A risk set calibration method for failure time regression by using a covariate reliability sample
A risk set calibration method for failure time regression by using a covariate reliability sample
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DOI：
10.1111/1467-9868.00317
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发表时间：
2001-01-01
影响因子：
5.8
通讯作者：
Prentice, RL
中科院分区：
文献类型：
--
作者：
Xie, SX;Wang, CY;Prentice, RL
当回归变量受到测量误差的影响时，考虑Cox失效时间模型中的回归参数估计，假设重复回归向量测量服从经典测量模型，我们可以考虑一种普通的回归校正方法，在给定可用协变量测量的情况下，用对其条件期望的估计来代替未观察到的协变量。然而，由于时间轴上由于失败或审查而从风险集合中撤出的比率通常取决于协变量，因此我们可以通过在每个风险集合内重新校准来改进回归参数估计器。研究了这种风险集回归校正估计的渐近性和小样本性质。基于每个风险集合中的最小二乘校准的简单估计器似乎能够消除在极端测量误差情况下普通回归校准估计器存在的大部分偏差。发展了相应的渐近分布理论，利用计算机模拟研究了小样本的性质，并给出了算例。
Regression parameter estimation in the Cox failure time model is considered when regression variables are subject to measurement error, Assuming that repeat regression vector measurements adhere to a classical measurement model, we can consider an ordinary regression calibration approach in which the unobserved covariates are replaced by an estimate of their conditional expectation given available covariate measurements. However, since the rate of withdrawal from the risk set across the time axis, due to failure or censoring, will typically depend on covariates, we may Improve the regression parameter estimator by recalibrating within each risk set. The asymptotic and small sample properties of such a risk set regression calibration estimator are studied. A simple estimator based on a least squares calibration in each risk set appears able to eliminate much of the bias that attends the ordinary regression calibration estimator under extreme measurement error circumstances. Corresponding asymptotic distribution theory is developed, small sample properties are studied using computer simulations and an illustration is provided.