective of this thesis was to illustrate such a ‘semi-mechanistic’ pharmacokinetic modelling approach using mavoglurant as an example of a compound under clinical development. In particular, methods to accurately characterise complex oral pharmacokinetic profiles and evaluate the impact of absorption factors were investigated. When the purpose of the model-based analysis is to further extrapolate beyond the experimental conditions in order to guide the design of subsequent clinical trials, physiologically-based pharmacokinetic (PBPK) models are more valuable as they incorporate information not only on the drug but also on the system, i.e. on mammillary anatomy and physiology. The combination of such mechanistic models with statistical modelling techniques in order to analysis clinical data has been widely applied in toxicokinetics but has only recently received increasing interest in pharmacokinetics. This is probably because, due to the higher complexity of PBPK models compared to conventional pharmacokinetic models, additional efforts are required for adequate population data analysis. Hence, the second objective of this thesis was to explore methods to allow the application of PBPK models to clinical study data, such as the Bayesian approach or model order reduction techniques, and propose a general mechanistic modelling workflow for population data analysis. In pharmacodynamics, mechanistic modelling of clinical data is even less common than in pharmacokinetics. This is probably because our understanding of the interaction between therapeutic drugs and biological processes is limited and also because the types of data to analyse are often more complex than pharmacokinetic data. In oncology for instance, the most widely used clinical endpoint to evaluate the benefit of an experimental treatment is survival of patients. Survival data are typically censored due to logistic constraints associated with patient follow-up. Hence, the analysis of survival data requires specific statistical techniques. Longitudinal tumour size data have been increasingly used to assess treatment response for solid tumours. In particular, the survival prognostic value of measures derived from such data has been recently evaluated for various types of cancer although not for pancreatic cancer. The last objective of this thesis was therefore to investigate different modelling approaches to analyse survival data of pancreatic cancer patients treated with gemcitabine, and compare tumour burden measures with other patient clinical characteristics and established risk factors, in terms of predictive value for survival.

Philosophiae Doctor - PhD / The HIV/AIDS pandemic has been a torment to the African developmental agenda, especially the Southern African Development Countries (SADC), for the past two decades. The disease and condition tends to affect the productive age groups. Children have also not been spared from the severe effects associated with the disease. The advent of antiretroviral treatment (ART) has brought a great relief to governments and patients in these regions. More people living with HIV/AIDS have experienced a boost in their survival prospects and hence their contribution to national developmental projects. Survival analysis methods are usually used in biostatistics, epidemiological modelling and clinical research to model time to event data. The most interesting aspect of this analysis comes when survival models are used to determine risk factors for the survival of patients undergoing some treatment or living with a certain disease condition. The purpose of this thesis was to determine prognostic risk factors for patients' survival whilst on ART. The study sought to highlight the risk factors that impact the survival time negatively at different survival time points. The study utilized a sample of paediatric and adult datasets from Namibia and Zimbabwe respectively. The paediatric dataset from Katutura hospital (Namibia) comprised of the adolescents and children on ART, whilst the adult dataset from Bulawayo hospital (Zimbabwe) comprised of those patients on ART in the 15 years and above age categories. All datasets used in this thesis were based on retrospective cohorts followed for some period of time. Different methods to reduce errors in parameter estimation were employed to the datasets. The proportional hazards, Bayesian proportional hazards and the censored quantile regression models were utilized in this study. The results from the proportional hazards model show that most of the variables considered were not signifcant overall. The Bayesian proportional hazards model shows us that all the considered factors had different risk profiles at the different quartiles of the survival times. This highlights that by using the proportional hazards models, we only get a fixed constant effect of the risk factors, yet in reality, the effect of risk factors differs at different survival time points. This picture was strongly highlighted by the censored quantile regression model which indicated that some variables were significant in the early periods of initiation whilst they did not significantly affect survival time at any other points in the survival time distribution. The censored quantile regression models clearly demonstrate that there are significant insights gained on the dynamics of how different prognostic risk factors affect patient survival time across the survival time distribution compared to when we use proportional hazards and Bayesian propotional hazards models. However, the advantages of using the proportional hazards framework, due to the estimation of hazard rates as well as it's application in the competing risk framework are still unassailable. The hazard rate estimation under the censored quantile regression framework is an area that is still under development and the computational aspects are yet to be incorporated into the mainstream statistical softwares. This study concludes that, with the current literature and computational support, using both model frameworks to ascertain the dynamic effects of different prognostic risk factors for survival in people living with HIV/AIDS and on ART would give the researchers more insights. These insights will then help public health policy makers to draft relevant targeted policies aimed at improving these patients' survival time on treatment.

Survival analysis today is widely implemented in the fields of medical and biological sciences, social sciences, econometrics, and engineering. The basic principle behind the survival analysis implies to a statistical approach designed to take into account the amount of time utilized for a study period, or the study of time between entry into observation and a subsequent event. The event of interest pertains to death and the analysis consists of following the subject until death. Events or outcomes are defined by a transition from one discrete state to another at an instantaneous moment in time. In the recent years, research in the area of survival analysis has increased greatly because of its large usage in areas related to bio sciences and the pharmaceutical studies. After identifying the probability density function that best characterizes the tumors and survival times of breast cancer women, one purpose of this research is to compare the efficiency between competing estimators of the survival function. Our study includes evaluation of parametric, semi-parametric and nonparametric analysis of probability survival models. Artificial Neural Networks (ANNs), recently applied to a number of clinical, business, forecasting, time series prediction, and other applications, are computational systems consisting of artificial neurons called nodes arranged in different layers with interconnecting links. The main interest in neural networks comes from their ability to approximate complex nonlinear functions. Among the available wide range of neural networks, most research is concentrated around feed forward neural networks called Multi-layer perceptrons (MLPs). One of the important components of an artificial neural network (ANN) is the activation function. This work discusses properties of activation functions in multilayer neural networks applied to breast cancer stage classification. There are a number of common activation functions in use with ANNs. The main objective in this work is to compare and analyze the performance of MLPs which has back-propagation algorithm using various activation functions for the neurons of hidden and output layers to evaluate their performance on the stage classification of breast cancer data. Survival analysis can be considered a classification problem in which the application of machine-learning methods is appropriate. By establishing meaningful intervals of time according to a particular situation, survival analysis can easily be seen as a classification problem. Survival analysis methods deals with waiting time, i.e. time till occurrence of an event. Commonly used method to classify this sort of data is logistic regression. Sometimes, the underlying assumptions of the model are not true. In model building, choosing an appropriate model depends on complexity and the characteristics of the data that affect the appropriateness of the model. Two such strategies, which are used nowadays frequently, are artificial neural network (ANN) and decision trees (DT), which needs a minimal assumption. DT and ANNs are widely used methodological tools based on nonlinear models. They provide a better prediction and classification results than the traditional methodologies such as logistic regression. This study aimed to compare predictions of the ANN, DT and logistic models by breast cancer survival. In this work our goal is to design models using both artificial neural networks and logistic regression that can precisely predict the output (survival) of breast cancer patients. Finally we compare the performances of these models using receiver operating characteristic (ROC) analysis.

There has been an increasing interest in survival analysis with interval-censored data, where the event of interest (such as infection with a disease) is not observed exactly but only known to happen between two examination times. However, because so much research has been focused on right-censored data, so many statistical tests and techniques are available for right-censoring methods, hence interval-censoring methods are not as abundant as those for right-censored data. In this study, right-censoring methods are used to fit a proportional hazards model to some interval-censored data. Transformation of the interval-censored observations was done using a method called mid-point imputation, a method which assumes that an event occurs at some midpoint of its recorded interval. Results obtained gave conservative regression estimates but a comparison with the conventional methods showed that the estimates were not significantly different. However, the censoring mechanism and interval lengths should be given serious consideration before deciding on using mid-point imputation on interval-censored data.

Collective intelligence (CI) emerges from local coordination, collaboration and competition among the individuals within a social group. CI mainly results in a global intelligent behavior. One of the fundamental interactional channels within a CI system is energy flow. Each agent within an artificial or physical ecosystem must absorb energy in order to survive, evolve, breed, and reshape its local environment. In addition because the energy resources are limited in the environment, each agent has to compete with other agents to reach the required level of energy. Understanding the internal energy flow can potentially provide a deep insight into internal activities and external emergent behaviors of a given complex system. This study proposes a stochastic scheme for modeling a multi-species prey-predator artificial ecosystem with two levels of food chain. This will enable us to investigate the influence of energy flow on the ecosystem’s lifetime. The proposed model consists of a stationary hosting environment with dynamic weather condition and fruit trees. The inhabitants of this ecosystem are herbivore and carnivore birds each consisting of species. In our model, the collective behavior emerges in terms of flocking with more added rules consist of breeding, competing, resting, hunting, escaping, seeking and foraging behaviors. Using multi-species scheme, we define the ecosystem as a combination of prey and predator species with inter-competition among species within same level of food chain and intra-competition among those belonging to different levels of food chain. Furthermore, in order to model the energy within the ecosystem, some energy variables as functions of behaviors are incorporated in to the model. Finally, a simulation and visualization structure for implementing the proposed model is developed in this study. The experimental results of 11,000 simulations analyzed by Cox univariate analysis and hazard function suggest that only five out of eight behaviors can statistically significant influence the ecosystem’s lifetime. Furthermore, the results of survival analysis show that out of all possible interactions among energy factors, only two of them, interaction between flocking and seeking energies, and interaction between flocking and hunting energies, have statistically significant impact on the system’s lifetime. In addition, software implementation of the proposed framework validates the stability of simulation and visualization architecture. At last regression results using Nelson-Aalen cumulative hazard function and Cox-Snell variable and scaled Schoenfeld residuals test strongly validate our experimental results. To the best of our knowledge, there are three contributions in this research: First, the high level of complexity in the structure of the proposed model in comparison with the other systems which mostly contains only one species of prey, one species of predator and a kind of resource. While this study introduces two species of prey, capability of competition among species, dynamic weather condition with two element of wind and rain and dynamic resources, various behavioral rules such as escaping, breeding, hunting, resting, etc. Energy flow analysis within an artificial ecosystem is the second contribution. To the best of author’s knowledge there is no similar comprehensive model in the previous literature that investigates the life span of a stochastic multi-species predator-prey artificial ecosystem based on energy flow using Survival Analysis method. Lastly, the simulation results show that the flocking and seeking energy and flocking and hunting energy interactions are the most significant interactions which match with the Thompson iii et al. [ 65] observations in the real life. Their findings indicate that in the real life, birds use flocking behavior for better movement, more efficient food searching and social learning. Flocking motion also decrease predation risk as much as the flock size increases.

Survival analysis is a statistical discipline that analyzes the time to occurrence of certain events. The aim of this thesis is to describe the possibilities of survival analysis in the environment of statistical software R. Theoretical knowledge is applied to real data, parametric and nonparametric estimates of survival functions are evaluated by different methods and compared with each other. In the section focusing on nonparametric models Kaplan-Meier and Nelson-Aalen functions are described. Among the parametric estimates there were included well-known probability distributions, survival functions and risk functions derived from these distributions are presented and there is discussed their usefulness in survival analysis. Another aim is to show the possibility of deriving transition probabilities from estimates and building a Markov chain model to capture the changes of studied cohort over time. The second part of the work contains a description of the applications of the theory of survival analysis. In this section there are shown possibilities of statistical modeling in the field of survival analysis using the software R. Outputs from R were used to create Markov model. There are presented possibilities of pharmacoeconomic models and description of the basic concepts of HTA. Cost-effectiveness calculations using ICER were conducted in accordance with the methodology of SUKL. It was shown that the statistical modelling of survival plays an important role in the evaluation of the cost-effectiveness of medicines.

Orientador: Djalma de Carvalho Moreira Filho / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Ciências Médicas / Made available in DSpace on 2018-08-26T22:55:22Z (GMT). No. of bitstreams: 1 Ferraz_RosemeiredeOlanda_D.pdf: 2711370 bytes, checksum: b4966f4c4ea3b88daffa54c0576bd307 (MD5) Previous issue date: 2015 / Resumo: O objetivo deste estudo é identificar os fatores associados ao tempo de sobrevida do câncer de mama, como idade, estadiamento e extensão do tumor, utilizando modelos de riscos proporcionais de Cox e de riscos competitivos de Fine-Gray. E também propor um modelo de regressão paramétrico para ajustar o tempo de sobrevida na presença dos riscos competitivos. É um estudo de coorte retrospectivo de base-populacional referente a 524 mulheres diagnosticadas com câncer de mama no período de 1993 a 1995, acompanhadas até 2011, residentes no município de Campinas/SP. Um ponto de corte para a variável contínua da idade foi escolhido utilizando-se modelos de Cox. Nos ajustes de modelos simples e múltiplo de Fine-Gray e de Cox, a idade não foi significativa quando o óbito por câncer de mama foi o evento de interesse. As curvas de sobrevivências estimadas por Kaplan-Meier evidenciaram diferenças expressivas nas probabilidades comparando-se os óbitos por câncer de mama e por riscos competitivos. As curvas de sobrevida por câncer de mama não apresentaram diferenças significativas quando comparadas as categorias de idades, segundo teste de log rank. Os modelos de Fine-Gray e Cox identificaram praticamente as mesmas covariáveis influenciando no tempo de sobrevida para ambos eventos de interesse, óbitos por câncer de mama e óbitos por riscos competitivos. Foram comparados os modelos exponencial, de Weibull e lognormal com o modelo gama generalizada e conclui-se que o modelo de regressão de Weibull foi o mais adequado para ajustar o tempo de sobrevida na presença dos riscos competitivos, conforme resultados dos testes de razões de verossimilhanças / Abstract: The aim of this study is to identify associated factors to time failure survival of breast cancer such as age, stage and extent of the tumor using Cox's proportional hazards and Fine-Gray competing risks models. It is a retrospective cohort study of population-based concerning to 524 women diagnosed with breast cancer in the period 1993-1995, followed until 2011, living in the city of Campinas, São Paulo State, Brazil. The cutoff age variable has been defined using Cox models. In the settings of simple and multiple models of Fine-Gray and Cox age was not significant when the death from breast cancer was the outcome of interest. The survival curves estimated by Kaplan-Meier showed significant differences in the odds comparing the deaths from breast cancer and competing risks. The survival curves for breast cancer showed no significant differences when comparing age groups, according to the logrank test. The Fine-Gray and Cox models identified the same covariates influencing the survival time for both events of interest: deaths from breast cancer and deaths from competing risks. The exponential, Weibull and lognormal regression models were compared with generalized gamma model and it is concluded that the Weibull regression model was the most appropriate to adjust the survival time in the presence of competing risks, according to results of the ratio likelihood tests / Doutorado / Epidemiologia / Doutora em Saúde Coletiva