使用发育动物模型的研究中的设计与统计方法
Design and statistical methods in studies using animal models of development.
作者信息
Festing Michael F W
出版信息
ILAR J. 2006;47(1):5-14. doi: 10.1093/ilar.47.1.5.
Experiments involving neonates should follow the same basic principles as most other experiments. They should be unbiased, be powerful, have a good range of applicability, not be excessively complex, and be statistically analyzable to show the range of uncertainty in the conclusions. However, investigation of growth and development in neonatal multiparous animals poses special problems associated with the choice of "experimental unit" and differences between litters: the "litter effect." Two main types of experiments are described, with recommendations regarding their design and statistical analysis: First, the "between litter design" is used when females or whole litters are assigned to a treatment group. In this case the litter, rather than the individuals within a litter, is the experimental unit and should be the unit for the statistical analysis. Measurements made on individual neonatal animals need to be combined within each litter. Counting each neonate as a separate observation may lead to incorrect conclusions. The number of observations for each outcome ("n") is based on the number of treated females or whole litters. Where litter sizes vary, it may be necessary to use a weighted statistical analysis because means based on more observations are more reliable than those based on a few observations. Second, the more powerful "within-litter design" is used when neonates can be individually assigned to treatment groups so that individuals within a litter can have different treatments. In this case, the individual neonate is the experimental unit, and "n" is based on the number of individual pups, not on the number of whole litters. However, variation in litter size means that it may be difficult to perform balanced experiments with equal numbers of animals in each treatment group within each litter. This increases the complexity of the statistical analysis. A numerical example using a general linear model analysis of variance is provided in the Appendix. The use of isogenic strains should be considered in neonatal research. These strains are like immortal clones of genetically identical individuals (i.e., they are uniform, stable, and repeatable), and their use should result in more powerful experiments. Inbred females mated to males of a different inbred strain will produce F1 hybrid offspring that will be uniform, vigorous, and genetically identical. Different strains may develop at different rates and respond differently to experimental treatments.
涉及新生儿的实验应遵循与大多数其他实验相同的基本原则。这些实验应无偏差、有说服力、具有广泛的适用性、不过于复杂,并且能够进行统计分析以显示结论中的不确定性范围。然而,对新生多胎动物的生长和发育进行研究存在与“实验单位”的选择以及窝仔之间差异相关的特殊问题:即“窝仔效应”。本文描述了两种主要类型的实验,并针对其设计和统计分析提出了建议:第一,当将雌性动物或整个窝仔分配到一个治疗组时,使用“窝间设计”。在这种情况下,窝仔而非窝仔内的个体是实验单位,并且应该是统计分析的单位。对单个新生动物进行的测量需要在每个窝仔内进行合并。将每个新生儿视为一个单独的观察对象可能会导致错误的结论。每个结果的观察次数(“n”)基于接受治疗的雌性动物数量或整个窝仔数量。如果窝仔大小不同,可能有必要使用加权统计分析,因为基于更多观察值的均值比基于少数观察值的均值更可靠。第二,当新生儿可以被单独分配到治疗组,使得窝仔内的个体可以接受不同的治疗时,使用更有说服力的“窝内设计”。在这种情况下,单个新生儿是实验单位,“n”基于个体幼崽的数量,而不是整个窝仔的数量。然而,窝仔大小的差异意味着可能难以在每个窝仔内的每个治疗组中进行动物数量相等的平衡实验。这增加了统计分析的复杂性。附录中提供了一个使用一般线性模型方差分析的数值示例。在新生儿研究中应考虑使用同基因品系。这些品系就像是基因相同个体的不朽克隆(即它们是一致、稳定且可重复的),使用它们应该会使实验更有说服力。与不同近交系雄性交配的近交雌性会产生F1杂交后代,这些后代将是一致、有活力且基因相同的。不同品系的发育速度可能不同,对实验处理的反应也可能不同。