Quantile Regression Analysis of Length-Biased Survival Data

• Activity Number: 60
• Type: Topic Contributed
• Date/Time: Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Huixia Judy Wang*+ and Lan Wang
• Companies: North Carolina State University and University of Minnesota
• Keywords: censored quantile regression ; length-biased data ; Kaplan-Meier estimator ; kernel ; survival time

Abstract

Analysis of length-biased time-to-event data, which commonly arise in epidemiological cohort studies and cross-sectional surveys, has attracted considerable attention recently. Ignoring length-biased sampling often leads to severe bias in estimating the survival time in the general population. Existing work either completely ignore the covariate effects or use hazard or accelerated failure time regressions, which restrict the covariates to affect only the location of the transformed survival distribution. We propose a flexible quantile regression framework for analyzing the covariate effects on the population survival time under both length-biased sampling and random right censoring. This framework allows for easy interpretation of the statistical model. Furthermore, it allows the covariates to have different impacts at different tails of the survival distribution and thus is able to capture important population heterogeneity. Using an unbiased estimating equation approach, we develop two estimators, one for covariate-independent censoring and the other for covariate-dependent censoring. We establish the consistency and asymptotic normality theory for both estimators.