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Third-variable effect analysis with multilevel additive models
Third-variable effect refers to the effect transmitted by third-variables that intervene in the relationship between an exposure and a response variable. Third-variable effect analysis has been broadly studied in many fields. However, it remains a challenge for researchers to differentiate indirect...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7584256/ https://www.ncbi.nlm.nih.gov/pubmed/33095796 http://dx.doi.org/10.1371/journal.pone.0241072 |
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author | Yu, Qingzhao Li, Bin |
author_facet | Yu, Qingzhao Li, Bin |
author_sort | Yu, Qingzhao |
collection | PubMed |
description | Third-variable effect refers to the effect transmitted by third-variables that intervene in the relationship between an exposure and a response variable. Third-variable effect analysis has been broadly studied in many fields. However, it remains a challenge for researchers to differentiate indirect effect of individual factor from multiple third-variables, especially when the involving variables are of hierarchical structure. Yu et al. (2014) defined third-variable effects that were consistent for all different types of response (categorical or continuous), exposure, or third-variables. With these definitions, multiple third-variables can be considered simultaneously, and the indirect effects carried by individual third-variables can be separated from the total effect. In this paper, we extend the definitions of third-variable effects to multilevel data structures, where multilevel additive models are adapted to model the variable relationships. And then third-variable effects can be estimated at different levels. Moreover, transformations on variables are allowed to present nonlinear relationships among variables. We compile an R package mlma, to carry out the proposed multilevel third-variable analysis. Simulations show that the proposed method can effectively differentiate and estimate third-variable effects from different levels. Further, we implement the method to explore the racial disparity in body mass index accounting for both environmental and individual level risk factors. |
format | Online Article Text |
id | pubmed-7584256 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-75842562020-10-28 Third-variable effect analysis with multilevel additive models Yu, Qingzhao Li, Bin PLoS One Research Article Third-variable effect refers to the effect transmitted by third-variables that intervene in the relationship between an exposure and a response variable. Third-variable effect analysis has been broadly studied in many fields. However, it remains a challenge for researchers to differentiate indirect effect of individual factor from multiple third-variables, especially when the involving variables are of hierarchical structure. Yu et al. (2014) defined third-variable effects that were consistent for all different types of response (categorical or continuous), exposure, or third-variables. With these definitions, multiple third-variables can be considered simultaneously, and the indirect effects carried by individual third-variables can be separated from the total effect. In this paper, we extend the definitions of third-variable effects to multilevel data structures, where multilevel additive models are adapted to model the variable relationships. And then third-variable effects can be estimated at different levels. Moreover, transformations on variables are allowed to present nonlinear relationships among variables. We compile an R package mlma, to carry out the proposed multilevel third-variable analysis. Simulations show that the proposed method can effectively differentiate and estimate third-variable effects from different levels. Further, we implement the method to explore the racial disparity in body mass index accounting for both environmental and individual level risk factors. Public Library of Science 2020-10-23 /pmc/articles/PMC7584256/ /pubmed/33095796 http://dx.doi.org/10.1371/journal.pone.0241072 Text en © 2020 Yu, Li http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Yu, Qingzhao Li, Bin Third-variable effect analysis with multilevel additive models |
title | Third-variable effect analysis with multilevel additive models |
title_full | Third-variable effect analysis with multilevel additive models |
title_fullStr | Third-variable effect analysis with multilevel additive models |
title_full_unstemmed | Third-variable effect analysis with multilevel additive models |
title_short | Third-variable effect analysis with multilevel additive models |
title_sort | third-variable effect analysis with multilevel additive models |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7584256/ https://www.ncbi.nlm.nih.gov/pubmed/33095796 http://dx.doi.org/10.1371/journal.pone.0241072 |
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