Issues in the Experimental Design and Statistical Analysis of in Vitro Mutagenicity Tests

• Published in: Therapeutic innovation & regulatory science Vol. 31; no. 2; pp. 345 - 356
• Main Author: Lovell, David P.
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 1997; Springer Nature B.V
• Subjects: Analytical methods; Biocompatibility; Biomedical materials; Control data (computers); Design analysis; Design of experiments; Experimental design; In vitro methods and tests; In vivo methods and tests; Lymphocytes; Mutagenicity; Salmonella; Statistical analysis; Statistics; Toxicology; In vitro testing; Mutagenesis; Statistics; Genotoxicity; Guidelines
• ISSN: 2168-4790; 2168-4804
• DOI: 10.1177/009286159703100205

This paper discusses issues related to the statistical analysis of in vitro tests for mutagenicity. The range of methods available from bacterial tests such as the Ames/Salmonella assay to the use of human lymphocyte culture assays for in vitro mutagenicity tests are outlined. Aspects of experimental designs associated with the different in vitro tests are compared and contrasted. The relative power of the different in vitro tests is discussed and contrasted with the power associated with the comparable in vivo tests. The use of techniques such as randomization, replication, and blocking to minimize the introduction of biases is stressed. The need for independent repeats of the experiment is emphasized. The purpose of statistical analysis of the data and the relative importance of hypothesis testing and estimation is discussed in the context of the choice of analytical methods, the power of the designs, and the Type 1 and 2 errors associated with the tests. The potential to incorporate historical control data into the assessments is considered. Actual data are used to illustrate some of the problems encountered in the analysis of in vitro mutagenicity data. It is argued that statistical analysis is only one aspect of the assessment of in vitro mutagenicity tests. Guidelines for the choice of experimental design and statistical analysis should not be overprescriptive but rather should be flexible enough to allow the full power of the two disciplines of statistics and genetic toxicology to be utilized.