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The planning and conduct of animal experiments in the European Union issubject to strict legal conditions. Still, many preclinical animal experimentsare only poorly designed. As a consequence, discoveries that are made in oneanimal experiment, cannot be reproduced in another animal experiment ordiscoveries in translational animal research fail to be translated to humans.When designing new experiments in a classical frequentist framework, the samplesize for the new experiment is chosen with the goal to achieve at least acertain statistical power, given a statistical test for a null hypothesis, asignificance threshold and a minimally relevant effect size. In a Bayesianframework, inference is made by a combination of both the information fromnewly observed data and also by a prior distribution, that represents a prioriinformation on the parameters. In translational animal experiments, a prioriinformation is present in previously conducted experiments to the same outcomein similar animals. The prior information can be incorporated in a systematicway in the design and analysis of a new animal experiment by summarizing thehistorical data in a (Bayesian) meta-analysis model and using the meta-analysismodel to make predictions for the data in the new experiment. This is calledmeta-analytic predictive (MAP) approach. In this work, concepts of how todesign translational animal experiments by MAP approaches are introduced andcompared to classical frequentist power-oriented sample size planning. Currentchances and challenges, that exist in the practical application of theseapproaches in translational animal research, are discussed. Special emphasis isput on the construction of prior distributions and sample size calculation bydesign analysis. The considerations are motivated by a real world translationalresearch example.

Designing translational animal experiments by Bayesian MAP approaches