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

Yuye Zou and Chengxin Wu

Computational Statistics & Data Analysis, 2023, vol. 182, issue C

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.

Keywords: Cause-of-failure information; Composite quantile regression; Missing at random; Single-index coefficient model; Variable selection (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:182:y:2023:i:c:s0167947323000221

DOI: 10.1016/j.csda.2023.107711

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