Quantile regression models for survival data with missing censoring indicators

The quantile regression model has increasingly become a useful approach for analyzing survival data due to its easy interpretation and flexibility in exploring the dynamic relationship between a time-to-event outcome and the covariates. In this paper, we consider the quantile regression model for su...

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Bibliographic Details
Published in:Statistical methods in medical research 2021-05, Vol.30 (5), p.1320-1331
Main Authors: Qiu, Zhiping, Ma, Huijuan, Chen, Jianwei, Dinse, Gregg E
Format: Article
Language:English
Subjects:
Algorithms
Asymptotic methods
Asymptotic properties
Estimation
Flexibility
Indicators
Regression models
Resampling
Simulation
Statistical analysis
Survival
Weighting
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Summary:The quantile regression model has increasingly become a useful approach for analyzing survival data due to its easy interpretation and flexibility in exploring the dynamic relationship between a time-to-event outcome and the covariates. In this paper, we consider the quantile regression model for survival data with missing censoring indicators. Based on the augmented inverse probability weighting technique, two weighted estimating equations are developed and corresponding easily implemented algorithms are suggested to solve the estimating equations. Asymptotic properties of the resultant estimators and the resampling-based inference procedures are established. Finally, the finite sample performances of the proposed approaches are investigated in simulation studies and a real data application.
ISSN:0962-2802
1477-0334
DOI:10.1177/0962280221995986