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Tutorial: The Practical Application of Longitudinal Structural Equation Mediation Models in Clinical Trials

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Kimberley A. Goldsmith ; David P. MacKinnon ; Trudie Chalder ; Peter D. White ; Michael Sharpe ; Andrew Pickles

Original languageEnglish
JournalPSYCHOLOGICAL METHODS
StateAccepted/In press - 22 May 2017

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Abstract

The study of mediation of treatment effects, or how treatments work, is important to understanding and improving psychological and behavioral treatments, but applications have often focused on mediators and outcomes measured at a single time point. Such crosssectional analyses do not respect the implied temporal ordering that mediation suggests. Clinical trials of treatments often provide repeated measures of outcomes and, increasingly, of mediators as well. A trial with repeated measurements allows for the application of various types of longitudinal structural equation mediation models. These provide for flexibility in modeling, including the incorporation of some types of measurement error and unmeasured confounding that can strengthen the robustness of findings. The usual approach is to identify the most theoretically plausible model and apply that model. In the absence of clear theory, we put forward the option of fitting a few theoretically plausible models, providing a type of sensitivity analysis for the mediation hypothesis. In this tutorial, we outline how to fit several longitudinal mediation models. This will allow readers to learn about one type of model that is of interest, or about several alternative models so that they can take this sensitivity approach. We use the “Pacing, Graded Activity, and Cognitive Behavioral Therapy: A Randomized Evaluation” (PACE) trial of rehabilitative treatments for chronic fatigue syndrome (ISRCTN 54285094) as a motivating example and describe how to fit and interpret various longitudinal mediation models using simulated data similar to those in the PACE trial. The simulated dataset and Mplus code and output are provided.

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