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 article, 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 / F.M. Stefanini, A. Magrini. - In: APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY. - ISSN 1524-1904. - 37:5 Special issue(2021 Sep), pp. 973-989. [10.1002/asmb.2641]

Sample size determination to estimate mediation effects in Cell Transformation Assays: a Bayesian causal model

F.M. Stefanini
Primo
;
2021-09

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 article, 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.
Bayesian SCM, causal inference, CTA, in vitro test, structural causal model
Settore SECS-S/01 - Statistica
16-lug-2021
Article (author)
File in questo prodotto:
File Dimensione Formato  
2021_FMS_AM_asmb.2641.pdf

accesso aperto

Descrizione: Online first
Tipologia: Publisher's version/PDF
Dimensione 556.73 kB
Formato Adobe PDF
556.73 kB Adobe PDF Visualizza/Apri
Appl Stoch Models Bus Ind - 2021 - Stefanini - Sample size determination to estimate mediation effects in cell.pdf

accesso aperto

Tipologia: Publisher's version/PDF
Dimensione 579.48 kB
Formato Adobe PDF
579.48 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/863136
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 1
social impact