A censored quantile regression approach for the analysis of time to event data

Research output: Contribution to journalArticle

Abstract

The commonly used statistical model for studying time to event data, the Cox proportional hazards model, is limited by the assumption of a constant hazard ratio over time (i.e., proportionality), and the fact that it models the hazard rate rather than the survival time directly. The censored quantile regression model, defined on the quantiles of time to event, provides an alternative that is more flexible and interpretable. However, the censored quantile regression model has not been widely adopted in clinical research, due to the complexity involved in interpreting its results properly and consequently the difficulty to appreciate its advantages over the Cox proportional hazards model, as well as the absence of adequate validation procedure. In this paper, we addressed these limitations by (1) using both simulated examples and data from National Wilms’ Tumor clinical trials to illustrate proper interpretation of the censored quantile regression model and the differences and the advantages of the model compared to the Cox proportional hazards model; and (2) developing a validation procedure for the predictive censored quantile regression model. The performance of this procedure was examined using simulation studies. Overall, we recommend the use of censored quantile regression model, which permits a more sensitive analysis of time to event data together with the Cox proportional hazards model.

LanguageEnglish (US)
Pages955-965
Number of pages11
JournalStatistical Methods in Medical Research
Volume27
Issue number3
DOIs
StatePublished - Mar 1 2018

Fingerprint

Censored Regression
Quantile Regression
Cox Proportional Hazards Model
Proportional Hazards Models
Regression Model
Regression Analysis
Wilms Tumor
Hazard Rate
Survival Time
Statistical Models
Quantile
Hazard
Clinical Trials
Statistical Model
Tumor
Simulation Study
Alternatives
Research
Model

Keywords

  • accelerated failure time model
  • cross-validation
  • hazards ratio
  • prediction
  • Proportionality
  • quantile
  • validation

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

Cite this

@article{eb055d2cb6fd4cc487c7f1f8fc55b095,
title = "A censored quantile regression approach for the analysis of time to event data",
abstract = "The commonly used statistical model for studying time to event data, the Cox proportional hazards model, is limited by the assumption of a constant hazard ratio over time (i.e., proportionality), and the fact that it models the hazard rate rather than the survival time directly. The censored quantile regression model, defined on the quantiles of time to event, provides an alternative that is more flexible and interpretable. However, the censored quantile regression model has not been widely adopted in clinical research, due to the complexity involved in interpreting its results properly and consequently the difficulty to appreciate its advantages over the Cox proportional hazards model, as well as the absence of adequate validation procedure. In this paper, we addressed these limitations by (1) using both simulated examples and data from National Wilms’ Tumor clinical trials to illustrate proper interpretation of the censored quantile regression model and the differences and the advantages of the model compared to the Cox proportional hazards model; and (2) developing a validation procedure for the predictive censored quantile regression model. The performance of this procedure was examined using simulation studies. Overall, we recommend the use of censored quantile regression model, which permits a more sensitive analysis of time to event data together with the Cox proportional hazards model.",
keywords = "accelerated failure time model, cross-validation, hazards ratio, prediction, Proportionality, quantile, validation",
author = "Xiaonan Xue and Xianhong Xie and Strickler, {Howard D.}",
year = "2018",
month = "3",
day = "1",
doi = "10.1177/0962280216648724",
language = "English (US)",
volume = "27",
pages = "955--965",
journal = "Statistical Methods in Medical Research",
issn = "0962-2802",
publisher = "SAGE Publications Ltd",
number = "3",

}

TY - JOUR

T1 - A censored quantile regression approach for the analysis of time to event data

AU - Xue, Xiaonan

AU - Xie, Xianhong

AU - Strickler, Howard D.

PY - 2018/3/1

Y1 - 2018/3/1

N2 - The commonly used statistical model for studying time to event data, the Cox proportional hazards model, is limited by the assumption of a constant hazard ratio over time (i.e., proportionality), and the fact that it models the hazard rate rather than the survival time directly. The censored quantile regression model, defined on the quantiles of time to event, provides an alternative that is more flexible and interpretable. However, the censored quantile regression model has not been widely adopted in clinical research, due to the complexity involved in interpreting its results properly and consequently the difficulty to appreciate its advantages over the Cox proportional hazards model, as well as the absence of adequate validation procedure. In this paper, we addressed these limitations by (1) using both simulated examples and data from National Wilms’ Tumor clinical trials to illustrate proper interpretation of the censored quantile regression model and the differences and the advantages of the model compared to the Cox proportional hazards model; and (2) developing a validation procedure for the predictive censored quantile regression model. The performance of this procedure was examined using simulation studies. Overall, we recommend the use of censored quantile regression model, which permits a more sensitive analysis of time to event data together with the Cox proportional hazards model.

AB - The commonly used statistical model for studying time to event data, the Cox proportional hazards model, is limited by the assumption of a constant hazard ratio over time (i.e., proportionality), and the fact that it models the hazard rate rather than the survival time directly. The censored quantile regression model, defined on the quantiles of time to event, provides an alternative that is more flexible and interpretable. However, the censored quantile regression model has not been widely adopted in clinical research, due to the complexity involved in interpreting its results properly and consequently the difficulty to appreciate its advantages over the Cox proportional hazards model, as well as the absence of adequate validation procedure. In this paper, we addressed these limitations by (1) using both simulated examples and data from National Wilms’ Tumor clinical trials to illustrate proper interpretation of the censored quantile regression model and the differences and the advantages of the model compared to the Cox proportional hazards model; and (2) developing a validation procedure for the predictive censored quantile regression model. The performance of this procedure was examined using simulation studies. Overall, we recommend the use of censored quantile regression model, which permits a more sensitive analysis of time to event data together with the Cox proportional hazards model.

KW - accelerated failure time model

KW - cross-validation

KW - hazards ratio

KW - prediction

KW - Proportionality

KW - quantile

KW - validation

UR - http://www.scopus.com/inward/record.url?scp=85042566462&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85042566462&partnerID=8YFLogxK

U2 - 10.1177/0962280216648724

DO - 10.1177/0962280216648724

M3 - Article

VL - 27

SP - 955

EP - 965

JO - Statistical Methods in Medical Research

T2 - Statistical Methods in Medical Research

JF - Statistical Methods in Medical Research

SN - 0962-2802

IS - 3

ER -