小柯机器人

近日，荷兰乌得勒支大学V. Bonapersona等研究人员发现，使用以往的对照数据能够提高动物实验的统计能力 。相关论文于2021年2月18日在线发表在《自然—神经科学》杂志上。

据研究人员介绍，统计能力低会降低动物研究的可靠性； 然而，出于道德和实践的原因，通过增加样本量来增加统计能力是有问题的。

研究人员基于历史控制数据提出了使用贝叶斯先验的替代解决方案，该解决方案利用了这样的观察结果，即通常而言对照组是彼此相似的。在模拟研究中，研究人员发现，包括来自先前研究对照组的数据可以让使用相同数量动物时达到标准80％功效或增加功效所需的最小样本量减半。基于七项独立的啮齿动物研究，研究人员对生命早期逆境的认知影响进行了该方法有效性的验证。

研究人员提供了一个开放源代码工具RePAIR，可广泛应用于此方法并提高统计能力，从而提高动物实验的可靠性。 

附：英文原文

Title: Increasing the statistical power of animal experiments with historical control data

Author: V. Bonapersona, H. Hoijtink, R. A. Sarabdjitsingh, M. Jols

Issue&Volume: 2021-02-18

Abstract: Low statistical power reduces the reliability of animal research; yet, increasing sample sizes to increase statistical power is problematic for both ethical and practical reasons. We present an alternative solution using Bayesian priors based on historical control data, which capitalizes on the observation that control groups in general are expected to be similar to each other. In a simulation study, we show that including data from control groups of previous studies could halve the minimum sample size required to reach the canonical 80% power or increase power when using the same number of animals. We validated the approach on a dataset based on seven independent rodent studies on the cognitive effects of early-life adversity. We present an open-source tool, RePAIR, that can be widely used to apply this approach and increase statistical power, thereby improving the reliability of animal experiments. Bonapersona and colleagues describe how historical control data can be used to improve statistical power while reducing the number of animals required in experiments. They present an open-source tool, RePAIR, that can be used to apply this approach.

DOI: 10.1038/s41593-020-00792-3

Source: https://www.nature.com/articles/s41593-020-00792-3

Nature Neuroscience：《自然—神经科学》，创刊于1998年。隶属于施普林格·自然出版集团，最新IF：28.771
官方网址：https://www.nature.com/neuro/
投稿链接：https://mts-nn.nature.com/cgi-bin/main.plex