Ning Jing, Qin Jing, Shen Yu
Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center.
Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases.
Stat Sin. 2014 Jan 1;24(1):313-333. doi: 10.5705/ss.2011.197.
A semiparametric accelerated failure time (AFT) model is proposed to evaluate the effects of risk factors on the unbiased failure times for the target population given the observed length-biased data. The analysis of length-biased data is complicated by informative right censoring due to the biased sampling mechanism, and consequently the techniques for conventional survival analysis are not applicable. We propose estimating equation methods for estimation and show the asymptotic properties of the proposed estimators. The small sample performance of the estimating methods are investigated and compared with that of existing methods under various underlying distributions and censoring mechanisms. We apply the proposed model and estimating methods to a prevalent cohort study, the Canadian Study of Health and Aging (CSHA), to evaluate the survival duration according to diagnosis of subtype of dementia.
提出了一种半参数加速失效时间（AFT）模型，用于在给定观察到的长度偏倚数据的情况下，评估风险因素对目标人群无偏失效时间的影响。由于有偏抽样机制导致的信息性右删失，使得长度偏倚数据的分析变得复杂，因此传统生存分析技术并不适用。我们提出了用于估计的估计方程方法，并展示了所提出估计量的渐近性质。研究了估计方法的小样本性能，并在各种潜在分布和删失机制下与现有方法进行了比较。我们将所提出的模型和估计方法应用于一项流行队列研究——加拿大健康与老龄化研究（CSHA），以根据痴呆亚型诊断评估生存持续时间。
