A Bayesian predictive approach for dealing with pseudoreplication
A Bayesian predictive approach for dealing with pseudoreplication
复制标题
DOI：
10.1038/s41598-020-59384-7
复制
发表时间：
2020-02-11
影响因子：
4.6
通讯作者：
Munafo, Marcus R.
中科院分区：
文献类型：
--
作者：
Lazic, Stanley E.;Mellor, Jack R.;Munafo, Marcus R.
当测量值或数据点的数量超过真实重复的数量，并且统计分析将所有数据点视为独立并因此完全影响结果时，就会发生伪复制。通过人为地膨胀样本量，伪复制导致了不可重复性，这是生物学研究中普遍存在的问题。在某些领域，发表的实验中有一半以上存在假复制，这使其成为对推理有效性的最大威胁之一。如果他们的假设是关于假复制而不是真正的复制，研究人员可能不愿使用适当的统计方法；例如，当对怀孕的雌性啮齿动物(真正的复制)进行干预时，但假设是关于对多个后代(伪复制)的影响。我们建议使用贝叶斯预测方法，这使研究人员能够对感兴趣的生物实体做出有效的推断，即使它们是伪复制的，并使用两个活体数据集展示了这种方法的好处。
Pseudoreplication occurs when the number of measured values or data points exceeds the number of genuine replicates, and when the statistical analysis treats all data points as independent and thus fully contributing to the result. By artificially inflating the sample size, pseudoreplication contributes to irreproducibility, and it is a pervasive problem in biological research. In some fields, more than half of published experiments have pseudoreplication - making it one of the biggest threats to inferential validity. Researchers may be reluctant to use appropriate statistical methods if their hypothesis is about the pseudoreplicates and not the genuine replicates; for example, when an intervention is applied to pregnant female rodents (genuine replicates) but the hypothesis is about the effect on the multiple offspring (pseudoreplicates). We propose using a Bayesian predictive approach, which enables researchers to make valid inferences about biological entities of interest, even if they are pseudoreplicates, and show the benefits of this approach using two in vivo data sets.