Skip to main content
Skip to main menu Skip to spotlight region Skip to secondary region Skip to UGA region Skip to Tertiary region Skip to Quaternary region Skip to unit footer

Slideshow

Limin Peng

Peng
Emory University

Quantile regression offers great flexibility in assessing covariate effects on event times, thereby attracting considerable interests in its applications in survival analysis. However, currently available methods often require stringent assumptions on the censoring mechanism or residual distribution, or complex algorithms, which may complicate both theoretical arguments and inferences. In this paper we develop a new quantile regression approach for survival data subject to conditionally independent censoring. The proposed martingale-based estimating equations naturally lead to a simple algorithm that only involves minimizations of L1 type convex functions. We establish uniform consistency and weak convergence of the resultant estimators. Inferences are developed accordingly, including hypothesis testing, second-stage inference, and model diagnostics. We evaluate the finite-sample performance of the proposed methods via extensive simulation studies. An analysis of a recent dialysis study illustrates the practical utility of our proposals.

Support us

We appreciate your financial support. Your gift is important to us and helps support critical opportunities for students and faculty alike, including lectures, travel support, and any number of educational events that augment the classroom experience. Click here to learn more about giving.

Every dollar given has a direct impact upon our students and faculty.