背景:在职业健康研究中,纵向环境暴露,生物监测数据经常受到右倾斜和左审查的影响,其中测量值低于检测限(LOD)。要解决右倾斜数据,通常的做法是对数变换数据并对几何平均值进行建模,假设对数正态分布。然而,如果转换后的数据不遵循已知的分布,对曝光的平均值进行建模可能会导致偏差并降低效率。此外,检查纵向数据时,某些协变量可能会随时间变化。
目的:开发预测分位数回归模型,以解决左审查和时间依赖协变量的问题,并定量评估先前和当前协变量是否可以预测当前和/或未来的暴露水平。
方法:为了解决这些问题,我们建议将不同的替代方法纳入分位数回归,并利用一种方法为协变量选择时间依赖性的工作类型。
结果:在一项模拟研究中,我们证明了,在不同类型的时间依赖协变量下,多重随机值插补的方法优于其他方法。我们还将我们的方法应用于碳纳米管和纳米纤维暴露研究。因变量是可吸入和可吸入气溶胶尺寸分数下元素碳的左删失质量。在这项研究中,我们确定了一些关于工人水平决定因素和工作任务的潜在时间相关协变量.
结论:当通过预测建模分析具有小于LOD值的纵向环境暴露和生物监测数据时,很少考虑协变量的时间依赖性。错误地将时间依赖性视为时间独立性将导致回归参数估计的效率损失。因此,我们通过左删失测量解决了纵向暴露和生物监测数据中的时变协变量,并通过不同的分位数说明了整个条件分布.
BACKGROUND: In studies of occupational health, longitudinal environmental exposure, and biomonitoring data are often subject to right skewing and left censoring, in which measurements fall below the limit of detection (LOD). To address right-skewed data, it is common practice to log-transform the data and model the geometric mean, assuming a log-normal distribution. However, if the transformed data do not follow a known distribution, modeling the mean of exposure may result in bias and reduce efficiency. In addition, when examining longitudinal data, it is possible that certain covariates may vary over time.
OBJECTIVE: To develop predictive quantile regression models to resolve the issues of left censoring and time-dependent covariates and to quantitatively evaluate if previous and current covariates can predict current and/or future exposure levels.
METHODS: To address these gaps, we suggested incorporating different substitution approaches into quantile regression and utilizing a method for selecting a working type of time dependency for covariates.
RESULTS: In a simulation study, we demonstrated that, under different types of time-dependent covariates, the approach of multiple random value imputation outperformed the other approaches. We also applied our methods to a carbon nanotube and nanofiber exposure study. The dependent variables are the left-censored mass of elemental carbon at both the respirable and inhalable aerosol size fractions. In this study, we identified some potential time-dependent covariates with respect to worker-level determinants and job tasks.
CONCLUSIONS: Time dependency for covariates is rarely accounted for when analyzing longitudinal environmental exposure and biomonitoring data with values less than the LOD through predictive modeling. Mistreating the time-dependency as time-independency will lead to an efficiency loss of regression parameter estimation. Therefore, we addressed time-varying covariates in longitudinal exposure and biomonitoring data with left-censored measurements and illustrated an entire conditional distribution through different quantiles.