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Mediators of Treatment Effect in the Back In Action Trial: Using Latent Growth Modeling to Take Change Over Time Into Account

OBJECTIVES: To test whether change in fear-avoidance beliefs was a mediator of the effect of treatment on disability outcome, and to test an analytical approach, latent growth modeling, not often applied to mediation analysis. METHODS: Secondary analysis was carried out on a randomized controlled tr...

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Autores principales: Mansell, Gemma, Hill, Jonathan C., Main, Chris J., Von Korff, Michael, van der Windt, Daniëlle
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5638429/
https://www.ncbi.nlm.nih.gov/pubmed/27930393
http://dx.doi.org/10.1097/AJP.0000000000000463
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author Mansell, Gemma
Hill, Jonathan C.
Main, Chris J.
Von Korff, Michael
van der Windt, Daniëlle
author_facet Mansell, Gemma
Hill, Jonathan C.
Main, Chris J.
Von Korff, Michael
van der Windt, Daniëlle
author_sort Mansell, Gemma
collection PubMed
description OBJECTIVES: To test whether change in fear-avoidance beliefs was a mediator of the effect of treatment on disability outcome, and to test an analytical approach, latent growth modeling, not often applied to mediation analysis. METHODS: Secondary analysis was carried out on a randomized controlled trial designed to compare an intervention addressing fear-avoidance beliefs (n=119) with treatment as usual (n=121) for patients with low back pain, which found the intervention to be effective. Latent growth modelling was used to perform a mediation analysis on the trial data to assess the role of change in fear-avoidance beliefs on disability outcome. The product of coefficients with bias-corrected bootstrapped confidence intervals was used to calculate the mediating effect. RESULTS: A statistically significant mediating effect of fear-avoidance beliefs on the effect of treatment on disability outcome was found (standardized indirect effect −0.35; bias-corrected 95% CI, −0.47 to −0.24). Poor fit of the model to the data suggested that other factors not accounted for in this model are likely to be part of the same mediating pathway. DISCUSSION: Fear-avoidance beliefs were found to mediate the effect of treatment on disability outcome. Measurement of all potential mediator variables in future studies would help to more strongly identify which factors explain observed treatment effects. Latent growth modelling was found to be a useful technique to apply to studies of treatment mediation, suggesting that future studies could use this approach.
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spelling pubmed-56384292017-10-24 Mediators of Treatment Effect in the Back In Action Trial: Using Latent Growth Modeling to Take Change Over Time Into Account Mansell, Gemma Hill, Jonathan C. Main, Chris J. Von Korff, Michael van der Windt, Daniëlle Clin J Pain Original Articles OBJECTIVES: To test whether change in fear-avoidance beliefs was a mediator of the effect of treatment on disability outcome, and to test an analytical approach, latent growth modeling, not often applied to mediation analysis. METHODS: Secondary analysis was carried out on a randomized controlled trial designed to compare an intervention addressing fear-avoidance beliefs (n=119) with treatment as usual (n=121) for patients with low back pain, which found the intervention to be effective. Latent growth modelling was used to perform a mediation analysis on the trial data to assess the role of change in fear-avoidance beliefs on disability outcome. The product of coefficients with bias-corrected bootstrapped confidence intervals was used to calculate the mediating effect. RESULTS: A statistically significant mediating effect of fear-avoidance beliefs on the effect of treatment on disability outcome was found (standardized indirect effect −0.35; bias-corrected 95% CI, −0.47 to −0.24). Poor fit of the model to the data suggested that other factors not accounted for in this model are likely to be part of the same mediating pathway. DISCUSSION: Fear-avoidance beliefs were found to mediate the effect of treatment on disability outcome. Measurement of all potential mediator variables in future studies would help to more strongly identify which factors explain observed treatment effects. Latent growth modelling was found to be a useful technique to apply to studies of treatment mediation, suggesting that future studies could use this approach. Lippincott Williams & Wilkins 2017-09 2017-08-18 /pmc/articles/PMC5638429/ /pubmed/27930393 http://dx.doi.org/10.1097/AJP.0000000000000463 Text en Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/) (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0/
spellingShingle Original Articles
Mansell, Gemma
Hill, Jonathan C.
Main, Chris J.
Von Korff, Michael
van der Windt, Daniëlle
Mediators of Treatment Effect in the Back In Action Trial: Using Latent Growth Modeling to Take Change Over Time Into Account
title Mediators of Treatment Effect in the Back In Action Trial: Using Latent Growth Modeling to Take Change Over Time Into Account
title_full Mediators of Treatment Effect in the Back In Action Trial: Using Latent Growth Modeling to Take Change Over Time Into Account
title_fullStr Mediators of Treatment Effect in the Back In Action Trial: Using Latent Growth Modeling to Take Change Over Time Into Account
title_full_unstemmed Mediators of Treatment Effect in the Back In Action Trial: Using Latent Growth Modeling to Take Change Over Time Into Account
title_short Mediators of Treatment Effect in the Back In Action Trial: Using Latent Growth Modeling to Take Change Over Time Into Account
title_sort mediators of treatment effect in the back in action trial: using latent growth modeling to take change over time into account
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5638429/
https://www.ncbi.nlm.nih.gov/pubmed/27930393
http://dx.doi.org/10.1097/AJP.0000000000000463
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