A tutorial for conducting intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)
A tutorial for conducting intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)
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DOI：
10.1016/j.ssmph.2024.101664
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发表时间：
2024-04-22
影响因子：
4.7
通讯作者：
Merlo，Juan
中科院分区：
文献类型：
--
作者：
Evans，Clare R.;Leckie，George;Merlo，Juan
个体异质性和歧视准确性的交叉多水平分析（I-MAIHDA）是一种调查不平等的创新方法，包括健康，疾病，心理，社会经济和其他结果的交叉不平等。I-MAIHDA和相关的MAIHDA方法在概念和方法上优于传统的单水平回归分析。通过研究由众多边缘化和压迫的连锁系统产生的不平等现象，并解决传统分析中研究相互作用的许多局限性，交叉MAIHDA在社会流行病学，健康心理学，精准医学和公共卫生，环境正义等方面提供了一个有价值的分析工具。该方法允许估计交叉层之间的平均差异（层的不平等），深入探索的相互作用的影响，以及分解的总个体差异（异质性）在个人的结果内和之间的strategy.Specific建议进行和解释MAIHDA模型已经分散在一个新兴的文学。我们将这些知识整合到一个可访问的概念和应用教程中，用于研究连续和二元个体结果。我们在插图中强调了I-MAIHDA，但是本教程也为理解相关方法提供了信息，例如多类别MAIHDA，已被提议用于临床研究及其他领域。本教程将支持那些希望进行自己的分析和那些有兴趣扩大他们的理解的方法的读者。为了证明该方法，我们提供了一步一步的分析建议，并提出了一个说明性的健康应用程序使用模拟数据。我们提供数据和语法来复制我们所有的分析。
Intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (I-MAIHDA) is an innovative approach for investigating inequalities, including intersectional inequalities in health, disease, psychosocial, socioeconomic, and other outcomes. I-MAIHDA and related MAIHDA approaches have conceptual and methodological advantages over conventional single-level regression analysis. By enabling the study of inequalities produced by numerous interlocking systems of marginalization and oppression, and by addressing many of the limitations of studying interactions in conventional analyses, intersectional MAIHDA provides a valuable analytical tool in social epidemiology, health psychology, precision medicine and public health, environmental justice, and beyond. The approach allows for estimation of average differences between intersectional strata (stratum inequalities), in-depth exploration of interaction effects, as well as decomposition of the total individual variation (heterogeneity) in individual outcomes within and between strata.Specific advice for conducting and interpreting MAIHDA models has been scattered across a burgeoning literature. We consolidate this knowledge into an accessible conceptual and applied tutorial for studying both continuous and binary individual outcomes. We emphasize I-MAIHDA in our illustration, however this tutorial is also informative for understanding related approaches, such asmulticategorical MAIHDA, which has been proposed for use in clinical research and beyond. The tutorial will support readers who wish to perform their own analyses and those interested in expanding their understanding of the approach. To demonstrate the methodology, we provide step-by-step analytical advice and present an illustrative health application using simulated data. We provide the data and syntax to replicate all our analyses.