Application of "robust" methods to the analysis of collaborative trial data using bacterial colony counts

Alan John Hedges; Basil Jarvis

doi:10.1016/j.mimet.2006.02.006

Application of "robust" methods to the analysis of collaborative trial data using bacterial colony counts

J Microbiol Methods. 2006 Sep;66(3):504-11. doi: 10.1016/j.mimet.2006.02.006. Epub 2006 Mar 6.

Authors

Alan John Hedges¹, Basil Jarvis

Affiliation

¹ University of Bristol, Department of Cellular and Molecular Medicine, University Walk, Bristol, Bristol BS8 1TD, United Kingdom. A.Hedges@bris.ac.uk

PMID: 16574261
DOI: 10.1016/j.mimet.2006.02.006

Abstract

We describe techniques for the application of two methods, robust to the presence of "outliers", to the hierarchical analysis of variance of bacterial count data from collaborative trials. The techniques are tested against both artificially-generated data with known distributional parameters and actual trial results containing outliers. The relative merits of the robust methods are discussed in comparison with conventional ANOVA techniques.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Analysis of Variance
Colony Count, Microbial / methods*
Colony Count, Microbial / standards
Computer Simulation
Laboratories / standards
Multicenter Studies as Topic / methods*
Reproducibility of Results