Cell Transformation Assays (CTAs) are in-vitro methods used in the preliminary assessment of the carcinogenic potential of substances. CTAs are promising tests for cosmetic, food and pharma companies because they are not only quick-and-cheap, but also able to reduce animal-based testing. An assay has the simple structure of a randomized one-way experiment, where the experimental factor is defined by 5 increasing concentrations. Different families of distributions have been proposed to evaluate the effect of a substance on counts of Type III foci, but all models proposed so far do not consider differences in the number of viable cells and in the total number of foci occurring among Petri dishes. In this paper, a Bayesian structural causal model is proposed to distinguish total, direct and indirect effects of a carcinogen in CTA experiments. The recommended sample size is calculated by Monte Carlo simulation given the type of effect and the magnitude to detect. An informative joint prior distribution on parameters elicited for Balb/c 3T3 CTAs is exploited to obtain the posterior distribution from each simulated dataset.

Sample size determination to estimate mediation effects in cell transformation assays: A Bayesian causal model / Stefanini, Federico Mattia; Magrini, Alessandro. - In: APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY. - ISSN 1524-1904. - ELETTRONICO. - 37:(2021), pp. 973-989. [10.1002/asmb.2641]

Sample size determination to estimate mediation effects in cell transformation assays: A Bayesian causal model

Stefanini, Federico Mattia
;
Magrini, Alessandro
2021

Abstract

Cell Transformation Assays (CTAs) are in-vitro methods used in the preliminary assessment of the carcinogenic potential of substances. CTAs are promising tests for cosmetic, food and pharma companies because they are not only quick-and-cheap, but also able to reduce animal-based testing. An assay has the simple structure of a randomized one-way experiment, where the experimental factor is defined by 5 increasing concentrations. Different families of distributions have been proposed to evaluate the effect of a substance on counts of Type III foci, but all models proposed so far do not consider differences in the number of viable cells and in the total number of foci occurring among Petri dishes. In this paper, a Bayesian structural causal model is proposed to distinguish total, direct and indirect effects of a carcinogen in CTA experiments. The recommended sample size is calculated by Monte Carlo simulation given the type of effect and the magnitude to detect. An informative joint prior distribution on parameters elicited for Balb/c 3T3 CTAs is exploited to obtain the posterior distribution from each simulated dataset.
2021
37
973
989
Goal 3: Good health and well-being for people
Goal 9: Industry, Innovation, and Infrastructure
Stefanini, Federico Mattia; Magrini, Alessandro
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1239405
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