Chiou Sy Han, Kang Sangwook, Kim Junghi, Yan Jun
Department of Mathematics and Statistics, University of Minnesota, Duluth, Duluth, MN, USA.
Lifetime Data Anal. 2014 Oct;20(4):599-618. doi: 10.1007/s10985-014-9292-x. Epub 2014 Feb 19.
The semiparametric accelerated failure time (AFT) model is not as widely used as the Cox relative risk model due to computational difficulties. Recent developments in least squares estimation and induced smoothing estimating equations for censored data provide promising tools to make the AFT models more attractive in practice. For multivariate AFT models, we propose a generalized estimating equations (GEE) approach, extending the GEE to censored data. The consistency of the regression coefficient estimator is robust to misspecification of working covariance, and the efficiency is higher when the working covariance structure is closer to the truth. The marginal error distributions and regression coefficients are allowed to be unique for each margin or partially shared across margins as needed. The initial estimator is a rank-based estimator with Gehan's weight, but obtained from an induced smoothing approach with computational ease. The resulting estimator is consistent and asymptotically normal, with variance estimated through a multiplier resampling method. In a large scale simulation study, our estimator was up to three times as efficient as the estimateor that ignores the within-cluster dependence, especially when the within-cluster dependence was strong. The methods were applied to the bivariate failure times data from a diabetic retinopathy study.
由于计算困难，半参数加速失效时间（AFT）模型的使用不如Cox相对风险模型广泛。最近在截尾数据的最小二乘估计和诱导平滑估计方程方面的进展提供了有前景的工具，使AFT模型在实际应用中更具吸引力。对于多变量AFT模型，我们提出了一种广义估计方程（GEE）方法，将GEE扩展到截尾数据。回归系数估计量的一致性对于工作协方差的错误设定具有稳健性，并且当工作协方差结构更接近真实情况时效率更高。允许边际误差分布和回归系数对于每个边际是唯一的，或者根据需要在边际之间部分共享。初始估计量是具有Gehan权重的基于秩的估计量，但通过计算简便的诱导平滑方法获得。所得估计量是一致的且渐近正态，其方差通过乘数重抽样方法估计。在大规模模拟研究中，我们的估计量比忽略聚类内相关性的估计量效率高多达三倍，特别是当聚类内相关性很强时。这些方法应用于来自糖尿病视网膜病变研究的双变量失效时间数据。
