Ning Jing, Qin Jing, Shen Yu
Division of Biostatistics, The University of Texas School of Public Health, Houston, Texas 77030, USA.
Biometrics. 2011 Dec;67(4):1369-78. doi: 10.1111/j.1541-0420.2011.01568.x. Epub 2011 Mar 8.
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.
我们提出了一种将传统生存数据的Buckley-James型估计量自然推广到加速失效时间（AFT）模型下的右删失长度偏倚数据的方法。长度偏倚数据在现患队列研究和癌症筛查试验中经常遇到。由长度偏倚抽样引起的信息性右删失在对风险因素对目标人群无偏失效时间的影响进行建模时带来了额外的挑战。在本文中，我们在给定观察到的长度偏倚数据的情况下，评估AFT模型下协变量对目标人群失效时间的影响。我们构建了一个Buckley-James型估计方程，开发了一种迭代计算算法，并建立了估计量的渐近性质。我们将所提出估计量的有限样本性质与从现有方法获得的估计量进行了评估。来自一项痴呆症患者现患队列研究的数据用于说明所提出的方法。
