Hwang Myungsil, Lee Seung Chan, Park Jae-Hong, Choi Jihee, Lee Hae-Jeung
Department of Food and Nutrition, Institute for Aging and Clinical Nutrition Research Gachon University Seongnam-si Korea.
Food Safety Risk Assessment Division National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety Cheongju City Korea.
Food Sci Nutr. 2023 Jul 6;11(9):5223-5235. doi: 10.1002/fsn3.3481. eCollection 2023 Sep.
Chemical risk assessment is important for risk management, and estimates of chemical exposure must be as accurate as possible. Chemical concentrations in food below the limit of detection are known as nondetects and result in left-censored data. During statistical analysis, the method used for handling values below the limit of detection is important. Many risk assessors employ widely used substitution methods to treat left-censored data, as recommended by international organizations. The National Institute of Food and Drug Safety Evaluation of South Korea also recommends these methods, which are currently used for chemical exposure assessments. However, these methods have statistical limitations, and international organizations recommend more advanced alternative statistical approaches. In this study, we assessed the validity of currently used statistical methods for handling nondetects. To identify the most suitable statistical method for handling nondetection, we created virtual data and conducted simulation studies. Based on both simulation and case studies, the Maximum Likelihood Estimation (MLE) and Robust Regression on Order Statistics (ROS) methods were found to be the best options. The statistical values obtained from these methods were similar to those obtained from the commonly used 1/2 Limit of Detection (LOD) substitution method for nondetection treatment. In three case studies, we compared the various methods based on the root mean squared error. The data for all case studies were from the same source, to avoid heterogeneity. Across various sample sizes and nondetection rates, the mean and 95th percentile values for all treatment methods were similar. However, "lognormal maximum likelihood estimation" method was not suitable for estimating the mean. Risk assessors should consider statistical processing of monitoring data to reduce uncertainty. Currently used substitution methods are effective and easy to apply to large datasets with nondetection rates <80%. However, advanced statistical methods are required in some circumstances, and national guidelines are needed regarding their use in risk assessments.
化学风险评估对风险管理很重要，化学暴露估计必须尽可能准确。食品中低于检测限的化学浓度被称为未检出值，会产生左删失数据。在统计分析中，处理低于检测限的值所使用的方法很重要。许多风险评估人员采用国际组织推荐的广泛使用的替代方法来处理左删失数据。韩国食品药品安全评价院也推荐这些方法，目前这些方法用于化学暴露评估。然而，这些方法存在统计局限性，国际组织推荐更先进的替代统计方法。在本研究中，我们评估了当前用于处理未检出值的统计方法的有效性。为了确定处理未检出情况最合适的统计方法，我们创建了虚拟数据并进行了模拟研究。基于模拟和案例研究，发现最大似然估计（MLE）和顺序统计稳健回归（ROS）方法是最佳选择。从这些方法获得的统计值与从不检出处理常用的1/2检测限（LOD）替代方法获得的统计值相似。在三个案例研究中，我们基于均方根误差比较了各种方法。所有案例研究的数据都来自同一来源，以避免异质性。在各种样本量和未检出率下，所有处理方法的均值和第95百分位数都相似。然而，“对数正态最大似然估计”方法不适用于估计均值。风险评估人员应考虑对监测数据进行统计处理以降低不确定性。目前使用的替代方法有效且易于应用于未检出率<80%的大型数据集。然而，在某些情况下需要先进的统计方法，并且在风险评估中使用这些方法需要国家指南。
