Buckley–James‐Type Estimator with Right‐Censored and Length‐Biased Data
Summary 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 (AFT) 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. In this article, we evaluate
covariate effects on the failure times of the target population under the AFT 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.
No Supplementary Data
Document Type: Research Article
Division of Biostatistics, The University of Texas School of Public Health, Houston, Texas 77030, U.S.A.
Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Bethesda, Maryland 20892, U.S.A.
Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, Texas 77030, U.S.A.
Publication date: 2011-12-01