Cox regression with covariate measurement error
Cox regression with covariate measurement error
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DOI：
10.1111/1467-9469.00310
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发表时间：
2002-12-01
影响因子：
1
通讯作者：
Lin, DY
中科院分区：
文献类型：
--
作者：
Hu, CC;Lin, DY
本文讨论了协变量有误差时考克斯比例风险模型的参数估计问题。我们考虑经典的加性测量误差模型和一个更一般的模型，该模型将协变量的误测量版本表示为真实协变量加随机噪声的任意线性函数。只有矩条件被施加在协变量和测量误差的分布上。假设协变量是精确测量的验证集，我们开发了一类估计方程的向量值回归参数通过校正偏似然评分函数。证明了所得的估计量是相合的，渐近正态的，方差很容易估计。此外，Breslow估计的累积风险函数的校正版本的开发，这是一致一致的，适当的归一化后，弱收敛到一个零均值高斯过程。模拟研究表明，渐近近似工作以及实际的样本容量。的情况下，其中重复测量（而不是一个vididation集）是可用的也进行了研究。
This article deals with parameter estimation in the Cox proportional hazards model when covariates are measured with error. We consider both the classical additive measurement error model and a more general model which represents the mis-measured version of the covariate as an arbitrary linear function of the true covariate plus random noise. Only moment conditions are imposed on the distributions of the covariates and measurement error. Under the assumption that the covariates are measured precisely for a validation set, we develop a class of estimating equations for the vector-valued regression parameter by correcting the partial likelihood score function. The resultant estimators are proven to be consistent and asymptotically normal with easily estimated variances. Furthermore, a corrected version of the Breslow estimator for the cumulative hazard function is developed, which is shown to be uniformly consistent and, upon proper normalization, converges weakly to a zero-mean Gaussian process. Simulation studies indicate that the asymptotic approximations work well for practical sample sizes. The situation in which replicate measurements (instead of a vididation set) are available is also studied.