Handling of dioxin measurement data in the presence of non-detectable values: Overview of available methods and their application in the Seveso chloracne study
Handling of dioxin measurement data in the presence of non-detectable values: Overview of available methods and their application in the Seveso chloracne study
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DOI：
10.1016/j.chemosphere.2005.01.055
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发表时间：
2005-08-01
期刊：
影响因子：
8.8
通讯作者：
Landi, MT
中科院分区：
文献类型：
--
作者：
Baccarelli, A;Pfeiffer, R;Landi, MT
在环境研究中，无法检测到或接近检测极限（DL）的暴露浓度测量很常见。对未检测到的数据进行适当的统计处理对于避免偏差和不必要的信息损失至关重要。在目前的工作中，我们概述了处理不可检测值的可能统计策略，包括删除，简单替换，分布方法和基于分布的imputation。简单的替代方法（例如，用0、DL/2、DL/root 2或DL代替未检测到的数据）是最常用的方法，尽管EPA数据质量评估指南不鼓励在未检测到的数据占比达到15%时使用这些方法。基于分布的多重归算方法，也称为稳健或“填充”程序，即使在50-70%的观测值未被检测到并且可以使用常用的统计软件执行的情况下，也可能产生可靠的结果。可以对输入的数据集进行任何统计分析。结果适当地反映了不可检测值的存在，并产生有效的统计推断。我们描述了在最近对Seveso人群暴露于2,3,7,8-四氯二苯并-对二恶英（TCDD）的受试者进行的调查中使用基于分布的多重代入，其中55.6%的血浆TCDD测量未检测到。我们建议，当大量观测数据未被检测到时，基于分布的多重imputation是分析环境数据的首选方法。(c) 2005 Elsevier Ltd版权所有。
Exposure measurements of concentrations that are non-detectable or near the detection limit (DL) are common in environmental research. Proper statistical treatment of non-detects is critical to avoid bias and unnecessary loss of information. In the present work, we present an overview of possible statistical strategies for handling non-detectable values, including deletion, simple substitution, distributional methods, and distribution-based imputation. Simple substitution methods (e.g., substituting 0, DL/2, DL/root 2, or DL for the non-detects) are the most commonly applied, even though the EPA Guidance for Data Quality Assessment discouraged their use when the percentage of non-detects is > 15%. Distribution-based multiple imputation methods, also known as robust or "fill-in" procedures, may produce dependable results even when 50-70% of the observations are non-detects and can be performed using commonly available statistical software. Any statistical analysis can be conducted on the imputed datasets. Results properly reflect the presence of non-detectable values and produce valid statistical inference. We describe the use of distribution-based multiple imputation in a recent investigation conducted on subjects from the Seveso population exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), in which 55.6% of plasma TCDD measurements were non-detects. We suggest that distribution-based multiple imputation be the preferred method to analyze environmental data when substantial proportions of observations are non-detects. (c) 2005 Elsevier Ltd. All rights reserved.