标题： 带有广义估计方程的半参数多元加速失效时间模型 显示英文标题

标题： Semiparametric Multivariate Accelerated Failure Time Model with Generalized Estimating Equations

摘要： 半参数加速失效时间模型不像科克斯相对风险模型那样广泛使用，主要是由于计算上的困难。最近最小二乘估计和诱导平滑估计方程的发展提供了有前景的工具，使加速失效时间模型在实践中更具吸引力。对于半参数多元加速失效时间模型，我们提出了一种广义估计方程方法来通过工作相关结构考虑多元依赖关系。边缘误差分布可以相同（如在顺序事件设定下）或不同（如在平行事件设定下）。一些回归系数可以根据需要在边缘之间共享。初始估计量是一种基于等级的估计量，采用Gehan权重，但通过诱导平滑方法获得，以简化计算。所得估计量是一致且渐近正态的，其方差通过乘数重采样方法进行估计。在模拟研究中，我们的估计量比初始估计量效率高出三倍，尤其是在较强的多元依赖性和较高的删失百分比情况下。两个实际例子展示了所提出方法的实用性。 显示英文摘要

摘要： The semiparametric accelerated failure time model is not as widely used as the Cox relative risk model mainly due to computational difficulties. Recent developments in least squares estimation and induced smoothing estimating equations provide promising tools to make the accelerate failure time models more attractive in practice. For semiparametric multivariate accelerated failure time models, we propose a generalized estimating equation approach to account for the multivariate dependence through working correlation structures. The marginal error distributions can be either identical as in sequential event settings or different as in parallel event settings. Some regression coefficients can be 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 computation ease. The resulting estimator is consistent and asymptotically normal, with a variance estimated through a multiplier resampling method. In a simulation study, our estimator was up to three times as efficient as the initial estimator, especially with stronger multivariate dependence and heavier censoring percentage. Two real examples demonstrate the utility of the proposed method.