St-Pierre N R
Department of Animal Sciences, The Ohio State University, Columbus 43210, USA.
J Dairy Sci. 2007 Jun;90 Suppl 1:E87-99. doi: 10.3168/jds.2006-612.
Increasingly, research is being performed in which animals subjected to a common treatment are also housed in a common pen. Issues have been raised regarding the proper planning of experiments and conduct of statistical analyses in these instances. This paper reviews the problems associated with ignoring animal grouping during data analyses, and examples are provided for appropriate methods to use when animals are grouped in pens. Using animals as the error term when treatments are applied to pens can result in biased estimates of treatment effects when pens are of unequal sizes and animals are moved in and out of the pens. It always results in biased probability statements regarding their significance. The pen effect includes systematic effects other than that of the treatment, which is why pens must be replicated and randomized. In essence, pen studies have an implicit split-plot design in which the main plots (pens) receive the treatments of interest, whereas the subplots (cows) receive all the same subplot treatment. Using the subplot error to test main-plot treatment effects creates inflated degrees of freedom and uses the wrong denominator mean square to test the effect; hence, severely biasing the test of significance for the treatment effects and resulting in an invalid causal inference base. The interactions of pens with the fixed-effect elements of the treatment design are the correct error terms for those fixed-effects factors applied to the pens. The same statistical designs used with animals as experimental units can be used with pens. The number of experimental units to achieve a given power can be, and generally is, considerably less with pens because the variance among pens is generally less than the variance of cows within pens. Pens must be replicated, randomized, and included in the statistical model to ensure valid statistical inference.
越来越多的研究中,接受相同处理的动物被饲养在同一个围栏中。在这些情况下,关于实验的合理规划和统计分析的进行引发了一些问题。本文回顾了数据分析时忽略动物分组所带来的问题,并给出了动物在围栏中分组时适用的恰当方法示例。当处理应用于围栏时,将动物作为误差项,在围栏大小不等且动物进出围栏时,会导致对处理效应的估计有偏差。这总是会导致关于其显著性的概率陈述有偏差。围栏效应包括除处理效应之外的系统效应,这就是为什么围栏必须重复设置并随机化。本质上,围栏研究有一种隐含的裂区设计,其中主区(围栏)接受感兴趣的处理,而副区(奶牛)接受所有相同的副区处理。使用副区误差来检验主区处理效应会使自由度膨胀,并使用错误的分母均方来检验效应;因此,会严重偏向处理效应的显著性检验,并导致无效的因果推断基础。围栏与处理设计的固定效应元素的相互作用是应用于围栏的那些固定效应因素的正确误差项。与将动物作为实验单位时使用的相同统计设计也可用于围栏。由于围栏间的方差通常小于围栏内奶牛的方差,实现给定检验效能所需的实验单位数量对于围栏来说可以且通常会少得多。围栏必须重复设置、随机化并纳入统计模型,以确保有效的统计推断。