Using measurement error models to assess effects of prenatal and postnatal methylmercury exposure in the Seychelles Child Development Study
Using measurement error models to assess effects of prenatal and postnatal methylmercury exposure in the Seychelles Child Development Study
复制标题
DOI：
10.1016/s0013-9351(03)00089-6
复制
发表时间：
2003-10-01
影响因子：
8.3
通讯作者：
Clarkson, TW
中科院分区：
文献类型：
--
作者：
Huang, LS;Cox, C;Clarkson, TW
研究环境接触对人类健康的影响通常需要估计接触和结果。用于评估暴露与结果之间的关联的标准方法包括多元线性回归分析，该分析假设结果变量的观察存在误差，而暴露水平和其他解释变量的测量完全准确，因此测量值与实际值没有偏差。本讨论中的术语测量误差是指实际或真实水平与实际观察到的值之间的差异。在调查产前食用鱼类接触甲基汞对儿童发育的影响时，获得真实接触水平（产生毒性效应）的唯一方法是确定胎儿大脑中的浓度，但这是不可能的。与环境接触研究中的常见情况一样，测量的接触水平是一种生物标志物，例如怀孕期间母体毛发的平均水平。头发汞含量的测量被广泛用作甲基汞暴露的生物指标，也是唯一根据目标组织-发育中的大脑-进行校准的指标。在多元回归分析中，解释变量的测量值和真实值之间的变异性会产生偏差，导致回归参数（斜率）的高估或低估。幸运的是，被称为测量误差模型（MEM）的统计方法可用于解释多元回归分析中解释变量的测量误差，并且这些方法可以提供未知结果/暴露关系的（无偏的或偏差校正的）估计。在本文中，我们说明MEM分析重新分析的数据，从5.5年的测试电池在塞舌尔儿童发展研究，一项纵向研究，产前暴露于甲基汞从产妇消费的饮食高的鱼。由于存在关于本研究中使用的产前暴露生物标志物（母体毛发水平）的测量误差偏差变异性大小的独立校准数据，因此可以使用MEM方法。我们的重新分析表明，调整解释变量的测量误差对原始结果没有明显的影响。(C)2003年爱思唯尔公司All rights reserved.
Studies of the effects of environmental exposures on human health typically require estimation of both exposure and outcome. Standard methods for the assessment of the association between exposure and outcome include multiple linear regression analysis, which assumes that the outcome variable is observed with error, while the levels of exposure and other explanatory variables are measured with complete accuracy, so that there is no deviation of the measured from the actual value. The term measurement error in this discussion refers to the difference between the actual or true level and the value that is actually observed. In the investigations of the effects of prenatal methylmercury (MeHg) exposure from fish consumption on child development, the only way to obtain a true exposure level (producing the toxic effect) is to ascertain the concentration in fetal brain, which is not possible. As is often the case in studies of environmental exposures, the measured exposure level is a biomarker, such as the average maternal hair level during gestation. Measurement of hair mercury is widely used as a biological indicator for exposure to MeHg and is the only indicator that has been calibrated against the target tissue, the developing brain. Variability between the measured and the true values in explanatory variables in a multiple regression analysis can produce bias, leading to either over or underestimation of regression parameters (slopes). Fortunately, statistical methods known as measurement error models (MEM) are available to account for measurement errors in explanatory variables in multiple regression analysis, and these methods can provide an (either "unbiased" or "bias-corrected") estimate of the unknown outcome/exposure relationship. In this paper, we illustrate MEM analysis by reanalyzing data from the 5.5-year test battery in the Seychelles Child Development Study, a longitudinal study of prenatal exposure to MeHg from maternal consumption of a diet high in fish. The use of the MEM approach was made possible by the existence of independent, calibration data on the magnitude of the variability of the measurement error deviations for the biomarker of prenatal exposure used in this study, the maternal hair level. Our reanalysis indicated that adjustment for measurement errors in explanatory variables had no appreciable effect on the original results. (C) 2003 Elsevier Inc. All rights reserved.