A comparison of test statistics for the recovery of rapid growth-based enumeration tests

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This paper considers five test statistics for comparing the recovery of a rapid growth-based enumeration test with respect to the compendial microbiological method using a specific nonserial dilution experiment. The finite sample distributions of these test statistics are unknown, because they are functions of correlated count data. A simulation study is conducted to investigate the type I and type II error rates. For a balanced experimental design, the likelihood ratio test and the main effects analysis of variance (ANOVA) test for microbiological methods demonstrated nominal values for the type I error rate and provided the highest power compared with a test on weighted averages and two other ANOVA tests. The likelihood ratio test is preferred because it can also be used for unbalanced designs. It is demonstrated that an increase in power can only be achieved by an increase in the spiked number of organisms used in the experiment. The power is surprisingly not affected by the number of dilutions or the number of test samples. A real case study is provided to illustrate the theory. Keywords: accuracy of microbiological methods; correlated count data; detection of organisms; limit of quantification; multinomial distribution; validation of microbiological methods
Original languageEnglish
Pages (from-to)291-299
Number of pages9
JournalPharmaceutical Statistics
Issue number5
Publication statusPublished - 2013


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