Caudill Samuel P, Turner Wayman E, Patterson Donald G
Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, 4770 Buford Highway NE, Atlanta, GA 30341, United States.
Chemosphere. 2007 Sep;69(3):371-80. doi: 10.1016/j.chemosphere.2007.05.061. Epub 2007 Jul 5.
Biomonitoring for environmental chemicals presents various challenges due to the expense of measuring some compounds and the fact that in some samples the levels of many compounds may be below the limit of detection (LOD) of the measuring instrument. Even though various statistical methods have been developed to address issues associated with data being censored because results were below the LOD, the expense of measuring many compounds in large numbers of subjects remains a challenge. One solution to these challenges is to use pooled samples. There are many problems associated with the use of pooled samples as compared with individual samples, but using pooled samples can sometimes reduce the number of analytical measurements needed. Also, because pooled samples often have larger sample volumes, using pooled samples can result in lower LODs and thereby decrease the likelihood that results will be censored. However, many data sets obtained from environmental measurements have been shown to have a log-normal distribution, so using pooled samples presents a new problem: The measured value for a pooled sample is comparable to an arithmetic average of log-normal results and thus represents a biased estimate of the central tendency of the samples making up the pool. In this paper, we present a method for correcting the bias associated with using data from pooled samples with a log-normal distribution. We use simulation experiments to demonstrate how well the bias-correction method performs. We also present estimates for levels of PCB 153 and p,p'-DDE using data from pooled samples from the 2001 to 2002 National Health and Nutrition Examination Surveys.
对环境化学物质进行生物监测面临着各种挑战，这是由于测量某些化合物的成本较高，以及在一些样本中，许多化合物的含量可能低于测量仪器的检测限（LOD）。尽管已经开发出各种统计方法来解决因结果低于检测限而导致数据被审查的相关问题，但在大量受试者中测量多种化合物的成本仍然是一个挑战。应对这些挑战的一种方法是使用混合样本。与单个样本相比，使用混合样本存在许多问题，但使用混合样本有时可以减少所需的分析测量次数。此外，由于混合样本通常具有更大的样本量，使用混合样本可以降低检测限，从而降低结果被审查的可能性。然而，从环境测量中获得的许多数据集已被证明具有对数正态分布，因此使用混合样本会带来一个新问题：混合样本的测量值相当于对数正态结果的算术平均值，因此代表了构成混合样本的各个样本中心趋势的有偏估计。在本文中，我们提出了一种校正与使用具有对数正态分布的混合样本数据相关的偏差的方法。我们使用模拟实验来证明偏差校正方法的效果。我们还使用2001年至2002年国家健康与营养检查调查的混合样本数据，给出了多氯联苯153和对，对'-滴滴伊含量的估计值。
