Marginal regression models for multivariate failure time data
Marginal regression models for multivariate failure time data
复制标题
DOI：
10.2307/2669859
复制
发表时间：
1998-09-01
影响因子：
3.7
通讯作者：
Lin, DY
中科院分区：
文献类型：
--
作者：
Spiekerman, CF;Lin, DY
在本文中，我们提出了一个一般的COX型回归模型来描述多变量失效时间数据的边际分布。该模型具有嵌套结构，允许在不同的失效类型之间建立不同的基线风险函数，并对相同类型的失效次数施加共同的基线风险函数。本文证明了在独立工作假设下回归参数向量的最大“拟偏似然”估计与协方差矩阵一致且渐近正态，并给出了一致估计。此外，我们建立了累积基线风险函数的Aalen-Breslow型估计量的一致相合性和联合弱收敛性质，并发展了一种重抽样技术来逼近这些过程的联合分布，从而使人们能够在时间轴上和不同的失效类型上同时推断生存函数。最后，我们通过蒙特卡罗模拟评估了所提方法的小样本特性，并给出了一个在实际牙科研究中的应用。
In this article we propose a general Cox-type regression model to formulate the marginal distributions of multivariate failure time data. This model has a nested structure in that it allows different baseline hazard functions among distinct failure types and imposes a common baseline hazard function on the failure times of the same type. We prove that the maximum "quasi-partial-likelihood" estimator for the vector of regression parameters under the independence working assumption is consistent and asymptotically normal with a covariance matrix for which a consistent estimator is provided. Furthermore, we establish the uniform consistency and joint weak convergence of the Aalen-Breslow type estimators for the cumulative baseline hazard functions, and develop a resampling technique to approximate the joint distribution of these processes, which enables one to make simultaneous inference about the survival functions over the time axis and across failure types. Finally, we assess the small-sample properties of the proposed methods through Monte Carlo simulation, and present an application to a real dental study.