Buckley-James-Type Estimator with Right-Censored and Length-Biased Data

• Activity Number: 506
• Type: Contributed
• Date/Time: Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Jing Ning*+ and Jing Qin and Yu Shen
• Companies: The University of Texas School of Public Health and National Institute of Allergy and Infectious Diseases and The University of Texas MD Anderson Cancer Center
• Keywords: Accelerated failure time model ; Buckley-James Estimator ; Estimating equation ; Length-biased sampling ; Prevalent cohort

Abstract

We present a natural generalization of the Buckley-James-type estimator for traditional survival data to right-censored length-biased data under the accelerated failure time model. Length-biased data are often encountered in prevalent cohort studies and cancer screening trials. Informative right censoring induced by length-biased sampling creates additional challenges in modeling the effects of risk factors on the unbiased failure times for the target population. We evaluate covariate effects on the failure times of the target population under the accelerated failure time model given the observed length-biased data. We construct a Buckley-James-type estimating equation, develop an iterative computing algorithm, and establish the asymptotic properties of the estimators. We assess the finite-sample properties of the proposed estimators against the estimators obtained from the existing methods. Data from a prevalent cohort study of patients with dementia are used to illustrate the proposed methodology.