Rank regression analysis of multivariate failure time data based on marginal linear models
Rank regression analysis of multivariate failure time data based on marginal linear models
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DOI：
10.1111/j.1467-9469.2005.00487.x
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发表时间：
2006-03-01
影响因子：
1
通讯作者：
Ying, Z
中科院分区：
文献类型：
--
作者：
Jin, Z;Lin, DY;Ying, Z
多变量故障时间数据出现时，每个研究对象可能会遇到几种类型的故障或复发的某种现象，或故障时间在集群中采样。我们制定的边际分布，这样的多变量数据与半参数加速故障时间模型（即线性回归模型的对数转换的故障时间与任意误差分布），而离开相关的故障时间的依赖结构完全未指定。我们开发了基于秩的单调估计函数的回归参数的边缘模型的基础上右删失的观察。估计方程可以很容易地通过线性规划求解。所得的估计是一致的和渐近正态的。极限协方差矩阵可以很容易地估计由一种新的reservation方法，它不涉及非参数密度估计或数值导数的评价。所提出的估计是基于加权对数秩统计量的潜在非单调估计方程的一致根。仿真研究表明，新的推理过程在小样本情况下表现良好。提供了具有真实的医学数据的插图。
Multivariate failure time data arises when each study subject can potentially experience several types of failures or recurrences of a certain phenomenon, or when failure times are sampled in clusters. We formulate the marginal distributions of such multivariate data with semiparametric accelerated failure time models (i.e. linear regression models for log-transformed failure times with arbitrary error distributions) while leaving the dependence structures for related failure times completely unspecified. We develop rank-based monotone estimating functions for the regression parameters of these marginal models based on right-censored observations. The estimating equations can be easily solved via linear programming. The resultant estimators are consistent and asymptotically normal. The limiting covariance matrices can be readily estimated by a novel resampling approach, which does not involve non-parametric density estimation or evaluation of numerical derivatives. The proposed estimators represent consistent roots to the potentially nonmonotone estimating equations based on weighted log-rank statistics. Simulation studies show that the new inference procedures perform well in small samples. Illustrations with real medical data are provided.