A bivariate survival model for events with dependent failure times based on Archimedean copula functions. Application case: A sample of HIV patients : English
This paper proposes a bivariate survival model for dependent failure times based on copula functions of the Archimedean family and the mean cumulative function for non-recurrent events of different types (MCFR ̅E) and uses it to estimate the probability of survival from the occurrence of events of d...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | Brasil |
| Institución: | Universidade Federal de Lavras (UFLA) |
| Repositorio: | Brazilian Journal of Biometrics |
| Idioma: | inglés |
| OAI Identifier: | oai:biometria.ufla.br:article/644 |
| Acceso en línea: | https://biometria.ufla.br/index.php/BBJ/article/view/644 |
| Access Level: | acceso abierto |
| Palabra clave: | Archimedean copula family Bivariate survival model Dependent failure times HIV/AIDS Família da cópula de Arquimedes Modelo de sobrevida bivariado Tempos de falha dependentes VIH/SIDA |
| Sumario: | This paper proposes a bivariate survival model for dependent failure times based on copula functions of the Archimedean family and the mean cumulative function for non-recurrent events of different types (MCFR ̅E) and uses it to estimate the probability of survival from the occurrence of events of different types on the same HIV/AIDS patient. The copula functions evaluate the dependence structure between the failure times of the events experienced by the same patient throughout their follow-up period, and the MCFR ̅E generates the marginal survival function for each event. The marginal function is a nonparametric estimator that gives the same estimated survival probability as the Kaplan-Meier estimator if the failure times of the different types of events are independent. If each patient experiences at least one event, a subset of them generates a compound event that affects the estimated probability of survival. The results show that the traditionally estimated survival probabilities are biased if dependent failure times are treated as independent. |
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