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Counting Test and Parametric Two One-Sided Tolerance Interval Test for Content Uniformity Using Large Sample Sizes

  • Meiyu ShenEmail author
  • Yi Tsong
  • Richard Lostritto
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 218)

Abstract

The purpose of uniformity of dosage unit test is to determine the degree of uniformity in the amount of drug substance among dosage units in a batch. Recently, there are several nonparametric methods including the large sample counting approach proposed in European Pharmacopeia 8.1 (EU Option 2). All nonparametric methods specify a maximum number of tablets, of which the contents fall outside the interval (85%, 115%) of labeling claim (LC) for a given large sample size. The nonparametric method in European Pharmacopeia requires another maximum number of tablets, of which the contents fall outside the interval (75%, 125%) LC. We denote the nonparametric method as the counting test which will be used in the rest of the article. We focus on the comparison of the acceptance probabilities between EU Option 2 and the parametric two one-sided tolerance intervals (PTIT_matchUSP90) test. Obviously, a counting test is less efficient than a parametric test in general. Our simulation study clearly shows that the EU Option 2 is not sensitive to batches with a large variability in contents which follow a normal distribution with an off-target mean, a mixture of two normal distributions, or a mixture of a uniform distribution with small percent of extreme values. The EU Option 2 is not sensitive to the mean shift of the majority population (97%) from 100% LC to 90% LC. In addition, the EU Option 2 is not sensitive to low assay values (about 90% LC). The EU Option 2 is over-sensitive to one extreme case: 97% tablets with 100% LC and 3% tablets with 76% LC.

Keywords

Uniformity of dosage units Content uniformity Large sample sizes Tolerance interval Counting test 

Notes

Acknowledgements

The authors would like to thank two anonymous reviewers for their comments.

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Copyright information

© This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2019

Authors and Affiliations

  1. 1.Center for Drug Evaluation and Research, Food and Drug AdministrationSilver SpringUSA
  2. 2.Division Biometrics VI, Office of Biostatistics, Office of Translational SciencesCenter for Drug Evaluation and Research, Food and Drug AdministrationSilver SpringUSA

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