Incorporating model uncertainties along with data uncertainties in microbial risk assessment
Incorporating model uncertainties along with data uncertainties in microbial risk assessment
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DOI：
10.1006/rtph.2000.1404
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发表时间：
2000-08-01
影响因子：
3.4
通讯作者：
Chen, JJ
中科院分区：
文献类型：
--
作者：
Kang, SH;Kodell, RL;Chen, JJ
自1997年国家食品安全倡议以来，人们对食品安全进行了大量研究。风险评估在食品安全实践和计划中扮演着重要的角色，各种用于评估微生物风险的剂量-反应模型已经被研究。有几个剂量-反应模型可以很好地拟合实验剂量范围内的数据，但在低剂量范围内，产量风险估计不同数量级。因此，在风险评估中，模型不确定性与数据不确定性(实验变量)一样重要。尽管在风险评估中，考虑数据不确定性是很常见的，但考虑模型不确定性却很少见。在这篇文章中，我们将数据不确定性与置信度和模型不确定性结合在一起，并对每个模型的估计值进行加权平均。给出了一个计算极大似然估计和置信限的数值工具。用实际数据集说明了所提出的包含模型不确定性的方法。(C)2000年学术出版社。
Much research on food safety has been conducted since the National Food Safety Initiative of 1997. Risk assessment plays an important role in food safety practices and programs, and various dose-response models for estimating microbial risks have been investigated. Several dose-response models can provide reasonably good fits to the data in the experimental dose range, but yield risk estimates that differ by orders of magnitude in the low-dose range. Hence, model uncertainty can be just important as data uncertainty (experimental variation) in risk assessment. Although it is common in risk assessment to account for data uncertainty, it is uncommon to account for model uncertainties. In this paper we incorporate data uncertainties with confidence limits and model uncertainties with a weighted average of an estimate from each of various models. A numerical tool to compute the maximum likelihood estimates and confidence limits is addressed. The proposed method for incorporating model uncertainties is illustrated with real data sets. (C) 2000 Academic Press.