The comparison of censored quantile regression methods in prognosis factors of breast cancer survival

Abstract The Cox proportional hazards model is a widely used statistical method for the censored data that model the hazard rate rather than survival time. To overcome complexity of interpreting hazard ratio, quantile regression was introduced for censored data with more straightforward interpretati...

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Autores principales:	Akram Yazdani, Mehdi Yaseri, Shahpar Haghighat, Ahmad Kaviani, Hojjat Zeraati
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7f33710e98904ef8b05975ab0acbfae6
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spelling	oai:doaj.org-article:7f33710e98904ef8b05975ab0acbfae62021-12-02T18:02:22ZThe comparison of censored quantile regression methods in prognosis factors of breast cancer survival10.1038/s41598-021-97665-x2045-2322https://doaj.org/article/7f33710e98904ef8b05975ab0acbfae62021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97665-xhttps://doaj.org/toc/2045-2322Abstract The Cox proportional hazards model is a widely used statistical method for the censored data that model the hazard rate rather than survival time. To overcome complexity of interpreting hazard ratio, quantile regression was introduced for censored data with more straightforward interpretation. Different methods for analyzing censored data using quantile regression model, have been introduced. The quantile regression approach models the quantile function of failure time and investigates the covariate effects in different quantiles. In this model, the covariate effects can be changed for patients with different risk and is a flexible model for controlling the heterogeneity of covariate effects. We illustrated and compared five methods in quantile regression for right censored data included Portnoy, Wang and Wang, Bottai and Zhang, Yang and De Backer methods. The comparison was made through the use of these methods in modeling the survival time of breast cancer. According to the results of quantile regression models, tumor grade and stage of the disease were identified as significant factors affecting 20th percentile of survival time. In Bottai and Zhang method, 20th percentile of survival time for a case with higher unit of stage decreased about 14 months and 20th percentile of survival time for a case with higher grade decreased about 13 months. The quantile regression models acted the same to determine prognostic factors of breast cancer survival in most of the time. The estimated coefficients of five methods were close to each other for quantiles lower than 0.1 and they were different from quantiles upper than 0.1.Akram YazdaniMehdi YaseriShahpar HaghighatAhmad KavianiHojjat ZeraatiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Akram Yazdani Mehdi Yaseri Shahpar Haghighat Ahmad Kaviani Hojjat Zeraati The comparison of censored quantile regression methods in prognosis factors of breast cancer survival
description	Abstract The Cox proportional hazards model is a widely used statistical method for the censored data that model the hazard rate rather than survival time. To overcome complexity of interpreting hazard ratio, quantile regression was introduced for censored data with more straightforward interpretation. Different methods for analyzing censored data using quantile regression model, have been introduced. The quantile regression approach models the quantile function of failure time and investigates the covariate effects in different quantiles. In this model, the covariate effects can be changed for patients with different risk and is a flexible model for controlling the heterogeneity of covariate effects. We illustrated and compared five methods in quantile regression for right censored data included Portnoy, Wang and Wang, Bottai and Zhang, Yang and De Backer methods. The comparison was made through the use of these methods in modeling the survival time of breast cancer. According to the results of quantile regression models, tumor grade and stage of the disease were identified as significant factors affecting 20th percentile of survival time. In Bottai and Zhang method, 20th percentile of survival time for a case with higher unit of stage decreased about 14 months and 20th percentile of survival time for a case with higher grade decreased about 13 months. The quantile regression models acted the same to determine prognostic factors of breast cancer survival in most of the time. The estimated coefficients of five methods were close to each other for quantiles lower than 0.1 and they were different from quantiles upper than 0.1.
format	article
author	Akram Yazdani Mehdi Yaseri Shahpar Haghighat Ahmad Kaviani Hojjat Zeraati
author_facet	Akram Yazdani Mehdi Yaseri Shahpar Haghighat Ahmad Kaviani Hojjat Zeraati
author_sort	Akram Yazdani
title	The comparison of censored quantile regression methods in prognosis factors of breast cancer survival
title_short	The comparison of censored quantile regression methods in prognosis factors of breast cancer survival
title_full	The comparison of censored quantile regression methods in prognosis factors of breast cancer survival
title_fullStr	The comparison of censored quantile regression methods in prognosis factors of breast cancer survival
title_full_unstemmed	The comparison of censored quantile regression methods in prognosis factors of breast cancer survival
title_sort	comparison of censored quantile regression methods in prognosis factors of breast cancer survival
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7f33710e98904ef8b05975ab0acbfae6
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The comparison of censored quantile regression methods in prognosis factors of breast cancer survival

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