Departments of Epidemiology and Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA.
Am J Epidemiol. 2012 Oct 1;176(7):608-12. doi: 10.1093/aje/kws213. Epub 2012 Sep 6.
In this commentary, structural equation models (SEMs) are discussed as a tool for epidemiologic analysis. Such models are related to and compared with other analytic approaches often used in epidemiology, including regression analysis, causal diagrams, causal mediation analysis, and marginal structural models. Several of these other approaches in fact developed out of the SEM literature. However, SEMs themselves tend to make much stronger assumptions than these other techniques. SEMs estimate more types of effects than do these other techniques, but this comes at the price of additional assumptions. Many of these assumptions have often been ignored and not carefully evaluated when SEMs have been used in practice. In light of the strong assumptions employed by SEMs, the author argues that they should be used principally for the purposes of exploratory analysis and hypothesis generation when a broad range of effects are potentially of interest.
在这篇评论中，将讨论结构方程模型 (SEM) 作为一种流行病学分析工具。此类模型与流行病学中常用的其他分析方法（包括回归分析、因果图、因果中介分析和边缘结构模型）有关，并可与之进行比较。实际上，其中的一些其他方法正是从 SEM 文献中发展而来的。然而，SEM 本身往往比这些其他技术具有更强的假设。SEM 可以估计比这些其他技术更多类型的效应，但这是以更多的假设为代价的。在实践中使用 SEM 时，人们常常忽略了许多这些假设，并且没有对其进行仔细评估。鉴于 SEM 所采用的严格假设，作者认为，当广泛关注各种效应时，它们主要应被用于探索性分析和假设生成。
