Comparison of Methods for Analyzing Left-Censored Occupational Exposure Data
Comparison of Methods for Analyzing Left-Censored Occupational Exposure Data
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DOI：
10.1093/annhyg/meu067
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发表时间：
2014-11-01
影响因子：
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通讯作者：
Stewart, Patricia A.
中科院分区：
文献类型：
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作者：
Tran Huynh;Ramachandran, Gurumurthy;Stewart, Patricia A.
美国国家环境健康科学研究所 (NIEHS) 正在进行一项流行病学研究 (GuLF STUDY)，以调查 2010 年 4 月至 12 月参与墨西哥湾深水地平线爆炸后漏油应对和清理工作的工人和志愿者的健康状况。该研究的暴露评估部分涉及分析在此过程中收集的数千个个人监测测量结果。分析实验室报告的这些数据的很大一部分值低于检测限 (LOD)。进行了一项模拟研究，以评估三种已建立的方法，用于分析带有删失观测值的数据，以估计算术平均值 (AM)、几何平均值 (GM)、几何标准差 (GSD) 和暴露分布的第 95 个百分位数 (X-0.95)：最大似然 (ML) 估计、β 替换和 Kaplan-Meier (K-M) 方法。每种方法都受到计算机生成的暴露数据集的挑战，这些数据集取自对数正态分布和混合对数正态分布，样本大小 (N) 范围为 5 至 100，GSD 范围为 2 至 5，审查水平范围为 10% 至 90%，具有单个和多个 LOD。使用相对偏差和相对均方根误差 (rMSE) 作为评估指标，在大多数模拟对数正态和混合对数正态分布条件下，β 替代方法通常表现得与 ML 和 K-M 方法一样好或更好。 ML 方法适用于大样本量 (N >= 30)，对变异性较小 (GSD = 2-3) 的对数正态分布进行高达 80% 的删失。当删失时，K-M 方法通常可以提供 AM 的准确估计。
The National Institute for Environmental Health Sciences (NIEHS) is conducting an epidemiologic study (GuLF STUDY) to investigate the health of the workers and volunteers who participated from April to December of 2010 in the response and cleanup of the oil release after the Deepwater Horizon explosion in the Gulf of Mexico. The exposure assessment component of the study involves analyzing thousands of personal monitoring measurements that were collected during this effort. A substantial portion of these data has values reported by the analytic laboratories to be below the limits of detection (LOD). A simulation study was conducted to evaluate three established methods for analyzing data with censored observations to estimate the arithmetic mean (AM), geometric mean (GM), geometric standard deviation (GSD), and the 95th percentile (X-0.95) of the exposure distribution: the maximum likelihood (ML) estimation, the beta-substitution, and the Kaplan-Meier (K-M) methods. Each method was challenged with computer-generated exposure datasets drawn from lognormal and mixed lognormal distributions with sample sizes (N) varying from 5 to 100, GSDs ranging from 2 to 5, and censoring levels ranging from 10 to 90%, with single and multiple LODs. Using relative bias and relative root mean squared error (rMSE) as the evaluation metrics, the beta-substitution method generally performed as well or better than the ML and K-M methods in most simulated lognormal and mixed lognormal distribution conditions. The ML method was suitable for large sample sizes (N >= 30) up to 80% censoring for lognormal distributions with small variability (GSD = 2-3). The K-M method generally provided accurate estimates of the AM when the censoring was