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Sample size determination for mediation analysis of longitudinal data

BACKGROUND: Sample size planning for longitudinal data is crucial when designing mediation studies because sufficient statistical power is not only required in grant applications and peer-reviewed publications, but is essential to reliable research results. However, sample size determination is not...

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Detalles Bibliográficos
Autores principales: Pan, Haitao, Liu, Suyu, Miao, Danmin, Yuan, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5870539/
https://www.ncbi.nlm.nih.gov/pubmed/29580203
http://dx.doi.org/10.1186/s12874-018-0473-2
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author Pan, Haitao
Liu, Suyu
Miao, Danmin
Yuan, Ying
author_facet Pan, Haitao
Liu, Suyu
Miao, Danmin
Yuan, Ying
author_sort Pan, Haitao
collection PubMed
description BACKGROUND: Sample size planning for longitudinal data is crucial when designing mediation studies because sufficient statistical power is not only required in grant applications and peer-reviewed publications, but is essential to reliable research results. However, sample size determination is not straightforward for mediation analysis of longitudinal design. METHODS: To facilitate planning the sample size for longitudinal mediation studies with a multilevel mediation model, this article provides the sample size required to achieve 80% power by simulations under various sizes of the mediation effect, within-subject correlations and numbers of repeated measures. The sample size calculation is based on three commonly used mediation tests: Sobel’s method, distribution of product method and the bootstrap method. RESULTS: Among the three methods of testing the mediation effects, Sobel’s method required the largest sample size to achieve 80% power. Bootstrapping and the distribution of the product method performed similarly and were more powerful than Sobel’s method, as reflected by the relatively smaller sample sizes. For all three methods, the sample size required to achieve 80% power depended on the value of the ICC (i.e., within-subject correlation). A larger value of ICC typically required a larger sample size to achieve 80% power. Simulation results also illustrated the advantage of the longitudinal study design. The sample size tables for most encountered scenarios in practice have also been published for convenient use. CONCLUSIONS: Extensive simulations study showed that the distribution of the product method and bootstrapping method have superior performance to the Sobel’s method, but the product method was recommended to use in practice in terms of less computation time load compared to the bootstrapping method. A R package has been developed for the product method of sample size determination in mediation longitudinal study design. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-018-0473-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-58705392018-03-29 Sample size determination for mediation analysis of longitudinal data Pan, Haitao Liu, Suyu Miao, Danmin Yuan, Ying BMC Med Res Methodol Research Article BACKGROUND: Sample size planning for longitudinal data is crucial when designing mediation studies because sufficient statistical power is not only required in grant applications and peer-reviewed publications, but is essential to reliable research results. However, sample size determination is not straightforward for mediation analysis of longitudinal design. METHODS: To facilitate planning the sample size for longitudinal mediation studies with a multilevel mediation model, this article provides the sample size required to achieve 80% power by simulations under various sizes of the mediation effect, within-subject correlations and numbers of repeated measures. The sample size calculation is based on three commonly used mediation tests: Sobel’s method, distribution of product method and the bootstrap method. RESULTS: Among the three methods of testing the mediation effects, Sobel’s method required the largest sample size to achieve 80% power. Bootstrapping and the distribution of the product method performed similarly and were more powerful than Sobel’s method, as reflected by the relatively smaller sample sizes. For all three methods, the sample size required to achieve 80% power depended on the value of the ICC (i.e., within-subject correlation). A larger value of ICC typically required a larger sample size to achieve 80% power. Simulation results also illustrated the advantage of the longitudinal study design. The sample size tables for most encountered scenarios in practice have also been published for convenient use. CONCLUSIONS: Extensive simulations study showed that the distribution of the product method and bootstrapping method have superior performance to the Sobel’s method, but the product method was recommended to use in practice in terms of less computation time load compared to the bootstrapping method. A R package has been developed for the product method of sample size determination in mediation longitudinal study design. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-018-0473-2) contains supplementary material, which is available to authorized users. BioMed Central 2018-03-27 /pmc/articles/PMC5870539/ /pubmed/29580203 http://dx.doi.org/10.1186/s12874-018-0473-2 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Pan, Haitao
Liu, Suyu
Miao, Danmin
Yuan, Ying
Sample size determination for mediation analysis of longitudinal data
title Sample size determination for mediation analysis of longitudinal data
title_full Sample size determination for mediation analysis of longitudinal data
title_fullStr Sample size determination for mediation analysis of longitudinal data
title_full_unstemmed Sample size determination for mediation analysis of longitudinal data
title_short Sample size determination for mediation analysis of longitudinal data
title_sort sample size determination for mediation analysis of longitudinal data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5870539/
https://www.ncbi.nlm.nih.gov/pubmed/29580203
http://dx.doi.org/10.1186/s12874-018-0473-2
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