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Composite quantile regression analysis of survival data with missing cause-of-failure information and its application to breast cancer clinical trial.

Authors :
Zou, Yuye
Wu, Chengxin
Source :
Computational Statistics & Data Analysis. Jun2023, Vol. 182, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

The analysis of survival data can be challenging due to the presence of missing data. This paper proposes weighted composite quantile regression (CQR) for estimating a lot of quantile regression (QR) of survival data based on single-index coefficient model (SICM), which is a very general and flexible tool for exploring the relationship between response variable and a set of predictors. The statistical inference for SICM is considered when cause-of-failure information (censored or non-censored) is always observed. However, the cause-of-failure information may be missing at random (MAR) for various reasons. Regression calibration, imputation and inverse probability weighted approaches are applied to deal with the MAR assumption. The asymptotic normalities of the proposed estimators are established. Meanwhile, the oracle property of the variable selection based on adaptive LASSO penalty procedure is conducted. To assess the finite sample performance of the proposed estimators, simulation study with normal error and heavy-tail error are considered. As expected, the CQR estimators perform as good as the least-square estimators for normal error, and are more robust to heavy-tailed error. Finally, a breast cancer real data analysis is carried out to illustrate the proposed methodologies. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01679473
Volume :
182
Database :
Academic Search Index
Journal :
Computational Statistics & Data Analysis
Publication Type :
Periodical
Accession number :
162254540
Full Text :
https://doi.org/10.1016/j.csda.2023.107711