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

• Published in: Psychological methods 2018-06, Vol.23 (2), p.191-207
• Main Authors: Goldsmith, Kimberley A, MacKinnon, David P, Chalder, Trudie, White, Peter D, Sharpe, Michael, Pickles, Andrew
• Format: Article
• Language: English
• Subjects: Clinical Trials; Clinical Trials as Topic - methods; Computer Software; Error of Measurement; Fatigue Syndrome, Chronic - rehabilitation; Humans; Longitudinal Studies; Maximum Likelihood; Models, Statistical; Outcome Assessment, Health Care - methods; Research Design; Structural Equation Modeling
• ISSN: 1082-989X; 1939-1463
• DOI: 10.1037/met0000154

Abstract The study of mediation of treatment effects, or how treatments work, is important to understanding and improving psychological and behavioral treatments, but applications often focus on mediators and outcomes measured at a single time point. Such cross-sectional 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. Repeated measurements allow the application of various types of longitudinal structural equation mediation models. These provide flexibility in modeling, including the ability to incorporate 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, including simplex, latent growth and latent change 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 data set and Mplus code and output are provided. Translational Abstract Studying how treatments work is important to understanding and improving them. We can do this using mediation analysis. This sort of analysis studies a chain of events in which a treatment affects one variable, which then affects another variable in turn. To date, investigators have tended to measure these variables at the same point. Instead, several measurements should be taken so that we can better understand the relationships between the variables over time. Nowadays, studies often measure variables of interest several times during treatment and follow-up. When this is done, more flexible and realistic models of change can be used, giving a more holistic picture of how the treatment worked (the treatment mechanism). This