Background Jarvis et al. in 2019 (J. AOAC Int. 102: 1617–1623) estimated the mean laboratory effect (µ), standard deviation of laboratory effects (σ), probability of detection (POD), and level of detection (LOD) from a multi-laboratory validation study of qualitative microbiological assays using a random intercept complementary log–log model. Their approach estimated σ based on a Laplace approximation to the likelihood function of the model, but estimated µ from a fixed effectmodel due to a limitation in the MS Excel spreadsheet which was used by the authors to develop a calculation tool. Objective We compared the estimates of µ and σ from three approaches (the Laplace approximation that estimates µ and σ simultaneously from the random intercept model, adaptive Gauss–Hermite quadrature (AGHQ), and the method of Jarvis et al.) and introduced an R Shiny app to implement the AGHQ using the widely used “lme4” R package. Methods We conducted a simulation study to compare the accuracy of the estimates of µ and σ from the three approaches and compared the estimates of µ, σ, LOD, etc. between the R Shiny app and the spreadsheet calculation tool developed by Jarvis et al. for an example dataset. Results Our simulation study shows that, while the three approaches produce similar estimates of σ, the AGHQ has generally the best performance for estimating µ (and hence mean POD and LOD). The differences in the estimates between the R Shiny app and the spreadsheet were demonstrated using the example dataset. Conclusion The AGHQ is the best method for estimating µ, POD, and LOD. Highlights The user-friendly R Shiny app provides a better alternative to the spreadsheet.

中文翻译：

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从多实验室验证研究估计定性微生物测定的 POD 和 LOD

背景 Jarvis 等人。2019 年 (J. AOAC Int. 102: 1617–1623) 估计了平均实验室效应 (µ)、实验室效应的标准差 (σ)、检测概率 (POD) 和检测水平 (LOD)使用随机截距互补对数对数模型进行定性微生物测定的实验室验证研究。他们的方法基于模型似然函数的拉普拉斯近似估计 σ，但由于作者用于开发计算工具的 MS Excel 电子表格的限制，从固定效应模型估计 μ。目的 我们比较了三种方法对 μ 和 σ 的估计值（从随机截距模型同时估计 μ 和 σ 的拉普拉斯近似、自适应高斯-厄米特正交 (AGHQ) 和 Jarvis 等人的方法。) 并引入了一个 R Shiny 应用程序来使用广泛使用的“lme4”R 包来实现 AGHQ。方法 我们进行了一项模拟研究，比较了三种方法对 µ 和 σ 估计值的准确性，并比较了 R Shiny 应用程序和 Jarvis 等人开发的电子表格计算工具对 µ、σ、LOD 等的估计值。对于示例数据集。结果 我们的模拟研究表明，虽然这三种方法产生了相似的 σ 估计值，但 AGHQ 通常在估计 μ（以及因此平均 POD 和 LOD）方面具有最佳性能。使用示例数据集演示了 R Shiny 应用程序和电子表格之间的估计差异。结论 AGHQ 是估计 µ、POD 和 LOD 的最佳方法。亮点 用户友好的 R Shiny 应用程序为电子表格提供了更好的替代方案。