A pseudo-partial likelihood method for semiparametric survival regression with covariate errors
A pseudo-partial likelihood method for semiparametric survival regression with covariate errors
复制标题
DOI：
10.1198/016214505000000538
复制
发表时间：
2005-12-01
影响因子：
3.7
通讯作者：
Zucker, DM
中科院分区：
文献类型：
--
作者：
Zucker, DM
本文给出了协变量误差下考克斯比例风险模型中回归系数向量的一个估计。通过最大化类似于考克斯偏似然的似然型函数来获得估计量。似然函数涉及累积基线风险函数，用一个简单的估计量代替。该方法能够处理一般协变量误差结构，它不限于独立的加性误差模型。它可以应用于外部或内部验证样本的研究，也可以应用于替代协变量重复测量的研究。估计是一致的和渐近正态的，并估计的渐近协方差矩阵的推导。一般变换生存模型的一些扩展。模拟研究的设置与一个单一的易错二元协变量和一个单一的易错正态分布协变量的设置。这些模拟研究表明，该方法通常会产生低偏差的估计值和准确覆盖率的置信区间。效率结果相对于全参数最大似然。该方法适用于心脏研究的数据。
This article presents an estimator for the regression coefficient vector in the Cox proportional hazards model with covariate error. The estimator is obtained by maximizing a likelihood-type function similar to the Cox partial likelihood. The likelihood function involves the cumulative baseline hazard function, for which a simple estimator is substituted. The method is capable of handling general covariate error structures; it is not restricted to the independent additive error model. It can be applied to studies with either an external or internal validation sample, and also to studies with replicate measurements of the surrogate covariate. The estimator is shown to be consistent and asymptotically normal, and an estimate of the asymptotic covariance matrix is derived. Some extensions to general transformation survival models are indicated. Simulation studies are presented for a setup with a single error-prone binary covariate and a setup with a single error-prone normally distributed covariate. These simulation studies show that the method typically produces estimates with low bias and confidence intervals with accurate coverage rates. Efficiency results relative to fully parametric maximum likelihood are also presented. The method is applied to data from the Framingham Heart Study.