title: Bayesian analysis of quantitative antimicrobial assays.
abstract: Quantitative antimicrobial assays are used to assess the efficacy of chemical germicides. Standard methods for statistical analysis use log reduction (LR), the difference on the log scale between average surviving microbes for control and test carriers, as an efficacy measure. These methods have several deficiencies. The LR parameter is not on the original response scale, which complicates its interpretation. The presence of two different definitions of LR makes the statistical inference even more difficult. Current statistical methods for antimicrobial assay analysis rely on asymptotic normal theory, which might not work well for small samples. In addition, they do not appropriately incorporate censored ('too numerous to be counted') observations in the analysis. To overcome those problems, a new Bayesian approach is introduced here. It has also the advantages of more flexible statistical inference, and incorporated prior information in the model.
authors.affiliation: Health Care Research Center, Procter & Gamble Co, Mason, OH 45040, USA. varbanov.ar@pg.com
authors.forename: Alex R
authors.initials: AR
authors.lastname: Varbanov
authors.forename: Charles H
authors.initials: CH
authors.lastname: Taylor
mesh_terms: D001419:Bacteria / Q000187:drug effects / Q000254:growth & development; D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D004202:Disinfectants / Q000494:pharmacology; D008826:Microbial Sensitivity Tests / Q000379:methods*
chemical_list: D004202:Disinfectants