{"title":"Incorporating sources of correlation between outcomes: An introduction to mixed models.","authors":"Limeng Liu, Ashley Petersen","doi":"10.1177/00236772241259518","DOIUrl":null,"url":null,"abstract":"<p><p>Animal research often involves measuring the outcomes of interest multiple times on the same animal, whether over time or for different exposures. These repeated outcomes measured on the same animal are correlated due to animal-specific characteristics. While this repeated measures data can address more complex research questions than single-outcome data, the statistical analysis must take into account the study design resulting in correlated outcomes, which violate the independence assumption of standard statistical methods (e.g. a two-sample <i>t</i>-test, linear regression). When standard statistical methods are incorrectly used to analyze correlated outcome data, the statistical inference (i.e. confidence intervals and <i>p</i>-values) will be incorrect, with some settings leading to null findings too often and others producing statistically significant findings despite no support for this in the data. Instead, researchers can leverage approaches designed specifically for correlated outcomes. In this article, we discuss common study designs that lead to correlated outcome data, motivate the intuition about the impact of improperly analyzing correlated outcomes using methods for independent data, and introduce approaches that properly leverage correlated outcome data.</p>","PeriodicalId":18013,"journal":{"name":"Laboratory Animals","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Laboratory Animals","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/00236772241259518","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/9/20 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"VETERINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Animal research often involves measuring the outcomes of interest multiple times on the same animal, whether over time or for different exposures. These repeated outcomes measured on the same animal are correlated due to animal-specific characteristics. While this repeated measures data can address more complex research questions than single-outcome data, the statistical analysis must take into account the study design resulting in correlated outcomes, which violate the independence assumption of standard statistical methods (e.g. a two-sample t-test, linear regression). When standard statistical methods are incorrectly used to analyze correlated outcome data, the statistical inference (i.e. confidence intervals and p-values) will be incorrect, with some settings leading to null findings too often and others producing statistically significant findings despite no support for this in the data. Instead, researchers can leverage approaches designed specifically for correlated outcomes. In this article, we discuss common study designs that lead to correlated outcome data, motivate the intuition about the impact of improperly analyzing correlated outcomes using methods for independent data, and introduce approaches that properly leverage correlated outcome data.
动物研究通常涉及在同一动物身上多次测量感兴趣的结果,无论是随时间推移还是针对不同的暴露。由于动物的特异性,在同一动物身上重复测量的结果具有相关性。虽然与单一结果数据相比,重复测量数据可以解决更复杂的研究问题,但统计分析必须考虑到研究设计导致的相关结果,这违反了标准统计方法(如双样本 t 检验、线性回归)的独立性假设。如果不正确地使用标准统计方法来分析相关结果数据,统计推断(即置信区间和 p 值)将是不正确的,有些设置往往会导致无效结果,而有些设置则会产生具有统计意义的结果,尽管数据中并不支持这种结果。相反,研究人员可以利用专为相关结果设计的方法。在本文中,我们将讨论导致相关结果数据的常见研究设计,激发对使用独立数据方法不当分析相关结果的影响的直觉,并介绍正确利用相关结果数据的方法。
期刊介绍:
The international journal of laboratory animal science and welfare, Laboratory Animals publishes peer-reviewed original papers and reviews on all aspects of the use of animals in biomedical research. The journal promotes improvements in the welfare or well-being of the animals used, it particularly focuses on research that reduces the number of animals used or which replaces animal models with in vitro alternatives.