Abstract
Censored quantile regression is an important alternative to the Cox proportional hazards model in survival analysis. In contrast to the usual central covariate effects, quantile regression can effectively characterize the covariate effects at different quantiles of the survival time. When covariates are measured with errors, it is known that naively treating mismeasured covariates as error-free would result in estimation bias. Under censored quantile regression, we propose smoothed and corrected estimating equations to obtain consistent estimators. We establish consistency and asymptotic normality for the proposed estimators of quantile regression coefficients. Compared with the naive estimator, the proposed method can eliminate the estimation bias under various measurement error distributions and model error distributions. We conduct simulation studies to examine the finite-sample properties of the new method and apply it to a lung cancer study. Supplementary materials for this article are available online.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1670-1683 |
| Number of pages | 14 |
| Journal | Journal of the American Statistical Association |
| Volume | 110 |
| Issue number | 512 |
| DOIs | |
| State | Published - Oct 2 2015 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Statistics, Probability and Uncertainty
Cite this
}
Smoothed and Corrected Score Approach to Censored Quantile Regression With Measurement Errors. / Wu, Yuanshan; Ma, Yanyuan; Yin, Guosheng.
In: Journal of the American Statistical Association, Vol. 110, No. 512, 02.10.2015, p. 1670-1683.Research output: Contribution to journal › Article
TY - JOUR
T1 - Smoothed and Corrected Score Approach to Censored Quantile Regression With Measurement Errors
AU - Wu, Yuanshan
AU - Ma, Yanyuan
AU - Yin, Guosheng
PY - 2015/10/2
Y1 - 2015/10/2
N2 - Censored quantile regression is an important alternative to the Cox proportional hazards model in survival analysis. In contrast to the usual central covariate effects, quantile regression can effectively characterize the covariate effects at different quantiles of the survival time. When covariates are measured with errors, it is known that naively treating mismeasured covariates as error-free would result in estimation bias. Under censored quantile regression, we propose smoothed and corrected estimating equations to obtain consistent estimators. We establish consistency and asymptotic normality for the proposed estimators of quantile regression coefficients. Compared with the naive estimator, the proposed method can eliminate the estimation bias under various measurement error distributions and model error distributions. We conduct simulation studies to examine the finite-sample properties of the new method and apply it to a lung cancer study. Supplementary materials for this article are available online.
AB - Censored quantile regression is an important alternative to the Cox proportional hazards model in survival analysis. In contrast to the usual central covariate effects, quantile regression can effectively characterize the covariate effects at different quantiles of the survival time. When covariates are measured with errors, it is known that naively treating mismeasured covariates as error-free would result in estimation bias. Under censored quantile regression, we propose smoothed and corrected estimating equations to obtain consistent estimators. We establish consistency and asymptotic normality for the proposed estimators of quantile regression coefficients. Compared with the naive estimator, the proposed method can eliminate the estimation bias under various measurement error distributions and model error distributions. We conduct simulation studies to examine the finite-sample properties of the new method and apply it to a lung cancer study. Supplementary materials for this article are available online.
UR - http://www.scopus.com/inward/record.url?scp=84954415188&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84954415188&partnerID=8YFLogxK
U2 - 10.1080/01621459.2014.989323
DO - 10.1080/01621459.2014.989323
M3 - Article
AN - SCOPUS:84954415188
VL - 110
SP - 1670
EP - 1683
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
SN - 0162-1459
IS - 512
ER -