Application of seemingly unrelated regression in medical data with intermittently observed time-dependent covariates.
Application of seemingly unrelated regression in medical data with intermittently observed time-dependent covariates.
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DOI：
10.1155/2012/821643
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发表时间：
2012
影响因子：
--
通讯作者：
Pakfetrat M
中科院分区：
文献类型：
--
作者：
Keshavarzi S;Ayatollahi SM;Zare N;Pakfetrat M
背景。在许多使用纵向数据的研究中，只能间歇性地测量依赖于时间的协变量（并非在所有观察时间），这给标准统计分析带来了困难。这种情况在医学研究中很常见，应对这一挑战的方法将会很有用。方法。在本研究中，我们针对纵向数据中的每个观察时间以及间歇性观察到的时间依赖性协变量执行了基于看似不相关的回归（SUR）的模型，并进一步将这些模型与三种经典插补程序下的混合效应回归模型（MRM）进行了比较。进行模拟研究以比较不同建模选择的估计系数的样本大小特性。结果。一般来说，所提出的模型在存在间歇性观察的时间依赖性协变量的情况下表现出良好的性能。然而，当我们仅考虑协变量的观察值而不进行任何插补时，产生的偏差更大。与使用经典插补方法的 MRM 相比，所提出的基于 SUR 的模型的性能几乎相似，且偏差和 MSE 大致相等。结论。模拟研究表明，在间歇性观察时间相关协变量的情况下，基于 SUR 的模型与 MRM 一样有效。因此，它可以用作 MRM 的替代品。
Background. In many studies with longitudinal data, time-dependent covariates can only be measured intermittently (not at all observation times), and this presents difficulties for standard statistical analyses. This situation is common in medical studies, and methods that deal with this challenge would be useful. Methods. In this study, we performed the seemingly unrelated regression (SUR) based models, with respect to each observation time in longitudinal data with intermittently observed time-dependent covariates and further compared these models with mixed-effect regression models (MRMs) under three classic imputation procedures. Simulation studies were performed to compare the sample size properties of the estimated coefficients for different modeling choices. Results. In general, the proposed models in the presence of intermittently observed time-dependent covariates showed a good performance. However, when we considered only the observed values of the covariate without any imputations, the resulted biases were greater. The performances of the proposed SUR-based models in comparison with MRM using classic imputation methods were nearly similar with approximately equal amounts of bias and MSE. Conclusion. The simulation study suggests that the SUR-based models work as efficiently as MRM in the case of intermittently observed time-dependent covariates. Thus, it can be used as an alternative to MRM.
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