Bayesian Estimation of Causal Effects in Carcinogenicity Tests Based upon CTA

Despite more than 30,000 chemical substances are currently produced or imported in the European Union in volumes of 1 ton or more per year, they remain widely yet to be tested for carcinogenicity. Cell Transformation Assays (CTAs) are cheap and fast in vitro methods developed to screen chemical substances without resorting to animal-based testing. Here we propose two models for potential outcomes to estimate causal effects of different concentrations of a candidate carcinogen on counts of Type III foci growing within Petri dishes. A comparison of our proposals with simpler alternatives suggested in the literature for the BALB/c 3T3 CTA protocol is performed using the LOO information criterion. Here we overcome data manipulations recently proposed in the literature by introducing a flexible class of models based on experts’ belief that do not necessitate of: (i) adding fake observations to actual data; (ii) making cumbersome transformations to original counts; (iii) constraining distributions at low concentrations to have a variance larger than the mean. Open issues are discussed in relation to the current practice adopted to perform multi-laboratory experiments on the same substance.