The planning and conduct of animal experiments in the European Union is subject to strict legal conditions. Still, many preclinical animal experiments are only poorly designed. As a consequence, discoveries that are made in one animal experiment, cannot be reproduced in another animal experiment or discoveries in translational animal research fail to be translated to humans. When designing new experiments in a classical frequentist framework, the sample size for the new experiment is chosen with the goal to achieve at least a certain statistical power, given a statistical test for a null hypothesis, a significance threshold and a minimally relevant effect size. In a Bayesian framework, inference is made by a combination of both the information from newly observed data and also by a prior distribution, that represents a priori information on the parameters. In translational animal experiments, a priori information is present in previously conducted experiments to the same outcome in similar animals. The prior information can be incorporated in a systematic way in the design and analysis of a new animal experiment by summarizing the historical data in a (Bayesian) meta-analysis model and using the meta-analysis model to make predictions for the data in the new experiment. This is called meta-analytic predictive (MAP) approach. In this work, concepts of how to design translational animal experiments by MAP approaches are introduced and compared to classical frequentist power-oriented sample size planning. Current chances and challenges, that exist in the practical application of these approaches in translational animal research, are discussed. Special emphasis is put on the construction of prior distributions and sample size calculation by design analysis. The considerations are motivated by a real world translational research example.
翻译:欧洲联盟动物实验的规划和实施须遵守严格的法律条件。不过,许多临床前动物实验的设计不善。因此,在一项动物实验中发现的发现不能在另一项动物实验中复制,或者在翻译动物研究中发现的发现不能翻译给人类。在古典常客主义框架内设计新的实验时,选择新实验的样本规模的目的是至少实现一定的统计能力,考虑到对无效假设、重要临界值和最小相关影响大小的统计测试。在贝叶西亚框架中,通过将新观测的数据和先前分发的数据综合起来,得出推断,这是关于参数的先验性信息。在翻译中,先验性信息出现在以前对类似动物的相同结果进行的实验中。以前的信息可以系统地纳入新动物实验的设计和分析中,方法是在(贝耶斯)元分析模型中总结历史数据,用元分析模型分析模型来对新实验中的数据进行预测,在新实验中,先期设计设计规模的先期性研究分析方法称为对模型的预测。在目前研究中,先期的模型分析方法是先期性分析方法,先期的预测,然后是先期性分析方法,先期性分析,然后是先期性分析,然后是先期分析方法,然后将分析,然后将分析,然后再分析,然后将分析,然后将分析,然后再分析,再分析,再分析,再分析,再分析,然后将分析,然后将分析,然后将分析,再分析方法,再分析,再分析,再分析,然后将分析,再将分析,然后将分析方法,然后将分析,再进行为后分析,然后分析,然后分析,然后分析,再分析,然后分析,然后分析,然后分析,然后分析,再分析,然后分析,然后分析,然后分析,再分析,再分析,再分析,再分析,再分析,然后分析,再分析,再分析,再分析,再分析,再分析,再分析,再分析,再分析,再分析,再分析,再分析,再分析,再分析,再分析,再分析,再分析,再分析,再分析方法,再分析方法,再分析,再分析,再分析,再分析,再分析,再分析,再分析,再分析方法,再分析方法,再分析方法,再分析,再分析,再分析,再分析,再分析,再分析,再