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Sharp bounds on sufficient-cause interactions under the assumption of no redundancy

BACKGROUND: Sufficient-cause interaction is a type of interaction that has received much attention recently. The sufficient component cause model on which the sufficient-cause interaction is based is however a non-identifiable model. Estimating the interaction parameters from the model is mathematic...

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Autor principal: Lee, Wen-Chung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5399844/
https://www.ncbi.nlm.nih.gov/pubmed/28431517
http://dx.doi.org/10.1186/s12874-017-0348-y
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author Lee, Wen-Chung
author_facet Lee, Wen-Chung
author_sort Lee, Wen-Chung
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description BACKGROUND: Sufficient-cause interaction is a type of interaction that has received much attention recently. The sufficient component cause model on which the sufficient-cause interaction is based is however a non-identifiable model. Estimating the interaction parameters from the model is mathematically impossible. METHODS: In this paper, I derive bounding formulae for sufficient-cause interactions under the assumption of no redundancy. RESULTS: Two real data sets are used to demonstrate the method (R codes provided). The proposed bounds are sharp and sharper than previous bounds. CONCLUSIONS: Sufficient-cause interactions can be quantified by setting bounds on them. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12874-017-0348-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-53998442017-04-24 Sharp bounds on sufficient-cause interactions under the assumption of no redundancy Lee, Wen-Chung BMC Med Res Methodol Research Article BACKGROUND: Sufficient-cause interaction is a type of interaction that has received much attention recently. The sufficient component cause model on which the sufficient-cause interaction is based is however a non-identifiable model. Estimating the interaction parameters from the model is mathematically impossible. METHODS: In this paper, I derive bounding formulae for sufficient-cause interactions under the assumption of no redundancy. RESULTS: Two real data sets are used to demonstrate the method (R codes provided). The proposed bounds are sharp and sharper than previous bounds. CONCLUSIONS: Sufficient-cause interactions can be quantified by setting bounds on them. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12874-017-0348-y) contains supplementary material, which is available to authorized users. BioMed Central 2017-04-21 /pmc/articles/PMC5399844/ /pubmed/28431517 http://dx.doi.org/10.1186/s12874-017-0348-y Text en © The Author(s). 2017 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
Lee, Wen-Chung
Sharp bounds on sufficient-cause interactions under the assumption of no redundancy
title Sharp bounds on sufficient-cause interactions under the assumption of no redundancy
title_full Sharp bounds on sufficient-cause interactions under the assumption of no redundancy
title_fullStr Sharp bounds on sufficient-cause interactions under the assumption of no redundancy
title_full_unstemmed Sharp bounds on sufficient-cause interactions under the assumption of no redundancy
title_short Sharp bounds on sufficient-cause interactions under the assumption of no redundancy
title_sort sharp bounds on sufficient-cause interactions under the assumption of no redundancy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5399844/
https://www.ncbi.nlm.nih.gov/pubmed/28431517
http://dx.doi.org/10.1186/s12874-017-0348-y
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