When observations are correlated, modeling the within-subject correlation structure using quantile regression for longitudinal data can be difficult unless a working independence structure is utilized. Although this approach ensures consistent estimators of the regression coefficients, it may result in less efficient regression parameter estimation when data are highly correlated. Therefore, several marginal quantile regression methods have been proposed to improve parameter estimation. In a longitudinal study some of the covariates may change their values over time, and the topic of time-dependent covariate has not been explored in the marginal quantile literature. As a result, we propose an approach for marginal quantile regression in the presence of time-dependent covariates, which includes a strategy to select a working type of time-dependency. In this manuscript, we demonstrate that our proposed method has the potential to improve power relative to the independence estimating equations approach due to the reduction of mean squared error.

中文翻译：

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存在时间相关协变量的纵向数据分析的边际分位数回归

当观察结果相关时，除非使用工作独立性结构，否则使用纵向数据的分位数回归对主体内相关结构进行建模可能很困难。尽管这种方法可以确保回归系数的估计值一致，但当数据高度相关时，它可能会导致回归参数估计效率降低。因此，已经提出了几种边际分位数回归方法来改进参数估计。在纵向研究中，一些协变量可能会随着时间的推移而改变它们的值，并且在边际分位数文献中尚未探讨时间相关协变量的主题。因此，我们提出了一种在存在时间依赖性协变量的情况下进行边际分位数回归的方法，其中包括选择一种工作类型的时间依赖性的策略。