SEMIPARAMETRIC INFERENCE FOR THE ACCELERATED LIFE MODEL WITH TIME-DEPENDENT COVARIATES
SEMIPARAMETRIC INFERENCE FOR THE ACCELERATED LIFE MODEL WITH TIME-DEPENDENT COVARIATES
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DOI：
10.1016/0378-3758(94)00039-x
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发表时间：
1995-03-01
影响因子：
0.9
通讯作者：
YING, ZL
中科院分区：
文献类型：
--
作者：
LIN, DY;YING, ZL
加速寿命模型假设多维协变量过程的失效时间相对于零值协变量过程的失效时间是收缩或扩张的。在本文中，收缩率，膨胀的协变量过程的参数函数，而基线故障时间分布是未指定的制定。回归参数向量的估计函数由似然得分函数激励，并采用具有时间依赖协变量的对数秩统计的形式。在适当的正则性条件下，证明了所得估计是强相合的和渐近正态的。简单的方法推导出一个子集的回归参数的推断，而把其他滋扰量。通过蒙特卡罗模拟的估计和检验程序的样本性质进行了研究。一个例子与著名的斯坦福大学心脏移植的数据提供。
The accelerated life model assumes that the failure time associated with a multi-dimensional covariate process is contracted or expanded relative to that of the zero-valued covariate process. In the present paper, the rate of contraction;expansion is formulated by a parametric function of the covariate process while the baseline failure time distribution is unspecified. Estimating functions for the vector of regression parameters are motivated by likelihood score functions and take the form of log rank statistics with time-dependent covariates. The resulting estimators are proven to be strongly consistent and asymptotically normal under suitable regularity conditions. Simple methods are derived for making inference about a subset of regression parameters while regarding others as nuisance quantities. Finite-sample properties of the estimation and testing procedures are investigated through Monte Carlo simulations. An illustration with the well-known Stanford heart transplant data is provided.