In pre- and non-clinical toxicology, the reduction of animal use is highly desireable. Although approaches for possible sample size reduction in the concurrent control group were suggested previously under the virtual control groups framework for continuous endpoints, methodology that is applicable to binary outcomes that occur in long-term carcinogenicity studies is currently missing. In order to augment animals in the current control group with historical control data, we propose approaches that rely on dynamic Bayesian borrowing and simultaneous credible intervals for risk ratios. Several operation characteristics such as familywise error rate (FWER) and power are assessed via Monte-Carlo simulations and compared to the ones of approaches that rely on pooling of historical and current observations. It turned out that under optimal conditions, Bayesian approaches based on robustified prior distributions enable a substantial reduction of the control groups sample size, while still controlling the FWER up to a satisfactory level. Furthermore, at least to some extend, these approaches were able to protect against possible drift. This hightlights the potential of Bayesian study designs to reduce animal use in toxicology through re-use of the large pool of existing control data.

翻译：在临床前和非临床毒理学中，减少动物使用是高度期望的。尽管先前在连续终点的虚拟对照组框架下，已提出了可能减少同期对照组样本量的方法，但目前仍缺乏适用于长期致癌性研究中出现的二元结局的方法。为了利用历史对照数据增强当前对照组中的动物数量，我们提出了基于动态贝叶斯借用和风险比的同时可信区间的方法。通过蒙特卡洛模拟评估了诸如族系错误率（FWER）和功效等若干操作特性，并将其与依赖历史观测和当前观测合并的方法进行了比较。结果表明，在最优条件下，基于稳健化先验分布的贝叶斯方法能够显著减少对照组的样本量，同时仍能将FWER控制在令人满意的水平。此外，至少在一定程度上，这些方法能够抵御可能的漂移。这凸显了贝叶斯研究设计通过重复利用现有的大量对照数据来减少毒理学中动物使用的潜力。