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

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Tutorial : The Practical Application of Longitudinal Structural Equation Mediation Models in Clinical Trials. / Goldsmith, Kimberley A.; MacKinnon, David P.; Chalder, Trudie; White, Peter D.; Sharpe, Michael; Pickles, Andrew.

In: PSYCHOLOGICAL METHODS, Vol. 23, No. 2, 06.2018, p. 191-207.

Research output: Contribution to journalArticle

Harvard

Goldsmith, KA, MacKinnon, DP, Chalder, T, White, PD, Sharpe, M & Pickles, A 2018, 'Tutorial: The Practical Application of Longitudinal Structural Equation Mediation Models in Clinical Trials', PSYCHOLOGICAL METHODS, vol. 23, no. 2, pp. 191-207. https://doi.org/10.1037/met0000154

APA

Goldsmith, K. A., MacKinnon, D. P., Chalder, T., White, P. D., Sharpe, M., & Pickles, A. (2018). Tutorial: The Practical Application of Longitudinal Structural Equation Mediation Models in Clinical Trials. PSYCHOLOGICAL METHODS, 23(2), 191-207. https://doi.org/10.1037/met0000154

Vancouver

Goldsmith KA, MacKinnon DP, Chalder T, White PD, Sharpe M, Pickles A. Tutorial: The Practical Application of Longitudinal Structural Equation Mediation Models in Clinical Trials. PSYCHOLOGICAL METHODS. 2018 Jun;23(2):191-207. https://doi.org/10.1037/met0000154

Author

Goldsmith, Kimberley A. ; MacKinnon, David P. ; Chalder, Trudie ; White, Peter D. ; Sharpe, Michael ; Pickles, Andrew. / Tutorial : The Practical Application of Longitudinal Structural Equation Mediation Models in Clinical Trials. In: PSYCHOLOGICAL METHODS. 2018 ; Vol. 23, No. 2. pp. 191-207.

Bibtex Download

@article{a50c287c17da4f9b94f42d3ec0851eea,
title = "Tutorial: The Practical Application of Longitudinal Structural Equation Mediation Models in Clinical Trials",
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. ",
author = "Goldsmith, {Kimberley A.} and MacKinnon, {David P.} and Trudie Chalder and White, {Peter D.} and Michael Sharpe and Andrew Pickles",
year = "2018",
month = jun,
doi = "10.1037/met0000154",
language = "English",
volume = "23",
pages = "191--207",
journal = "PSYCHOLOGICAL METHODS",
issn = "1082-989X",
publisher = "American Psychological Association Inc.",
number = "2",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Tutorial

T2 - The Practical Application of Longitudinal Structural Equation Mediation Models in Clinical Trials

AU - Goldsmith, Kimberley A.

AU - MacKinnon, David P.

AU - Chalder, Trudie

AU - White, Peter D.

AU - Sharpe, Michael

AU - Pickles, Andrew

PY - 2018/6

Y1 - 2018/6

N2 - 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.

AB - 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.

U2 - 10.1037/met0000154

DO - 10.1037/met0000154

M3 - Article

VL - 23

SP - 191

EP - 207

JO - PSYCHOLOGICAL METHODS

JF - PSYCHOLOGICAL METHODS

SN - 1082-989X

IS - 2

ER -

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