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Master's Dissertation
DOI
https://doi.org/10.11606/D.45.1999.tde-20210729-024236
Document
Author
Full name
Ângela Tavares Paes
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 1999
Supervisor
Title in Portuguese
Modelos semiparamétricos para eventos recorrentes
Keywords in Portuguese
Processos Estocásticos
Abstract in Portuguese
A maioria dos estudos que envolvem Análise de Sobrevivência considera o tempo até a ocorrência de um único evento. Neste trabalho, analisamos situações onde o evento de interesse pode ocorrer mais de uma vez para o mesmo indivíduo. Embora osestudos nessa área tenham recebido considerável atenção nos últimos anos, as técnicas que já existem e que podem ser aplicadas a esses casos especiais ainda são pouco difundidas. O objetivo desta dissertação é descrever alguns métodosestatísticos para análise de eventos recorrentes e discutir suas aplicações. Utilizando a abordagem de processos de contagem multivariados, representamos o problema como um processo de Markov, em que os indivíduos são associados a diferentesestados ao longo do tempo. Esta metodologia consiste em estimar matrizes cujos elementos correspondem às probabilidades de transição entre os estados. Descrevemos métodos de estimação não paramétricos e três modelos semiparamétricos propostos naliteratura, baseados no modelo de riscos proporcionais de Cox. Os métodos são ilustrados através de um exemplo baseado em dados reais
Title in English
not available
Abstract in English
Many studies in life history analysis take into account the time until the occurrence of a terminal event. In this work, we analyse situations where the event of interest is non-fatal and subjects may experience it several times. Although thestatistical methods for such purposes have been improved in the last years, the techniques that can be applied in this special cases are not widely disseminated. The objective of this dissertation is to describe some methodologies for theanalysis of recurrent events and discuss their applications. Under a multivariate counting processes approach, the problem is represented as Markov processes where the subjects are associated to different states during a follow-up period. Thework is concerned with estimating matrices, the elements of which are the transition probabilities between states. We describe nonparametric and semiparametric methods based on the Cox proportional hazards regression model. We compare threemodels proposed in literature and apply them to a practical example
 
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Publishing Date
2021-07-29
 
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