Survival analysis with temporal covariate effects
Survival analysis with temporal covariate effects
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DOI：
10.1093/biomet/asm058
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发表时间：
2007-08-01
期刊：
影响因子：
2.7
通讯作者：
Huang, Yijian
中科院分区：
文献类型：
--
作者：
Peng, Limin;Huang, Yijian
我们提出了一个自然的推广的考克斯回归模型，其中的回归系数有直接的解释作为时间协变量对生存函数的影响。在条件独立截尾机制下，我们提出了一个基于鞅的方程组的无平滑估计方法。我们的估计是一致一致的，弱收敛到高斯过程。提出了一种简单的近似估计系数极限分布的方法。第二阶段的推理与时变系数相应地开发。仿真和一个真实的例子说明了所提出的方法的实用性。最后，我们将这个时间协变量效应的建议扩展到一般的线性变换模型，并与加性风险模型建立了联系。
We propose a natural generalization of the Cox regression model, in which the regression coefficients have direct interpretations as temporal covariate effects on the survival function. Under the conditionally independent censoring mechanism, we develop a smoothing-free estimation procedure with a set of martingale-based equations. Our estimator is shown to be uniformly consistent and to converge weakly to a Gaussian process. A simple resampling method is proposed for approximating the limiting distribution of the estimated coefficients. Second-stage inferences with time-varying coefficients are developed accordingly. Simulations and a real example illustrate the practical utility of the proposed method. Finally, we extend this proposal of temporal covariate effects to the general class of linear transformation models and also establish a connection with the additive hazards model.