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 (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.
journal_name
Biometricsjournal_title
Biometricsauthors
Ning J,Qin J,Shen Ydoi
10.1111/j.1541-0420.2011.01568.xsubject
Has Abstractpub_date
2011-12-01 00:00:00pages
1369-78issue
4eissn
0006-341Xissn
1541-0420journal_volume
67pub_type
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