Bayesian probability

Internal medicine

Inference

Disease

Approximate Bayesian computation

Mathematics

Statistics

Analysis of covariance

Machine learning

Body weight

Artificial intelligence

Covariance

Programming language

Organ system

Computer science

Interpretation (philosophy)

Chemical toxicity

Toxicity

Medicine

Scientific Reports

Regulatory authorities require animal toxicity tests for new chemical entities. Organ weight changes are accepted as a sensitive indicator of chemically induced organ damage, but can be difficult to interpret because changes in organ weight might reflect chemically-induced changes in overall body weight. A common solution is to calculate the relative organ weight (organ to body weight ratio), but this inadequately controls for the dependence on body weight – a point made by statisticians for decades, but which has not been widely adopted. The recommended solution is an analysis of covariance (ANCOVA), but it is rarely used, possibly because both the method of statistical correction and the interpretation of the output may be unclear to those with minimal statistical training. Using relative organ weights can easily lead to incorrect conclusions, resulting in poor decisions, wasted resources, and an ethically questionable use of animals. We propose to cast the problem into a causal modelling framework as it directly assesses questions of scientific interest, the results are easy to interpret, and the analysis is simple to perform with freely available software. Furthermore, by taking a Bayesian approach we can model unequal variances, control for multiple testing, and directly provide evidence of safety.

Determining organ weight toxicity with Bayesian causal models: Improving on the analysis of relative organ weights