Department of Public Health and Preventive Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239-3098, USA.
Behav Brain Res. 2011 Aug 1;221(1):271-5. doi: 10.1016/j.bbr.2011.03.007. Epub 2011 Mar 17.
In experimental designs of animal models, memory is often assessed by the time for a performance measure to occur (latency). Depending on the cognitive test, this may be the time it takes an animal to escape to a hidden platform (water maze), an escape tunnel (Barnes maze) or to enter a dark component (passive avoidance test). Latency outcomes are usually statistically analyzed using ANOVAs. Besides strong distributional assumptions, ANOVA cannot properly deal with animals not showing the performance measure within the trial time, potentially causing biased and misleading results. We propose an alternative approach for statistical analyses of latency outcomes. These analyses have less distributional assumptions and adequately handle results of trials in which the performance measure did not occur within the trial time. The proposed method is well known from survival analyses, provides comprehensible statistical results and allows the generation of meaningful graphs. Experiments of behavioral neuroscience and anesthesiology are used to illustrate this method.
在动物模型的实验设计中，通常通过性能测量的发生时间（潜伏期）来评估记忆。根据认知测试的不同，这可能是动物逃离隐藏平台（水迷宫）、逃脱隧道（巴恩斯迷宫）或进入黑暗部分（被动回避测试）所需的时间。潜伏期结果通常使用方差分析（ANOVA）进行统计分析。除了强烈的分布假设外，方差分析不能正确处理在试验时间内未表现出性能测量的动物，这可能导致有偏差和误导性的结果。我们提出了一种替代方法来对潜伏期结果进行统计分析。这些分析的分布假设较少，并且能够充分处理在试验时间内未发生性能测量的试验结果。该方法源自生存分析，提供了易于理解的统计结果，并允许生成有意义的图形。行为神经科学和麻醉学实验用于说明这种方法。
