Wang You-Gan, Zhao Yuning
CSIRO Mathematical and Information Sciences, CSIRO Long Pocket Laboratories, 120 Meiers Road, Indooroopilly, Queensland 4068, Australia.
Biometrics. 2007 Sep;63(3):681-9. doi: 10.1111/j.1541-0420.2006.00728.x.
We consider the analysis of longitudinal data when the covariance function is modeled by additional parameters to the mean parameters. In general, inconsistent estimators of the covariance (variance/correlation) parameters will be produced when the "working" correlation matrix is misspecified, which may result in great loss of efficiency of the mean parameter estimators (albeit the consistency is preserved). We consider using different "working" correlation models for the variance and the mean parameters. In particular, we find that an independence working model should be used for estimating the variance parameters to ensure their consistency in case the correlation structure is misspecified. The designated "working" correlation matrices should be used for estimating the mean and the correlation parameters to attain high efficiency for estimating the mean parameters. Simulation studies indicate that the proposed algorithm performs very well. We also applied different estimation procedures to a data set from a clinical trial for illustration.
当协方差函数通过均值参数之外的其他参数进行建模时，我们考虑对纵向数据进行分析。一般来说，当“工作”相关矩阵设定错误时，会产生协方差（方差/相关性）参数的不一致估计量，这可能会导致均值参数估计量的效率大幅损失（尽管一致性得以保留）。我们考虑对方差参数和均值参数使用不同的“工作”相关模型。特别地，我们发现应使用独立工作模型来估计方差参数，以确保在相关结构设定错误的情况下它们的一致性。应使用指定的“工作”相关矩阵来估计均值和相关参数，以获得估计均值参数的高效率。模拟研究表明所提出的算法表现非常出色。我们还将不同的估计程序应用于一个来自临床试验的数据集进行说明。
