FACTORS ASSOCIATED WITH SURVIVAL TIME FOR PATIENTS WITH HIV/AIDS IN THE STATE OF MATO GROSSO DO SUL: PARAMETRIC APPROACH

The goal of this study was to use frequentist and Bayesian methodologiesto adjust some probability distributions for survival time in HIV/AIDS patients in Mato Grosso do Sul, Brazil, followeds from 2009 to 2018. The influence of explanatory variables on the response variable can be calculated using...

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Detalhes bibliográficos
Autores: BUENO, Marcos Vinicius, ROSSI, Robson Marcelo
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2022
País:Brasil
Recursos:Universidade Federal de Lavras (UFLA)
Repositório:Brazilian Journal of Biometrics
Idioma:inglês
OAI Identifier:oai:biometria.ufla.br:article/574
Acesso em linha:https://biometria.ufla.br/index.php/BBJ/article/view/574
Access Level:Acceso aberto
Palavra-chave:survival analysis
probabilistic models
failure times
bayesian inference
análise de sobrevivência
modelos probabilísticos
tempos de falha
inferência bayesiana
Descrição
Resumo:The goal of this study was to use frequentist and Bayesian methodologiesto adjust some probability distributions for survival time in HIV/AIDS patients in Mato Grosso do Sul, Brazil, followeds from 2009 to 2018. The influence of explanatory variables on the response variable can be calculated using regression models. The Log-Normal distribution was shown to be the most parsimonious for the data using the Akaike information criterion (AIC) values and the maximum likelihood logarithm.Two regression models were built based on the described methodologies, converging to the same interpretation of the explanatory variables: sex, race, education, and injecting drug use. The median time to death from HIV/AIDS is approximately: 2.1 higher for females, 1.8 higher for white people, 5.4 higher for individuals with more than 8 years of education, 5.5 higher for individuals who do not use injecting drugs, according to the study. Based on the interpretations of the coefficients of the model parameters, the need for prevention and early diagnosis policies focused on groups that have a shorter median survival time after notification of HIV infection can be discussed.