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.

中文翻译：

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确定样本大小以估计细胞转化试验中的中介效应：贝叶斯因果模型

细胞转化分析 (CTA) 是用于初步评估物质致癌潜力的体外方法。CTA 对化妆品、食品和制药公司来说是很有前途的测试，因为它们不仅快速且便宜，而且能够减少基于动物的测试。测定具有随机单向实验的简单结构，其中实验因子由 5 个递增浓度定义。已经提出了不同的分布族来评估物质对 III 型病灶计数的影响，但迄今为止提出的所有模型都没有考虑活细胞数量和培养皿中病灶总数的差异。在本文中，提出了贝叶斯结构因果模型来区分致癌物质在 CTA 实验中的总体、直接和间接影响。推荐的样本量是通过蒙特卡罗模拟计算的，给出了影响的类型和要检测的幅度。利用为 BALB/c 3T3 CTA 引出的参数的信息联合先验分布从每个模拟数据集中获得后验分布。