Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, United States.
Department of Applied Health Science, Indiana University School of Public Health-Bloomington, Bloomington, United States.
Elife. 2024 May 16;13:e82268. doi: 10.7554/eLife.82268.
We discuss 12 misperceptions, misstatements, or mistakes concerning the use of covariates in observational or research. Additionally, we offer advice to help investigators, editors, reviewers, and readers make more informed decisions about conducting and interpreting research where the influence of covariates may be at issue. We primarily address misperceptions in the context of statistical management of the covariates through various forms of modeling, although we also emphasize design and model or variable selection. Other approaches to addressing the effects of covariates, including matching, have logical extensions from what we discuss here but are not dwelled upon heavily. The misperceptions, misstatements, or mistakes we discuss include accurate representation of covariates, effects of measurement error, overreliance on covariate categorization, underestimation of power loss when controlling for covariates, misinterpretation of significance in statistical models, and misconceptions about confounding variables, selecting on a collider, and p value interpretations in covariate-inclusive analyses. This condensed overview serves to correct common errors and improve research quality in general and in nutrition research specifically.
我们讨论了 12 种关于在观察性或研究中使用协变量的误解、错误陈述或错误。此外，我们还提供了一些建议，以帮助研究人员、编辑、审稿人和读者在协变量可能有影响的情况下，更明智地做出关于进行和解释研究的决策。我们主要在通过各种形式的建模对协变量进行统计管理的背景下解决误解，尽管我们也强调设计和模型或变量选择。解决协变量影响的其他方法，包括匹配，从我们这里讨论的内容中可以得到逻辑上的扩展，但并没有详细讨论。我们讨论的误解、错误陈述或错误包括对协变量的准确表示、测量误差的影响、过度依赖协变量分类、在控制协变量时低估力量损失、对统计模型中显著性的误解，以及对混杂变量、在共发器上选择和协变量分析中 p 值解释的误解。这个简要概述旨在纠正常见错误，提高研究质量，特别是在营养研究中。
