scholarly journals Quantile Regression for Survival Data

2021 ◽  
Vol 8 (1) ◽  
pp. 413-437
Author(s):  
Limin Peng
Keyword(s):  
Quantile Regression ◽  
Survival Data ◽  
Recurrent Events ◽  
Regression Methods ◽  
Practical Tool ◽  
Stable Computation ◽  
Randomly Censored Data ◽  
Semicompeting Risks ◽  
Events Data ◽  
Data Subject

Quantile regression offers a useful alternative strategy for analyzing survival data. Compared with traditional survival analysis methods, quantile regression allows for comprehensive and flexible evaluations of covariate effects on a survival outcome of interest while providing simple physical interpretations on the time scale. Moreover, many quantile regression methods enjoy easy and stable computation. These appealing features make quantile regression a valuable practical tool for delivering in-depth analyses of survival data. This article provides a review of a comprehensive set of statistical methods for performing quantile regression with different types of survival data. The review covers various survival scenarios, including randomly censored data, data subject to left truncation or censoring, competing risks and semicompeting risks data, and recurrent events data. Two real-world examples are presented to illustrate the utility of quantile regression for practical survival data analyses.

scholarly journals Marginal quantile regression for longitudinal data analysis in the presence of time-dependent covariates

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
I-Chen Chen ◽  
Philip M. Westgate
Keyword(s):  
Parameter Estimation ◽  
Longitudinal Data ◽  
Quantile Regression ◽  
Mean Squared Error ◽  
Time Dependent ◽  
Regression Methods ◽  
Squared Error ◽  
Highly Correlated ◽  
Time Dependent Covariates ◽  
Independence Structure

AbstractWhen observations are correlated, modeling the within-subject correlation structure using quantile regression for longitudinal data can be difficult unless a working independence structure is utilized. Although this approach ensures consistent estimators of the regression coefficients, it may result in less efficient regression parameter estimation when data are highly correlated. Therefore, several marginal quantile regression methods have been proposed to improve parameter estimation. In a longitudinal study some of the covariates may change their values over time, and the topic of time-dependent covariate has not been explored in the marginal quantile literature. As a result, we propose an approach for marginal quantile regression in the presence of time-dependent covariates, which includes a strategy to select a working type of time-dependency. In this manuscript, we demonstrate that our proposed method has the potential to improve power relative to the independence estimating equations approach due to the reduction of mean squared error.

scholarly journals Estimating time-varying effects for overdispersed recurrent events data with treatment switching

Biometrika ◽  
2013 ◽  
Vol 100 (2) ◽  
pp. 339-354 ◽  
Author(s):  
Q. Chen ◽  
D. Zeng ◽  
J. G. Ibrahim ◽  
M. Akacha ◽  
H. Schmidli
Keyword(s):  
Recurrent Events ◽  
Time Varying ◽  
Treatment Switching ◽  
Recurrent Events Data ◽  
Events Data

scholarly journals Flexible parametric model for survival data subject to dependent censoring

2019 ◽  
Vol 62 (1) ◽  
pp. 136-156 ◽  
Author(s):  
Negera Wakgari Deresa ◽  
Ingrid Van Keilegom
Keyword(s):  
Survival Data ◽  
Parametric Model ◽  
Dependent Censoring ◽  
Flexible Parametric Model ◽  
Data Subject

Ascertainment correction in frailty models for recurrent events data

2016 ◽  
Vol 35 (23) ◽  
pp. 4183-4201
Author(s):  
Theodor A. Balan ◽  
Marianne A. Jonker ◽  
Paul C. Johannesma ◽  
Hein Putter
Keyword(s):  
Recurrent Events ◽  
Frailty Models ◽  
Recurrent Events Data ◽  
Events Data ◽  
Ascertainment Correction

scholarly journals Estimation and Inference of Quantile Regression for Survival Data Under Biased Sampling

2017 ◽  
Vol 112 (520) ◽  
pp. 1571-1586 ◽  
Author(s):  
Gongjun Xu ◽  
Tony Sit ◽  
Lan Wang ◽  
Chiung-Yu Huang
Keyword(s):  
Quantile Regression ◽  
Survival Data ◽  
Biased Sampling
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