Use of statistical modeling to reassess the performance standard for the AOAC use-dilution methods (955.15 and 964.02). 2014

Stephen F Tomasino, and Albert E Parker, and Martin A Hamilton

The AOAC Use-dilution methods (UDM) 955.15 (Staphylococcus aureus) and 964.02 (Pseudomonas aeruginosa) are laboratory methods used to substantiate antimicrobial efficacy claims for liquid disinfectants on inanimate surfaces. The UDM is accepted by the U.S. Environmental Protection Agency for the purpose of product registration. To attain a hospital-level claim, testing against S. aureus and P. aeruginosa is required, and the product must pass against both microbes. Currently, the UDM's performance standard for a single 60-carrier test is the same for both microbes, and allows up to one positive carrier for the product to be considered as a pass. In this paper, the performance standards for these methods are reassessed using data from a 2009 five-laboratory collaborative study and a recently published statistical model. The reassessment focuses on the pass-error rate for ineffective products and the fail-error rate for highly effective products. The calculations indicate that the pass-error rate is between 9 and 24% and the fail-error rate between 18 and 23% when the current performance standard is used for a single test. For product registration, a smaller pass-error rate (1%) historically has been maintained by requiring that a disinfectant pass three UDM tests for each of the two microbes; however, the calculations also indicate that the fail-error rate is between 42 and 45%. This large fail-error rate is a compelling reason to consider a new performance standard for the two UDM methods, 955.15 (S. aureus) and 964.02 (P. aeruginosa). One alternative performance standard allows no more than six positive carriers out of 60 as a pass when using P. aeruginosa and no more than three positive carriers out of 60 when using S. aureus. In addition, the new performance standard requires that three UDM tests be performed with each of the two microbes, and the disinfectant must pass all six tests to be considered efficacious. The statistical calculations for this alternative performance standard indicate that the pass-error rate is no more than 3%, while the fail-error rate is as small as 5%. Based on these error rate calculations, proposed revisions to the performance standards for AOAC Methods 955.15 and 964.02 are provided.

UI MeSH Term Description Entries
D007201 Indicator Dilution Techniques Methods for assessing flow through a system by injection of a known quantity of an indicator, such as a dye, radionuclide, or chilled liquid, into the system and monitoring its concentration over time at a specific point in the system. (From Dorland, 28th ed) Dilution Techniques,Dilution Technics,Indicator Dilution Technics,Dilution Technic,Dilution Technic, Indicator,Dilution Technics, Indicator,Dilution Technique,Dilution Technique, Indicator,Dilution Techniques, Indicator,Indicator Dilution Technic,Indicator Dilution Technique,Technic, Dilution,Technic, Indicator Dilution,Technics, Dilution,Technics, Indicator Dilution,Technique, Dilution,Technique, Indicator Dilution,Techniques, Dilution,Techniques, Indicator Dilution
D012015 Reference Standards A basis of value established for the measure of quantity, weight, extent or quality, e.g. weight standards, standard solutions, methods, techniques, and procedures used in diagnosis and therapy. Standard Preparations,Standards, Reference,Preparations, Standard,Standardization,Standards,Preparation, Standard,Reference Standard,Standard Preparation,Standard, Reference
D003627 Data Interpretation, Statistical Application of statistical procedures to analyze specific observed or assumed facts from a particular study. Data Analysis, Statistical,Data Interpretations, Statistical,Interpretation, Statistical Data,Statistical Data Analysis,Statistical Data Interpretation,Analyses, Statistical Data,Analysis, Statistical Data,Data Analyses, Statistical,Interpretations, Statistical Data,Statistical Data Analyses,Statistical Data Interpretations
D015203 Reproducibility of Results The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results. Reliability and Validity,Reliability of Result,Reproducibility Of Result,Reproducibility of Finding,Validity of Result,Validity of Results,Face Validity,Reliability (Epidemiology),Reliability of Results,Reproducibility of Findings,Test-Retest Reliability,Validity (Epidemiology),Finding Reproducibilities,Finding Reproducibility,Of Result, Reproducibility,Of Results, Reproducibility,Reliabilities, Test-Retest,Reliability, Test-Retest,Result Reliabilities,Result Reliability,Result Validities,Result Validity,Result, Reproducibility Of,Results, Reproducibility Of,Test Retest Reliability,Validity and Reliability,Validity, Face
D015233 Models, Statistical Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc. Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model

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