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 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.

Automated summary

The paper discusses the quantile regression model for survival data with missing censoring indicators, proposes two weighted estimating equations based on the augmented inverse probability weighting technique, and establishes asymptotic properties of the resultant estimators and resampling-based inference procedures. The performance of the proposed approaches is investigated in simulation studies and a real data application.