Perils and pitfalls of mixed-effects regression models in biology
Perils and pitfalls of mixed-effects regression models in biology
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DOI：
10.7717/peerj.9522
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发表时间：
2020-08-12
期刊：
影响因子：
2.7
通讯作者：
Hodgson, David J.
中科院分区：
文献类型：
--
作者：
Silk, Matthew J.;Harrison, Xavier A.;Hodgson, David J.
从核酸到生态系统的所有组织尺度的生物系统本质上都是复杂和可变的。因此，生物学家使用统计分析来检测系统噪声中的信号。统计模型推断趋势，发现功能关系，并检测组之间存在的差异或由实验操作引起的差异。他们还使用统计关系来帮助预测不确定的未来。生物科学的所有分支现在都接受混合效应建模的可能性及其用于划分噪声和信号的灵活工具包。然而，混合效应模型并不是解决不良实验设计的灵丹妙药，在推断或推导固定效应和随机效应的重要性时应谨慎使用。在这里，我们描述了一个选择的危险和陷阱，在生物学文献中广泛存在，但可以通过仔细的反思，建模和模型检查来避免。我们专注于不谨慎的建模风险暴露于这些陷阱和得出不正确结论的情况。我们的立场是，重要性、信息内容或可信度的陈述在生物学研究中都有其地位，只要这些陈述是谨慎的，并通过检查假设的有效性而得到充分的信息。我们的目的是揭示混合模型估计中的潜在危险和陷阱，以便研究人员可以更好地认识和自信地使用这些强大的方法。我们的例子是生态学的，但很容易翻译到生物学的所有分支。
Biological systems, at all scales of organisation from nucleic acids to ecosystems, are inherently complex and variable. Biologists therefore use statistical analyses to detect signal among this systemic noise. Statistical models infer trends, find functional relationships and detect differences that exist among groups or are caused by experimental manipulations. They also use statistical relationships to help predict uncertain futures. All branches of the biological sciences now embrace the possibilities of mixed-effects modelling and its flexible toolkit for partitioning noise and signal. The mixed-effects model is not, however, a panacea for poor experimental design, and should be used with caution when inferring or deducing the importance of both fixed and random effects. Here we describe a selection of the perils and pitfalls that are widespread in the biological literature, but can be avoided by careful reflection, modelling and model-checking. We focus on situations where incautious modelling risks exposure to these pitfalls and the drawing of incorrect conclusions. Our stance is that statements of significance, information content or credibility all have their place in biological research, as long as these statements are cautious and well-informed by checks on the validity of assumptions. Our intention is to reveal potential perils and pitfalls in mixed model estimation so that researchers can use these powerful approaches with greater awareness and confidence. Our examples are ecological, but translate easily to all branches of biology.