Risk Assessment Division, Office of Public Health Science, Food Safety Inspection Service, USDA, Fort Collins, CO 80526, USA.
J Appl Microbiol. 2013 Jan;114(1):152-60. doi: 10.1111/jam.12019. Epub 2012 Oct 12.
The fitting of statistical distributions to microbial sampling data is a common application in quantitative microbiology and risk assessment applications. An underlying assumption of most fitting techniques is that data are collected with simple random sampling, which is often times not the case. This study develops a weighted maximum likelihood estimation framework that is appropriate for microbiological samples that are collected with unequal probabilities of selection.
A weighted maximum likelihood estimation framework is proposed for microbiological samples that are collected with unequal probabilities of selection. Two examples, based on the collection of food samples during processing, are provided to demonstrate the method and highlight the magnitude of biases in the maximum likelihood estimator when data are inappropriately treated as a simple random sample.
Failure to properly weight samples to account for how data are collected can introduce substantial biases into inferences drawn from the data.
The proposed methodology will reduce or eliminate an important source of bias in inferences drawn from the analysis of microbial data. This will also make comparisons between studies and the combination of results from different studies more reliable, which is important for risk assessment applications.
将统计分布拟合到微生物采样数据是定量微生物学和风险评估应用中的常见应用。大多数拟合技术的一个基本假设是数据是通过简单随机抽样收集的，但这种情况并不常见。本研究开发了一种加权最大似然估计框架，适用于采用不等概率选择收集的微生物样本。
提出了一种用于采用不等概率选择收集的微生物样本的加权最大似然估计框架。提供了两个基于食品加工过程中采样的示例，以演示该方法并强调了当数据被不当处理为简单随机样本时，最大似然估计量的偏差幅度。
未能正确加权样本以说明数据的收集方式，可能会导致从数据推断中引入重大偏差。
拟议的方法将减少或消除从微生物数据分析中得出的推断中一个重要的偏差来源。这也将使不同研究之间的比较和不同研究结果的组合更可靠，这对于风险评估应用非常重要。
