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Introduction (v1.10.0)

This is a Shiny app for interlaboratory microbiological method validation studies. Please visit the deployed app to see it in action.

General information

This app implements the random intercept complementary log-log model suggested by Jarvis et al. (2019) to estimate probability of detection (POD) and level of detection (LOD) from a multi-laboratory validation study for a qualitative (binary) microbiological assay. This app also calculates the intra-laboratory correlation coefficient (ICC) to estimate the proportion of total variance attributable to between-laboratory variance.

This app is intended to be an alternative to the tool discussed in Jarvis et al. (2019).

Citation

To cite this app in a publication, please use:

  • Wang SS, Ihrie J (2021). On the estimation of POD and LOD of qualitative microbiological assays from a multi-laboratory validation study. Journal of AOAC International, https://doi.org/10.1093/jaoacint/qsab130

References

  • Jarvis B, Wilrich C, Wilrich P-T. Estimation of the POD Function and the LOD of a Binary Microbiological Measurement Method from an Interlaboratory Experiment. Journal of AOAC International. 2019; 102(5):1617-1623.

Software needed

R version 4.2.3 or higher is needed. The following R packages (available on CRAN) are also needed:

  • shiny (v1.8.1.1)
  • shinydashboard (v0.7.2)
  • shinyjs (v2.1.0)
  • shinybusy (v0.3.3)
  • shinyvalidate (v0.1.3)
  • Matrix (v1.6-3)
  • lme4 (v1.1-35.3)
  • ggplot2 (v3.5.1)
  • openxlsx (v4.2.5.2)
  • sessioninfo (v1.2.2)
  • dplyr (v1.1.4)
  • tidyr (v1.3.1)

Running the code

To run this app, place all the files into your R working directory. At the R prompt, type: shiny::runApp()

Notes

  • This app runs in Microsoft Edge, Mozilla Firefox, and Google Chrome browsers.