Department of Psychology, Applied Social and Health Psychology, University of Zurich, Binzmuehlestrasse, Zurich, Switzerland.
Institute of Psychology, University of Bern, Bern, Switzerland.
Ann Behav Med. 2021 May 6;55(5):476-488. doi: 10.1093/abm/kaaa066.
Mediation analysis is an important tool for understanding the processes through which interventions affect health outcomes over time. Typically the temporal intervals between X, M, and Y are fixed by design, and little focus is given to the temporal dynamics of the processes.
In this article, we aim to highlight the importance of considering the timing of the causal effects of a between-person intervention X, on M and Y, resulting in a deeper understanding of mediation.
We provide a framework for examining the impact of a between-person intervention X on M and Y over time when M and Y are measured repeatedly. Five conceptual and analytic steps involve visualizing the effects of the intervention on Y, M, the relationship of M and Y, and the mediating process over time and selecting an appropriate analytic model.
We demonstrate how these steps can be applied to two empirical examples of health behavior change interventions. We show that the patterns of longitudinal mediation can be fit with versions of longitudinal multilevel structural equation models that represent how the magnitude of direct and indirect effects vary over time.
We urge researchers and methodologists to pay more attention to temporal dynamics in the causal analysis of interventions.
中介分析是理解干预措施随时间如何影响健康结果的重要工具。通常情况下，X、M 和 Y 之间的时间间隔是由设计固定的，而很少关注这些过程的时间动态。
本文旨在强调考虑个体间干预 X 对 M 和 Y 的因果效应发生时间的重要性，从而更深入地理解中介分析。
当 M 和 Y 被重复测量时，我们提供了一个框架来检验个体间干预 X 对 M 和 Y 的随时间变化的影响。五个概念和分析步骤包括可视化干预对 Y、M、M 和 Y 之间关系以及中介过程的影响，以及选择适当的分析模型。
我们展示了如何将这些步骤应用于两个健康行为改变干预的实证示例。我们表明，纵向中介的模式可以用代表直接和间接效应随时间变化的纵向多层结构方程模型的版本来拟合。
我们敦促研究人员和方法学家更加关注干预因果分析中的时间动态。
