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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 955-965 |
| Number of pages | 11 |
| Journal | Statistical Methods in Medical Research |
| Volume | 27 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 1 2018 |
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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
A censored quantile regression approach for the analysis of time to event data. / Xue, Xiaonan (Nan); Xie, Xianhong; Strickler, Howard.
In: Statistical Methods in Medical Research, Vol. 27, No. 3, 01.03.2018, p. 955-965.Research output: Contribution to journal › Article
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TY - JOUR
T1 - A censored quantile regression approach for the analysis of time to event data
AU - Xue, Xiaonan (Nan)
AU - Xie, Xianhong
AU - Strickler, Howard
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
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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 -