Comparison of some methods to estimate the Cox proportional hazards model for interval-censored data

Interval censored data is common in several areas of knowledge, such as: epidemiology, finance, demo- graphy, medicine, among others. They occur when the event of interest, the failure time, is not observed exactly, but is within some interval of the observation time. Often in this situation an impu...

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Fecha de publicación:
2022
Institución:
Universidad Pedagógica y Tecnológica de Colombia
Repositorio:
RiUPTC: Repositorio Institucional UPTC
Idioma:
spa
OAI Identifier:
oai:repositorio.uptc.edu.co:001/15319
Acceso en línea:
https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/12785
https://repositorio.uptc.edu.co/handle/001/15319
Palabra clave:
Métodos de imputación múltiple, censura a intervalo, enfoque Bayesiano, algoritmo ICM (Iterative Convex Minorant), modelo de riesgos proporcionales de Cox
Multiple imputation methods, Interval-censored, Bayesian approach, ICM (Iterative Convex Minorant) algorithm, Cox proportional hazards model.
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oai_identifier_str oai:repositorio.uptc.edu.co:001/15319
network_acronym_str REPOUPTC2
network_name_str RiUPTC: Repositorio Institucional UPTC
repository_id_str
dc.title.en-US.fl_str_mv Comparison of some methods to estimate the Cox proportional hazards model for interval-censored data
dc.title.es-ES.fl_str_mv Comparación de algunos métodos para estimar el modelo de riesgos proporcionales de Cox para datos con censura a intervalo
title Comparison of some methods to estimate the Cox proportional hazards model for interval-censored data
spellingShingle Comparison of some methods to estimate the Cox proportional hazards model for interval-censored data
Métodos de imputación múltiple, censura a intervalo, enfoque Bayesiano, algoritmo ICM (Iterative Convex Minorant), modelo de riesgos proporcionales de Cox
Multiple imputation methods, Interval-censored, Bayesian approach, ICM (Iterative Convex Minorant) algorithm, Cox proportional hazards model.
title_short Comparison of some methods to estimate the Cox proportional hazards model for interval-censored data
title_full Comparison of some methods to estimate the Cox proportional hazards model for interval-censored data
title_fullStr Comparison of some methods to estimate the Cox proportional hazards model for interval-censored data
title_full_unstemmed Comparison of some methods to estimate the Cox proportional hazards model for interval-censored data
title_sort Comparison of some methods to estimate the Cox proportional hazards model for interval-censored data
dc.subject.es-ES.fl_str_mv Métodos de imputación múltiple, censura a intervalo, enfoque Bayesiano, algoritmo ICM (Iterative Convex Minorant), modelo de riesgos proporcionales de Cox
topic Métodos de imputación múltiple, censura a intervalo, enfoque Bayesiano, algoritmo ICM (Iterative Convex Minorant), modelo de riesgos proporcionales de Cox
Multiple imputation methods, Interval-censored, Bayesian approach, ICM (Iterative Convex Minorant) algorithm, Cox proportional hazards model.
dc.subject.en-US.fl_str_mv Multiple imputation methods, Interval-censored, Bayesian approach, ICM (Iterative Convex Minorant) algorithm, Cox proportional hazards model.
description Interval censored data is common in several areas of knowledge, such as: epidemiology, finance, demo- graphy, medicine, among others. They occur when the event of interest, the failure time, is not observed exactly, but is within some interval of the observation time. Often in this situation an imputation is made of the data that is not exactly known. Some methods of multiple imputation proposed in the literature are the PMDA (Poor Man’s Data Augmentation) algorithm and the ANDA (Asymptotic Normal Data Augmen- tation) algorithm, which allow estimating the parameters of the Cox proportional hazards model using classical estimation methods. There are also alternative methods to make these estimations such as the ICM (Iterative Convex Minorant) algorithm and a Bayesian approach, which do not impute the data with interval censoring. In this work, a comparison was made via simulation of the performance of the estimators of the Cox model parameters produced by each of the aforementioned methods. The results showed that in general terms the ICM methods and the Bayesian approach present higher coverage probability values and lower mean square errors, in addition when increasing the sample size these values significantly improve compared to the PMDA and ANDA multiple imputation methods. In the latter, there were no significant differences between the results. Finally, an application was made with real data associated with a study of mastitis in milk cattle.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2024-07-08T14:24:03Z
dc.date.available.none.fl_str_mv 2024-07-08T14:24:03Z
dc.date.none.fl_str_mv 2022-01-29
dc.type.none.fl_str_mv info:eu-repo/semantics/article
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.identifier.none.fl_str_mv https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/12785
10.19053/01217488.v13.n1.2022.12785
dc.identifier.uri.none.fl_str_mv https://repositorio.uptc.edu.co/handle/001/15319
url https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/12785
https://repositorio.uptc.edu.co/handle/001/15319
identifier_str_mv 10.19053/01217488.v13.n1.2022.12785
dc.language.none.fl_str_mv spa
dc.language.iso.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/12785/12523
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv http://purl.org/coar/access_right/c_abf2
dc.format.none.fl_str_mv application/pdf
dc.publisher.es-ES.fl_str_mv Universidad Pedagógica y Tecnológica de Colombia
dc.source.en-US.fl_str_mv Ciencia En Desarrollo; Vol. 13 No. 1 (2022): Vol. 13 Núm. 1 (2022): Vol 13, Núm.1 (2022): Enero-Junio; 79-92
dc.source.es-ES.fl_str_mv Ciencia en Desarrollo; Vol. 13 Núm. 1 (2022): Vol. 13 Núm. 1 (2022): Vol 13, Núm.1 (2022): Enero-Junio; 79-92
dc.source.none.fl_str_mv 2462-7658
0121-7488
institution Universidad Pedagógica y Tecnológica de Colombia
repository.name.fl_str_mv Repositorio Institucional UPTC
repository.mail.fl_str_mv repositorio.uptc@uptc.edu.co
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spelling 2022-01-292024-07-08T14:24:03Z2024-07-08T14:24:03Zhttps://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/1278510.19053/01217488.v13.n1.2022.12785https://repositorio.uptc.edu.co/handle/001/15319Interval censored data is common in several areas of knowledge, such as: epidemiology, finance, demo- graphy, medicine, among others. They occur when the event of interest, the failure time, is not observed exactly, but is within some interval of the observation time. Often in this situation an imputation is made of the data that is not exactly known. Some methods of multiple imputation proposed in the literature are the PMDA (Poor Man’s Data Augmentation) algorithm and the ANDA (Asymptotic Normal Data Augmen- tation) algorithm, which allow estimating the parameters of the Cox proportional hazards model using classical estimation methods. There are also alternative methods to make these estimations such as the ICM (Iterative Convex Minorant) algorithm and a Bayesian approach, which do not impute the data with interval censoring. In this work, a comparison was made via simulation of the performance of the estimators of the Cox model parameters produced by each of the aforementioned methods. The results showed that in general terms the ICM methods and the Bayesian approach present higher coverage probability values and lower mean square errors, in addition when increasing the sample size these values significantly improve compared to the PMDA and ANDA multiple imputation methods. In the latter, there were no significant differences between the results. Finally, an application was made with real data associated with a study of mastitis in milk cattle.Los datos con censura a intervalo son comunes en varias áreas del conocimiento, tales como: epidemiolo- gía, finanzas, demografía, medicina, entre otras. Ocurren cuando el evento de interés, el tiempo de falla, no se observa exactamente, sino que se encuentra dentro de algún intervalo del tiempo de observación. Con frecuencia en esta situación se realiza una imputación de los datos que no se conocen exactamente. Algunos de los métodos de imputación múltiple propuestos en la literatura son el algoritmo PMDA (Poor Man’s Data Augmentation) y el algoritmo ANDA (Asymptotic Normal Data Augmentation), los cuales per- miten estimar los parámetros del modelo de riesgos proporcionales de Cox utilizando métodos clásicos de estimación. También existen métodos alternativos para realizar estas estimaciones, como el algoritmo ICM (Iterative Convex Minorant) y un enfoque Bayesiano, que no realizan imputación de los datos con censura a intervalo. En este trabajo se realizó una comparación vía simulación del desempeño de los estimadores de los pa- rámetros del modelo de Cox producidos por cada uno de los métodos anteriormente mencionados. Los resultados evidenciaron que en términos generales los métodos ICM y el enfoque Bayesiano presentan va- lores de probabilidad de cobertura más altos y errores cuadráticos medios más bajos, además al aumentar el tamaño de la muestra estos valores mejoran notablemente comparados con los métodos PMDA y ANDA. En estos últimos no se evidenciaron diferencias considerables entre los resultados. Finalmente, se realizó una aplicación con datos reales asociados a un estudio de mastitis en ganado lechero.application/pdfspaspaUniversidad Pedagógica y Tecnológica de Colombiahttps://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/12785/12523Ciencia En Desarrollo; Vol. 13 No. 1 (2022): Vol. 13 Núm. 1 (2022): Vol 13, Núm.1 (2022): Enero-Junio; 79-92Ciencia en Desarrollo; Vol. 13 Núm. 1 (2022): Vol. 13 Núm. 1 (2022): Vol 13, Núm.1 (2022): Enero-Junio; 79-922462-76580121-7488Métodos de imputación múltiple, censura a intervalo, enfoque Bayesiano, algoritmo ICM (Iterative Convex Minorant), modelo de riesgos proporcionales de CoxMultiple imputation methods, Interval-censored, Bayesian approach, ICM (Iterative Convex Minorant) algorithm, Cox proportional hazards model.Comparison of some methods to estimate the Cox proportional hazards model for interval-censored dataComparación de algunos métodos para estimar el modelo de riesgos proporcionales de Cox para datos con censura a intervaloinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/access_right/c_abf2Bustos Giraldo, Olga AlexandraJaramillo Elorza, Mario CésarLopera Gómez, Carlos Mario001/15319oai:repositorio.uptc.edu.co:001/153192025-07-18 10:56:32.948metadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCrepositorio.uptc@uptc.edu.co