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

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-01-21T18:23:30Z No. of bitstreams: 1 robertawolpdiniz.pdf: 1863742 bytes, checksum: 6c164077a0182789343f2f09aa65af87 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-01-25T18:48:13Z (GMT) No. of bitstreams: 1 robertawolpdiniz.pdf: 1863742 bytes, checksum: 6c164077a0182789343f2f09aa65af87 (MD5) / Made available in DSpace on 2016-01-25T18:48:13Z (GMT). No. of bitstreams: 1 robertawolpdiniz.pdf: 1863742 bytes, checksum: 6c164077a0182789343f2f09aa65af87 (MD5) Previous issue date: 2014-09-12 / O câncer de mama é um problema de saúde pública, sendo o segundo mais frequente no mundo e o de maior incidência na população feminina, além de ser uma das principais causas de óbito por câncer em mulheres. O objetivo deste estudo foi analisar a sobrevida livre de doença (SLD) em cinco anos e fatores prognósticos em mulheres com câncer de mama invasivo não metastático diagnosticadas entre 2003 e 2005 e tratadas em centro de assistência oncológica de referência de cidade de porte médio do Sudeste do país. As pacientes foram identificadas através do registro hospitalar de câncer da instituição. O seguimento dos casos foi realizado através de consulta aos prontuários, complementado por busca no banco do Sistema de Informação sobre Mortalidade (SIM) e contato telefônico. As variáveis analisadas foram distribuídas nos seguintes blocos: sociodemográficas (idade ao diagnóstico, cor da pele, grau de escolaridade, história familiar de câncer de mama, etc.), características relativas ao tumor (tamanho tumoral, comprometimento linfonodal, estadiamento, invasão neurovascular, grau tumoral, perfil imunohistoquímico, etc.), características relativas ao tratamento (tipo de cirurgia, realização de quimioterapia, radioterapia e hormonioterapia, completude do tratamento quimioterápico, tempo entre a cirurgia e terapia complementar, etc.). As funções de sobrevida foram calculadas por meio do método de Kaplan-Meier e o modelo de riscos proporcionais de Cox foi utilizado para avaliação dos fatores prognósticos. O estudo mostrou uma sobrevida livre de doença em cinco anos de 72% (IC95%: 67,6 – 75,9). As principais variáveis associadas à SLD, de forma independente, foram o comprometimento linfonodal, a realização de hormonioterapia e nível de escolaridade. Esse estudo mostrou a importância do diagnóstico precoce na SLD. Reforça-se ainda a relevância dessa pesquisa no país haja vista a escassez de estudos a respeito de SLD na população brasileira. / Breast cancer is a public health problem, being the second most common in the world and the highest incidence in the female population, in addition to being a major cause of death from cancer in this population overall. The aim of this study was to analyze the disease-free survival (DFS) at five years and prognostic factors in women with non metastatic invasive breast cancer diagnosed between 2003 and 2005 and treated at a referencial center of cancer care on a medium sized town of Southeast. Patients were identified using the medical records and data from the cancer registries of the institution. The follow up of the cases were performed using hospital records, supplemented by searching the database of the Mortality Information System (SIM) and telephone contact. The variables analyzed were: sociodemographic (age at diagnosis, race, education level, family history of breast cancer and presence of diagnostic mammography), related to tumor characteristics (size, lymph node involvement, stage, neurovascular invasion, tumor grade, immunohistochemical profile), characteristics related to treatment (type of surgery, use of chemotherapy, radiotherapy and hormone therapy, completion of chemotherapy, time between surgery and adjunctive therapy). Survival functions were calculated using the Kaplan-Meier model while the Cox proportional hazards method was used to evaluate prognostic factors. The study showed a disease-free survival at 60 months 72% (95% CI 67.6 to 75.9). The main variables associated with SLD, independently, were lymph node involvement, use of hormone therapy and degree of schooling. This study showed the importance of early diagnosis in DFS. This research is relevant due the lack of studies regarding the DFS at the Brazilian population.

Millions of people in the United States alone suffer from undiagnosed or late-diagnosed chronic diseases such as Chronic Kidney Disease and Type II Diabetes. Catching these diseases earlier facilitates preventive healthcare interventions, which in turn can lead to tremendous cost savings and improved health outcomes. We develop algorithms for predicting disease occurrence by drawing from ideas and techniques in the field of machine learning. We explore standard classification methods such as logistic regression and random forest, as well as more sophisticated sequence models, including recurrent neural networks. We focus especially on the use of medical code data for disease prediction, and explore different ways for representing such data in our prediction algorithms.

Residents of retirements homes in region of Zlín in point of demographic view Abstract Presented work introduces the law no. 108/2006 on social services that determines retirements homes as one of the facilities providing social services. The capacity of these facilities in the Zlín region is evaluated with the help of availability indices and facility normatives. More detailed attention was drawn to an analysis of age, gender, former permanent address and dependence degree of residents in selected retirement homes. This part of the research is based on data provided by five retirement homes in Zlín region. Obtained data was evaluated with the Kaplan-Meier method in order to estimate surviving function for these clients. The resemblance was tested employing the determination of the null hypothesis by log-rank test or Wilcoxon test. Keywords: ageing, residents of retirements homes, law on social services, survival analysis

Cette thèse s'intéresse à l'analyse des données longitudinales, potentiellement grandes selon les trois axes suivants : nombre d'individus, fréquence d'observation et nombre de covariables. A partir de ces données, éventuellement censurées, nous considérons comme facteur d'étude le temps d'apparition d'un ou plusieurs évènements. Nous cherchons dans des classes de modèles à coefficients dépendant du temps à estimer l’intensité d’apparition des événements. Or les estimateurs actuels, ne permettent pas de traiter efficacement un grand nombre d’observations et/ou un grand nombre de covariables. Nous proposons un nouvel estimateur défini via la vraisemblance complète de Cox et une pénalisation permettant à la fois la sélection de variables et de forcer, quand c’est possible, les coefficients à être constants. Nous introduisons des algorithmes d'optimisation proximaux, permettant d'estimer les coefficients du modèle de manière efficace. L'implémentation de ces méthodes en C++ et dans le package R coxtv permet d'analyser des jeux de données de taille supérieure à la mémoire vive; via un streaming du flux de données et des méthodes d'apprentissage en ligne, telles que la descente de gradient stochastique proximale aux pas adaptatifs. Nous illustrons les performances du modèle sur des simulations en nous comparant aux méthodes existantes. Enfin, nous nous intéressons à la problématique du design des jeux vidéo. Nous montrons que l'application directe de ce modèle, sur les grands jeux de données dont dispose l'industrie du jeu vidéo, permet de mettre en évidence des leviers d'amélioration du design des jeux étudiés. Nous nous intéressons d'abord à l'analyse des composantes bas niveau, telles que les choix d'équipement fait par les joueurs au fils du temps et montrons que le modèle permet de quantifier l'effet de chacun de ces éléments de jeu, offrant ainsi aux designers des leviers d'amélioration direct du design. Enfin, nous montrons que le modèle permet de dégager des enseignements plus généraux sur le design tels que l'influence de la difficulté sur la motivation des joueurs. / This thesis focuses on longitudinal time to event data possibly large along the following tree axes : number of individuals, observation frequency and number of covariates. We introduce a penalised estimator based on Cox complete likelihood with data driven weights. We introduce proximal optimization algorithms to efficiently fit models coefficients. We have implemented thoses methods in C++ and in the R package coxtv to allow everyone to analyse data sets bigger than RAM; using data streaming and online learning algorithms such that proximal stochastic gradient descent with adaptive learning rates. We illustrate performances on simulations and benchmark with existing models. Finally, we investigate the issue of video game design. We show that using our model on large datasets available in video game industry allows us to bring to light ways of improving the design of studied games. First we have a look at low level covariates, such as equipment choices through time and show that this model allows us to quantify the effect of each game elements, giving to designers ways to improve the game design. Finally, we show that the model can be used to extract more general design recommendations such as dificulty influence on player motivations.

This master thesis investigates the use of machine learning for predicting and assessing the risk of railway vehicle component failures. Data used for failure prediction often comes with limitations due to the complex nature of maintenance or sometimes requires investments for the extraction of information. Instead of real-time data, historical data and failure timestamps, easily accessed by organisations, are examined to see if they have the potential to contribute to a more effective maintenance strategy. Datasets used in maintenance often contain censored data and to overcome this problem survival machine learning models were also examined. Therefore both traditional machine learning models and survival machine learning models were evaluated and compared based on their C-index value. The results demonstrate that the survival machine learning models, which incorporate the risk and time-to-event aspects of the data, performed better than the traditional ones regarding the risk ranking of components. Random survival forest had the best result, and a ranking of important features. These findings indicate that there is a potential for survival machine learning, applied to existing historical data used for risk assessment for components failure.

No description available.

Thesis (Ph.D. (Statistics)) -- University of Limpopo, 2022 / This thesis is based on application of frailty and copula models to under five child mortality data set in South Africa. The main purpose of the study was to apply sample splitting techniques in a survival analysis setting and compare clustered survival models considering left truncation to the under five child mortality data set in South Africa. The major contributions of this thesis is in the application of the shared frailty model and a class of Archimedean copulas in particular, Clayton-Oakes copula with completely monotone generator, and introduction of sample splitting techniques in a survival analysis setting. The findings based on shared frailty model show that clustering effect was sig nificant for modelling the determinants of time to death of under five children, and revealed the importance of accounting for clustering effect. The conclusion based on Clayton-Oakes model showed association between survival times of children from the same mother. It was found that the parameter estimates for the shared frailty and the Clayton-Oakes models were quite different and that the two models cannot be comparable. Gender, province, year, birth order and whether a child is part of twin or not were found to be significant factors affect ing under five child mortality in South Africa. / NRF-TDG Flemish Interuniversity Council Institutional corporation (VLIR-IUC) VLIR-IUC Programme of the University of Limpopo

Thesis (M.Sc. (Statistics)) -- University of Limpopo, 2020 / Peritoneal Dialysis (PD) is a process of replacing kidney function which cleans waste from the blood and remove extra fluid from the body. In most cases, the process of PD is slowed down by a peritoneal membrane infection called peritonitis. Despite recent advancements in treatments and prevention, peritonitis still remains the leading complication which results in high morbidity and technique failure among PD patients. Using a prospective peritonitis dataset of 159 kidney patients who were on PD from 2008 to 2015 in Pietersburg Provincial Hospital, the aim of this study was to identify potential social, demographic and biological risk factors that contribute to the first episode of peritonitis. Both semi-parametric (Cox PH) and parametric (Accelerated Failure Time: Weibull, exponential, loglogistic, and gamma) survival models were fitted to the peritonitis dataset. Akaike Information Criterion (AIC) was applied to select models which best fit to the peritonitis data. Accordingly, log-logistic Accelerated Failure Time (AFT) model was found to be a working model that best fit to the data. A total of 96 (60.38%) peritonitis cases were recorded over the follow-up period with majority of peritonitis infection coming from females (65.4%) and rural dwellers (65.7%) with (62.6%) of black Africans showing higher risk of developing peritonitis. The multivariate log-logistic AFT model revealed that availability of water (p-value=0.018), electricity (p-value=0.018), dwelling (p-value=0.008), haemoglobin status (p-value=0.002) and duration on PD (p-value=0.001) are significant risk factors for the development of peritonitis. Therefore, patients with no water and electricity, coming from rural background with low level of haemoglobin and shorter duration on PD are associii ated with high risk or hazard of developing peritonitis for the first time.

Alzheimer's disease (AD), the most common form of dementia, is a chronic neurodegenerative disease. The HECT domain and ankyrin repeat containing E3 ubiquitin protein ligase 1 (HACE1) gene is expressed in human brain and may play a role in the pathogenesis of neurodegenerative disorders. Till now, no previous study has reported the association of the HACE1 gene with the risk and age at onset (AAO) of AD; while few studies have checked the proportional hazards assumption in the survival analysis of AAO of AD using Cox proportional hazards model. In this study, we examined the associations of 14 single nucleotide polymorphisms (SNPs) in the HACE1 gene with the risk and the AAO of AD using 791 AD patients and 782 controls. Multiple logistic regression model identified one SNP (rs9499937 with p = 1.8×10) to be associated with the risk of AD. For survival analysis of AAO, both classic Cox regression model and Bayesian survival analysis using the Cox proportional hazards model were applied to examine the association of each SNP with the AAO. The hazards ratio (HR) with its 95% confidence interval (CI) was estimated. Survival analysis using the classic Cox regression model showed that 4 SNPs were significantly associated with the AAO (top SNP rs9499937 with HR=1.33, 95%CI=1.13-1.57, p=5.0×10). Bayesian Cox regression model showed similar but a slightly stronger associations (top SNP rs9499937 with HR=1.34, 95%CI=1.11-1.55) compared with the classic Cox regression model. Using an independent family-based sample, one SNP rs9486018 was associated with the risk of AD (p=0.0323) and the T-T-G haplotype from rs9786015, rs9486018 and rs4079063 showed associations with both the risk and AAO of AD (p=2.27×10 and 0.0487, respectively). The findings of this study provide first evidence that several genetic variants in the HACE1 gene were associated with the risk and AAO of AD.