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Identifying causal mechanisms in psychotherapy: What can we learn from causal mediation analysis?
Clinical Psychology & Psychotherapy ( IF 3.198 ) Pub Date : 2021-11-12 , DOI: 10.1002/cpp.2687
Hugo Hesser 1, 2
Affiliation  

Despite widespread interest in the development of process-based psychotherapies, little is still known about the underlying processes that underpin our most effective therapies. Statistical mediation analysis is a commonly used analytical method to evaluate how, or by which processes, a therapy causes change in an outcome. Causal mediation analysis (CMA) represents a new advancement in mediation analysis that employs causally defined direct and indirect effects based on potential outcomes. These novel ideas and analytical techniques have been characterized as revolutionary in epidemiology and biostatistics, although they are not (yet) widely known among researchers in clinical psychology. In this paper, I outline the fundamental concepts underlying CMA, clarify the differences between the CMA approach and the traditional approach to mediation, and identify two important data analytical aspects that have been emphasized as a result of these recent advancements. To illustrate the key ideas, assumptions, and mathematical definitions intuitively, an applied clinical example from a previously published randomized controlled trial is used. CMA's main contributions are discussed, as well as some of the key challenges. Finally, it is argued that the most significant contribution of CMA is the formalization of mediation in a unified causal framework with clear assumptions.

中文翻译:

识别心理治疗中的因果机制:我们可以从因果中介分析中学到什么?

尽管人们对基于过程的心理疗法的发展产生了广泛的兴趣,但对于支撑我们最有效疗法的潜在过程仍然知之甚少。统计中介分析是一种常用的分析方法,用于评估治疗如何或通过哪些过程导致结果发生变化。因果中介分析(CMA)代表了调解分析的新进展,它根据潜在结果采用因果定义的直接和间接影响。这些新颖的想法和分析技术在流行病学和生物统计学中被认为是革命性的,尽管它们(尚未)在临床心理学研究人员中广为人知。在本文中,我概述了 CMA 的基本概念,阐明了 CMA 方法与传统调解方法之间的区别,并确定了由于这些最新进展而被强调的两个重要数据分析方面。为了直观地说明关键思想、假设和数学定义,我们使用了来自先前发表的随机对照试验的应用临床示例。讨论了 CMA 的主要贡献,以及一些关键挑战。最后,有人认为 CMA 最重要的贡献是在具有明确假设的统一因果框架中将调解形式化。
更新日期:2021-11-12
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