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

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Published: Sep 23, 2022
DOI: https://doi.org/10.28951/bjb.v40i3.574
Keywords:
survival analysis probabilistic models failure times bayesian inference

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Marcos Vinicius BUENO
State University of Maringá
Robson Marcelo ROSSI
State University of Maringá
https://orcid.org/0000-0001-5386-0571

Abstract

The goal of this study was to use frequentist and Bayesian methodologies
to 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.

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How to Cite
BUENO, M. V., & ROSSI, R. M. (2022). FACTORS ASSOCIATED WITH SURVIVAL TIME FOR PATIENTS WITH HIV/AIDS IN THE STATE OF MATO GROSSO DO SUL: PARAMETRIC APPROACH. Brazilian Journal of Biometrics, 40(3). https://doi.org/10.28951/bjb.v40i3.574
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Vol. 40 No. 3 (2022)
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Articles
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References

BASTOS, J.; ROCHA, C. Análise de sobrevivˆencia: conceitos básicos. Arquivos de Medicina, ArquiMed-Departamento de Edições Científicas da AEFMUP, v.20,n.5-6, p.185–187, 2006.

BOX, G. E.; TIAO, G. C. Bayesian inference in statistical analysis. [S.l.]: JohnWiley, 1973.

BRASIL. Boletim epidemiológico: HIV/AIDS. Brasília: Ministério da Saúde, 2020.

BRUNELLO, G. H. V.; NAKANO, E. Y. Inferˆencia bayesiana no modelo weibulldiscreto em dados com presençaa de censura. TEMA, SciELO Brasil, v.16, n.2,p.97–110, 2015.

CARVALHO, M. S.; ANDREOZZI, V. L.; CODEC ̧ O, C. T.; CAMPOS, D. P.;BARBOSA, M. T. S.; SHIMAKURA, S. E. Análise de sobrevivˆencia: teoria e aplicações em saúde. 2. ed. Rio de Janeiro: Fiocruz, 2011.

COLOSIMO, E.; VIEIRA, A. O modelo de regressao de Cox com covariável dependente o tempo: Uma aplicação envolvendo pacientes infectados pelo HIV. Revista Brasileira de Estatística, v.54, n.57, p.139–152, 1996.

COLOSIMO, E. A.; GIOLO, S. R. Análise de sobrevivˆencia aplicada. São Paulo: Blucher, 2006.

COX, D. R. Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), Wiley Online Library, v.34, n.2, p.187–202,1972.

COX, D. R.; SNELL, E. J. A general definition of residuals. Journal of the Royal Statistical Society: Series B (Methodological), Wiley Online Library, v.30, n.2,p.248–265, 1968.

HOSMER, D. W.; LEMESHOW, S. Applied survival analysis. New York: JohnWiley and Sons, 1999.

JACKSON, C. flexsurv: A platform for parametric survival modeling in R. Journal of Statistical Software, v.70, n.8, p.1–33, 2016.

JONES, M. Kumaraswamy’s distribution: A beta-type distribution with some tractability advantages. Statistical Methodology, Elsevier, v.6, n.1, p.70–81, 2009.

KAPLAN, E. L.; MEIER, P. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, Taylor & Francis,v.53, n.282, p.457–481, 1958.

LAWLESS, J. F. Statistical models and methods for lifetime data. [S.l.]: John Wiley& Sons, 2003.

LEE, E. T.; WANG, J. Statistical methods for survival data analysis. 3. ed. NewJersey: John Wiley & Sons, 2003.

MEDEIROS, A. R. C.; LIMA, R. L. F. C.; MEDEIROS; L. B. D., MORAES; R. M.D.; VIANNA, R. P. D. T. Análise de sobrevida de pessoas vivendo com HIV/AIDS. Revista de Enfermagem UFPE Online, v.11, n.1, p.47–56, 2017

MELO, M. C.; DONALISIO, M. R.; CORDEIRO, R. C. Sobrevida de pacientes com AIDS e coinfecção pelo bacilo da tuberculose nas regiões Sul e Sudeste do Brasil. Ciˆencia & Saúde Coletiva, SciELO Public Health, v.22, p.3781–3792, 2017.

MULLER, E. V.; BORGES, P. K. O. Sobrevida de pacientes HIV/AIDS em tratamento antirretroviral e fatores associados na Região dos Campos Gerais, Paraná: 2002-2014. Journal of Development, v.6, n.5, p.28523–28542,2020.

NETO, L. F. S. P.; PERINI, F. D. B.; ARAG ́ON, M. G.; FREITAS, M. A.;MIRANDA, A. E. Protocolo brasileiro para infecções sexualmente transmissíveis 2020: infecção pelo HIV em adolescentes e adultos. Epidemiologia e Serviços de Saúde, SciELO Public Health, v.30, p.e2020588, 2021.

PASCOA, M. A. D.; ORTEGA, E. M.; CORDEIRO, G. M. The kumaraswamy generalized gamma distribution with application in survival analysis. Statistical Methodology, Elsevier, v.8, n.5, p.411–433, 2011.

R CORE TEAM.R: a language and environment for statistical computing. Vienna,2021. Accessible at: http://www.R-project.org. Accessed on: Jul. 2021.

RIBEIRO, R. A.; FONSECA, F. F.; PEREIRA, G. F. M. Evolução da AIDS noBrasil: Uma análise espacial. Revista do Seminário Internacional de Estatística com R, v.4, n.2, 2019.

ROSSI, R. M.Introdução aos métodos Bayesianos na análise de dados zootécnicoscom uso do WinBUGS e R. Maringá: Eduem, 2011.

RUE, H.; MARTINO, S.; CHOPIN, N. Approximate Bayesian inference for latent Gaussian models using integrated nested Laplace approximations (with discussion). Journal of the Royal Statistical Society: Series B (Methodological), v.71, p.319–392,2009.

SANTOS, C. A.; ACHCAR, J. A. A Bayesian analysis in the presence of covariates for multivariate survival data: an example of application. Revista Colombiana de Estadística, Universidad Nacional de Colombia., v.34, n.1, p.111–131, 2011.

SANTOS, R. O.; NAKANO, E. Y. Análise do tempo de permanˆencia de trabalhadores no mercado de trabalho do Distrito Federal via modelo de riscos proporcionais de Cox e log-normal. Revista Brasileira de Biometria, v.33, n.4,p.570–584, 2015.

SILVA, A. O.; FILHO, A. G. C. G.; SILVA, C. R.; LEITE, D. R. A.; SILVA, L. C.M.; FREITAS, W. W. L. Modelos de sobrevivˆencia aplicados `a evasão dos alunos de Estatística da UFPB. Revista InterScientia, v.6, n. 2, p.134–145, 2018.

THERNEAU, T. M.A Package for Survival Analysis in R. [S.l.], 2020. R package version 3.2-3. Available at: https://CRAN.R-project.org/package=survival. Accessed on: Jul. 2021.

WERLE, J. E.HIV/AIDS em Mato Grosso do Sul: análise de tendˆencia, distribuição espacial e sobrevida dos casos, 2021. 75p. Dissertation (Mestrado em Enfermagem) — Universidade Federal do Mato Grosso do Sul, Campo Grande,2021.