Power-transformed linear regression on quantile residual life for censored competing risks data

Fan, Caiyun; Zhang, Feipeng; Zhou, Yong

doi:10.1080/03610926.2014.950873

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Peng and Huang [60] developed an estimator which is very close to Nelson-Aalen estimator. Most recently, great work is still expanding this area to recurrent events [61][62][63], various censoring types [64][65][66], competing risks [65,[67][68][69].…”

Section: Qr Models For Time-to-event Datamentioning

confidence: 99%

Quantile Regression Models and Their Applications: A Review

Huang¹,

Zhang²,

Chen³

et al. 2017

J Biom Biostat

100

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Quantile regression (QR) has received increasing attention in recent years and applied to wide areas such as investment, finance, economics, medicine and engineering. Compared with conventional mean regression, QR can characterize the entire conditional distribution of the outcome variable, may be more robust to outliers and misspecification of error distribution, and provides more comprehensive statistical modeling than traditional mean regression. QR models could not only be used to detect heterogeneous effects of covariates at different quantiles of the outcome, but also offer more robust and complete estimates compared to the mean regression, when the normality assumption violated or outliers and long tails exist. These advantages make QR attractive and are extended to apply for different types of data, including independent data, time-to-event data and longitudinal data. Consequently, we present a brief review of QR and its related models and methods for different types of data in various application areas.Citation: Huang Q, Zhang H, Chen J, He M (2017) Quantile Regression Models and Their Applications: A Review. J Biom Biostat 8: 354.

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Section: Qr Models For Time-to-event Datamentioning

confidence: 99%

Quantile Regression Models and Their Applications: A Review

Huang¹,

Zhang²,

Chen³

et al. 2017

J Biom Biostat

100

View full text Add to dashboard Cite

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Survival Analysis of Colon Cancer Data using Quantile Regression

Veeramachaneni

Mudunuru

2023

RJPT

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Quantile regression emerged as an alternative and robust technique to the commonly used regression models. Even in the survival analysis, quantile regression is offering more flexible modelling of survival data without any constraints attached. Unlike traditional Cox hazards models or accelerated failure models, quantile regression does not restrict the variation of the coefficients for different quantiles. In this research we modelled and compared traditional survival regression method with quantile regression applied to colon cancer data.

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Power-transformed linear regression on quantile residual life for censored competing risks data

Cited by 2 publications

References 21 publications

Quantile Regression Models and Their Applications: A Review

Quantile Regression Models and Their Applications: A Review

Survival Analysis of Colon Cancer Data using Quantile Regression

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