Sun Yifei, Chan Kwun Chuen Gary, Qin Jing
Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland 21205, U.S.A.
Department of Biostatistics, University of Washington, Seattle, Washington 98195, U.S.A.
Biometrics. 2018 Mar;74(1):77-85. doi: 10.1111/biom.12727. Epub 2017 May 15.
Length-biased survival data subject to right-censoring are often collected from a prevalent cohort. However, informative right censoring induced by the sampling design creates challenges in methodological development. While certain conditioning arguments could circumvent the problem of informative censoring, related rank estimation methods are typically inefficient because the marginal likelihood of the backward recurrence time is not ancillary. Under a semiparametric accelerated failure time model, an overidentified set of log-rank estimating equations is constructed based on the left-truncated right-censored data and backward recurrence time. Efficient combination of the estimating equations is simplified by exploiting an asymptotic independence property between two sets of estimating equations. A fast algorithm is studied for solving non-smooth, non-monotone estimating equations. Simulation studies confirm that the overidentified rank estimator can have a substantially improved estimation efficiency compared to just-identified rank estimators. The proposed method is applied to a dementia study for illustration.
长度偏倚的生存数据（受右删失影响）通常是从一个现患队列中收集的。然而，抽样设计所导致的信息性右删失在方法学发展中带来了挑战。虽然某些条件论证可以规避信息性删失的问题，但相关的秩估计方法通常效率不高，因为反向复发时间的边际似然不是辅助的。在半参数加速失效时间模型下，基于左截断右删失数据和反向复发时间构建了一组超定的对数秩估计方程。通过利用两组估计方程之间的渐近独立性性质，简化了估计方程的有效组合。研究了一种用于求解非光滑、非单调估计方程的快速算法。模拟研究证实，与恰好识别的秩估计器相比，超定秩估计器的估计效率可以有显著提高。所提出的方法应用于一项痴呆症研究以作说明。
