The Salmonella/microsome mutagenicity index revisited

statistical performance evaluation and modified measures

Authors

  • Davi Butturi-Gomes Departamento de Matemática e Estatística, Campus Santo Antônio, Universidade Federal de São João Del-Rei – DEMAT/UFSJ. Praça Frei Orlando 170, CEP 36307-352, São João Del-Rei, Minas Gerais, Brazil, Phone + 55 32 3379 2356 https://orcid.org/0000-0002-8398-533X
  • Silvio Sandoval Zocchi PPG em Estatística e Experimentação Agronômica, Departamento de Ciências Exatas, Escola Superior de Agricultura “Luiz de Queiroz”, Universidade de São Paulo – ESALQ/USP. Av Padua Dias 11, CEP 13418 900, Phone +55 19 3526 4200, Fax +55 19 3526 4201 https://orcid.org/0000-0003-2535-908X

Keywords:

Ames test, Bias, Bootstrap, Confidence intervals, Overdispersion

Abstract

The Ames test is a widely used, short-term mutagenicity assay for environmental and human health risk assessment and its results are usually expressed by means of a simplistic measure, namely the mutagenicity index (MI). In this study, we aimed at evaluating its statistical properties, its advantages and disadvantages (point and interval estimation performances) in comparison to three new, similar measures (MIm, \tilde{MI} and \tilde{MI}_{m}). We used computer simulations of realistic, protocol-recommended scenarios, and illustrated our methods with an observed, real dataset. Our major findings are the following: (i) all four measures are generally unbiased; (ii) both bias and variance raises with overdispersion; and (iii) only the MIm is guaranteed to meet the nominal confidence level in interval estimation. Along with other recommendations, we present a discussion on the implications of the misuse of any these indices.

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Published

20-04-2021

How to Cite

Butturi-Gomes, D., & Zocchi, S. S. (2021). The Salmonella/microsome mutagenicity index revisited: statistical performance evaluation and modified measures. Sigmae, 7(2), 13–23. Retrieved from https://publicacoes.unifal-mg.edu.br/revistas/index.php/sigmae/article/view/721

Issue

Vol. 7 No. 2 (2018): "Three is a magic number"

Section

Applied Statistics

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