Warwick Medical School, University of Warwick, Coventry, United Kingdom.
Sheffield School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom.
Elife. 2018 Jan 10;7:e32486. doi: 10.7554/eLife.32486.
Many studies in the biomedical research literature report analyses that fail to recognise important data dependencies from multilevel or complex experimental designs. Statistical inferences resulting from such analyses are unlikely to be valid and are often potentially highly misleading. Failure to recognise this as a problem is often referred to in the statistical literature as a (UoA) issue. Here, by analysing two example datasets in a simulation study, we demonstrate the impact of UoA issues on study efficiency and estimation bias, and highlight where errors in analysis can occur. We also provide code (written in R) as a resource to help researchers undertake their own statistical analyses.
许多生物医学研究文献中的研究报告分析未能识别多层次或复杂实验设计中的重要数据依赖性。由此类分析得出的统计推论不太可能有效，而且往往可能极具误导性。在统计文献中，未能认识到这是一个问题通常被称为（UoA）问题。在这里，我们通过在模拟研究中分析两个示例数据集，展示了 UoA 问题对研究效率和估计偏差的影响，并指出了分析中可能出现错误的地方。我们还提供了代码（用 R 编写）作为帮助研究人员进行自己的统计分析的资源。
