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Analysis of Genotoxicity Data in a Regulatory Context

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Genetic Toxicology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 817))

Abstract

Analytical methods for regulatory tests usually must be defined before testing. To take this into account and to minimise equivocal interpretations, a sequential strategy is recommended. Assay validity must be verified and results then classed as clearly negative, clearly positive, or uncertain based on historical data. Where there is uncertainty, standard parametric or non-parametric statistical methods should be used with appropriate corrections to assess the significance. The biological importance of statistically significant data should then be evaluated using historical data.

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References

  1. Parry, J., Brooks, T., Mitchell, I., and Wilcox, P. (1984) Genotoxicity studies using yeast cultures. In, UKEMS Sub-Committee on Guidelines for Mutagenicity Testing (Part 2 A) Supplementary tests (Ed. B. J. Dean, UKEMS Pub. Swansea, UK) Ch. 3. pp. 27–61.

    Google Scholar 

  2. Statistical Evaluation of Mutagenicity Test Data. (1989) (Ed. David J. Kirkland, Cambridge University Press, Cambridge, UK).

    Google Scholar 

  3. Mahon, G.A.T., Middleton, B., Robinson, W.D., Green, M.H.L., Mitchell, I. de G., and Tweats, D.J. (1989) Microbial assays from microbial colony assays. In, Statistical Evaluation of Mutagenicity Testn Data. (Ed. David J. Kirkland, Cambridge University Press, Cambridge, UK.) Ch. 2. pp. 26–65.

    Google Scholar 

  4. Abt, K. (1981) Problems of repeated significance testing. Controlled Clinical Trials 1, 377–381.

    Article  PubMed  CAS  Google Scholar 

  5. Hothorn, L. (1994) Multiple comparisons in long-term toxicity studies. Env. Hlth. Pers. 102 (Suppl. 1.), 3–38.

    Google Scholar 

  6. Sokal, R.R. and Rohlf, F.J. (1995) Biometry (3rd edition) (W.H.Freeman and Company, USA).

    Google Scholar 

  7. Bechhofer, R. E. (1988) Percentage points of multivariate Student ‘t’ distributions. In, Selected Tables in Mathematical Statistics Vol. 11. (Ed. R. E. Oden and J. M. Davenport, Am. J. Math. Soc. Pub. Providence, USA) pp. 1–319.

    Google Scholar 

  8. Williams, D.A. (1972) The comparison of several dose levels with a zero dose control. Biometrics 28, 519–531.

    Article  PubMed  CAS  Google Scholar 

  9. Barnett, V., and Lewis, T. (1984) Outliers in Statistical Data (2nd ed.) (John Wiley & Sons Pub. Chichester, UK).

    Google Scholar 

  10. Mitchell, I. de G., Carlton, J. B., and Gilbert, P. J. (1988) The detection and importance of outliers in the in vivo micronucleus assay. Mutagenesis 3, 491–495.

    Article  PubMed  CAS  Google Scholar 

  11. Grubbs, F. (1969) Procedures for Detecting Outlying Observations in Samples. Technometrics 11, 1–21.

    Article  Google Scholar 

  12. Mitchell, I. de G., Rees, R. W., Gilbert, P. J., and Carlton, J. B. (1990). The use of historical data for identifying biologically unimportant but statistically significant results in genotoxicity assays. Mutagenesis 5, 159–164.

    Article  PubMed  CAS  Google Scholar 

  13. Conover, W. J., and Iman, R. L. (1981) Rank transformations as a bridge between parametric and non-parametric statistics. American Statistician 35, 124–133.

    Google Scholar 

  14. Mitchell, I. de G., Amphlett, N. W., and Rees, R. W. (1994) Parametric analysis of rank transformed data for statistical assessment of genotoxicity data with examples from cultured mammalian cells. Mutagenesis 9, 125–132.

    Article  PubMed  CAS  Google Scholar 

  15. Campbell, R.C. (1989) Statistics for Biologists (3rd edition) (Cambridge University Press, Cambridge, UK).

    Google Scholar 

  16. Hood, G. M. PopTools – Software for the analysis of ecological models. Version 3.0.6, CSIRO, 2000.

    Google Scholar 

  17. Manly, B.F.J. (2007) Randomization, bootstrap and Monte Carlo methods in biology (3rd edition) Chapman and Hall. Boca Raton.

    Google Scholar 

  18. Pinheiro, J.C. and Bates, D.M. (2004) Mixed-effects models in S and S-plus. Springer Science, New York.

    Google Scholar 

  19. Verhoeven, K.J.F., Simonsen, K.L., McIntyre, L.M. (2005) Implementing false discovery rate control: increasing your power. OIKOS 108, 643–647.

    Article  Google Scholar 

  20. Burnham, K.P., Anderson, D.R. (2001) Kullback-Leibler information as a basis for strong inference in ecological studies. Wildlife Research 28, 111–119

    Article  Google Scholar 

  21. Luria, S. E., and Delbrück, M. (1943) Mutations of bacteria from virus sensitivity to virus resistance. Genetics 28, 491–511.

    PubMed  CAS  Google Scholar 

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Correspondence to Ian de G. Mitchell .

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de G. Mitchell, I., Skibinski, D.O.F. (2012). Analysis of Genotoxicity Data in a Regulatory Context. In: Parry, J., Parry, E. (eds) Genetic Toxicology. Methods in Molecular Biology, vol 817. Springer, New York, NY. https://doi.org/10.1007/978-1-61779-421-6_18

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  • DOI: https://doi.org/10.1007/978-1-61779-421-6_18

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-61779-420-9

  • Online ISBN: 978-1-61779-421-6

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