Transformed linear quantile regression with censored survival data

摘要

Quantile regression provides a flexible method for analyzing survival data, and attracts considerable interest in survival analysis. In this article, we propose a new inference procedure for a class of power-transformed linear quantile regression models with survival data subject to conditionally independent censoring, and present a two-stage algorithm that is computationally simple and easy to implement. Consistency and asymptotic normality of the resulting estimators are established, and a simple resampling-based inference procedure is developed for variance estimation. The finite-sample behavior of the proposed methods is examined through extensive simulation studies. An application to a real data example from a health maintenance organization is provided.