scholarly journals Linear censored quantile regression: A novel minimum‐distance approach

2020 ◽  
Vol 47 (4) ◽  
pp. 1275-1306
Author(s):  
Mickaël De Backer ◽  
Anouar El Ghouch ◽  
Ingrid Van Keilegom
Keyword(s):  
Quantile Regression ◽  
Minimum Distance ◽  
Censored Quantile Regression ◽  
Distance Approach

Censored quantile regression for residual lifetimes

2011 ◽  
Vol 18 (2) ◽  
pp. 177-194 ◽  
Author(s):  
Mi-Ok Kim ◽  
Mai Zhou ◽  
Jong-Hyeon Jeong
Keyword(s):  
Quantile Regression ◽  
Censored Quantile Regression ◽  
Residual Lifetimes

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

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Akram Yazdani ◽  
Mehdi Yaseri ◽  
Shahpar Haghighat ◽  
Ahmad Kaviani ◽  
Hojjat Zeraati
Keyword(s):  
Breast Cancer ◽  
Quantile Regression ◽  
Survival Time ◽  
Censored Data ◽  
Regression Models ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Right Censored Data ◽  
Censored Quantile Regression ◽  
Covariate Effects

AbstractThe 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.

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