title: LOCAL BUCKLEY-JAMES ESTIMATION FOR HETEROSCEDASTIC ACCELERATED FAILURE TIME MODEL.
abstract: In survival analysis, the accelerated failure time model is a useful alternative to the popular Cox proportional hazards model due to its easy interpretation. Current estimation methods for the accelerated failure time model mostly assume independent and identically distributed random errors, but in many applications the conditional variance of log survival times depend on covariates exhibiting some form of heteroscedasticity. In this paper, we develop a local Buckley-James estimator for the accelerated failure time model with heteroscedastic errors. We establish the consistency and asymptotic normality of the proposed estimator and propose a resampling approach for inference. Simulations demonstrate that the proposed method is flexible and leads to more efficient estimation when heteroscedasticity is present. The value of the proposed method is further assessed by the analysis of a breast cancer data set.
authors.affiliation: Merck & Co., Inc, North Wales, PA 19454, U.S.A.; Department of Statistics, George Washington University, Washington, D.C. 20052, U.S.A.
authors.forename: Lei
authors.initials: L
authors.lastname: Pang
authors.affiliation: Merck & Co., Inc, North Wales, PA 19454, U.S.A.; Department of Statistics, George Washington University, Washington, D.C. 20052, U.S.A.
authors.forename: Wenbin
authors.initials: W
authors.lastname: Lu
authors.affiliation: Merck & Co., Inc, North Wales, PA 19454, U.S.A.; Department of Statistics, George Washington University, Washington, D.C. 20052, U.S.A.
authors.forename: Huixia Judy
authors.initials: HJ
authors.lastname: Wang
keywords: Accelerated failure time model; Buckley-James estimation; Heteroscedasticity; Kernel estimation; Local Kaplan-Meier; Survival analysis