Centre for Health Informatics, Institute of Population Health, University of Manchester, Vaughan House, Portsmouth Street, M13 9GB, UK; NIHR School for Primary Care Research, University of Manchester, 5th floor Williamson Building, M13 9PL, UK.
Centre for Health Informatics, Institute of Population Health, University of Manchester, Vaughan House, Portsmouth Street, M13 9GB, UK.
J Clin Epidemiol. 2018 Jan;93:79-83. doi: 10.1016/j.jclinepi.2017.09.012. Epub 2017 Sep 21.
Effect heterogeneity, the variability of an association or exposure across subgroups, usually warrants further investigation. The aim of this deeper analysis is to identify effect modifiers (or moderators) and quantify their relationship with the exposure. We explain why it is better to harness interaction effects within a single analytic model than to use separate models to analyze each subgroup. Using examples, we demonstrate a practical approach to modeling and interpretation with interaction terms from various measurement scales (categorical by categorical; categorical by continuous; and continuous by continuous).
效应异质性，即关联或暴露在亚组间的可变性，通常需要进一步研究。这种更深入分析的目的是确定效应修饰因子（或调节剂），并量化它们与暴露的关系。我们解释了为什么最好在单个分析模型中利用交互作用，而不是使用单独的模型来分析每个亚组。通过示例，我们展示了一种实用的方法，用于对来自各种测量尺度（类别与类别；类别与连续；连续与连续）的交互项进行建模和解释。
