A longitudinal measurement error model with a semicontinuous covariate
A longitudinal measurement error model with a semicontinuous covariate
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DOI：
10.1111/j.1541-0420.2005.00342.x
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发表时间：
2005-09-01
期刊：
影响因子：
1.9
通讯作者：
Palta, M
中科院分区：
文献类型：
--
作者：
Li, L;Shao, J;Palta, M
回归中的协变量测量误差通常假设以加法或乘法方式作用于真实协变量值。然而，这种假设并不适用于威斯康星州睡眠队列研究（WSCS）中睡眠呼吸障碍（SDB）的测量误差。真正的协变量是SDB的严重程度，观察到的替代变量是每单位睡眠时间的呼吸暂停次数，其具有非负的非连续分布，点质量为零。我们提出了一个潜在的变量测量误差模型的误差结构在这种情况下，并实现它在一个线性混合模型。估计过程类似于回归校准，但涉及潜在变量的分布假设。建模和模型拟合策略进行了探索和说明，通过一个例子从WSCS。
Covariate measurement error in regression is typically assumed to act in an additive or multiplicative manner on the true covariate value. However, such an assumption does not hold for the measurement error of sleep-disordered breathing (SDB) in the Wisconsin Sleep Cohort Study (WSCS). The true covariate is the severity of SDB, and the observed surrogate is the number of breathing pauses per unit time of sleep, which has a nonnegative semicontinuous distribution with a point mass at zero. We propose a latent variable measurement error model for the error structure in this situation and implement it in a linear mixed model. The estimation procedure is similar to regression calibration but involves a distributional assumption for the latent variable. Modeling and model-fitting strategies are explored and illustrated through an example from the WSCS.