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首页> 外文期刊>Computational statistics >Joint modeling for mixed-effects quantile regression of longitudinal data with detection limits and covariates measured with error, with application to AIDS studies
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Joint modeling for mixed-effects quantile regression of longitudinal data with detection limits and covariates measured with error, with application to AIDS studies

机译：用误差测量的纵向数据的混合效应分位数的联合建模与误差测量，用效果施用辅助研究

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摘要

It is very common in AIDS studies that response variable (e.g., HIV viral load) may be subject to censoring due to detection limits while covariates (e.g., CD4 cell count) may be measured with error. Failure to take censoring in response variable and measurement errors in covariates into account may introduce substantial bias in estimation and thus lead to unreliable inference. Moreover, with non-normal and/or heteroskedastic data, traditional mean regression models are not robust to tail reactions. In this case, one may find it attractive to estimate extreme causal relationship of covariates to a dependent variable, which can be suitably studied in quantile regression framework. In this paper, we consider joint inference of mixed-effects quantile regression model with right-censored responses and errors in covariates. The inverse censoring probability weighted method and the orthogonal regression method are combined to reduce the biases of estimation caused by censored data and measurement errors. Under some regularity conditions, the consistence and asymptotic normality of estimators are derived. Finally, some simulation studies are implemented and a HIV/AIDS clinical data set is analyzed to to illustrate the proposed procedure.
机译：在艾滋病研究中，响应变量（例如，HIV病毒负载）可能受到由于检测限而受到抗抗的研究，而可以用误差测量协变量（例如，CD4细胞计数）。未能在响应变量中进行审查和协变量中的测量误差可能会在估计中引入大量偏见，从而导致不可靠的推断。此外，对于非正常和/或异源性数据，传统的平均回归模型对尾部反应并不稳健。在这种情况下，可以发现它有吸引力来估计协变量对依赖变量的极端因果关系，这可以在量化的回归框架中适当地研究。在本文中，我们考虑了混合效应量回归模型的联合推理，具有协变量的右缩短的反应和错误。逆猝死概率加权方法和正交回归方法被组合以减少由截取的数据和测量误差引起的估计的偏差。在一些规律性条件下，衍生估算者的一致性和渐近常态。最后，实施了一些模拟研究，分析了艾滋病毒/艾滋病临床数据集以说明所提出的程序。

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