Cho Youngjoo, Ghosh Debashis
Department of Statistics, The Pennsylvania State University, University Park, Pennsylvania, 16802, United States of America.
Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado, Aurora, Colorado, 80045, United States of America.
PLoS One. 2015 Apr 24;10(4):e0124381. doi: 10.1371/journal.pone.0124381. eCollection 2015.
Independent censoring is a crucial assumption in survival analysis. However, this is impractical in many medical studies, where the presence of dependent censoring leads to difficulty in analyzing covariate effects on disease outcomes. The semicompeting risks framework offers one approach to handling dependent censoring. There are two representative estimators based on an artificial censoring technique in this data structure. However, neither of these estimators is better than another with respect to efficiency (standard error). In this paper, we propose a new weighted estimator for the accelerated failure time (AFT) model under dependent censoring. One of the advantages in our approach is that these weights are optimal among all the linear combinations of the previously mentioned two estimators. To calculate these weights, a novel resampling-based scheme is employed. Attendant asymptotic statistical results for the estimator are established. In addition, simulation studies, as well as an application to real data, show the gains in efficiency for our estimator.
独立删失是生存分析中的一个关键假设。然而，这在许多医学研究中并不实际，在这些研究中，相依删失的存在导致分析协变量对疾病结局的影响变得困难。半竞争风险框架提供了一种处理相依删失的方法。在这种数据结构中，有两种基于人工删失技术的代表性估计量。然而，就效率（标准误差）而言，这两种估计量都不比另一种更好。在本文中，我们提出了一种在相依删失下用于加速失效时间（AFT）模型的新的加权估计量。我们方法的优点之一是，这些权重在上述两种估计量的所有线性组合中是最优的。为了计算这些权重，采用了一种基于重采样的新颖方案。建立了该估计量的伴随渐近统计结果。此外，模拟研究以及对实际数据的应用表明了我们的估计量在效率方面的提升。
