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Spatial analysis of cluster randomised trials: a systematic review of analysis methods
BACKGROUND: Cluster randomised trials (CRTs) often use geographical areas as the unit of randomisation, however explicit consideration of the location and spatial distribution of observations is rare. In many trials, the location of participants will have little importance, however in some, especial...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5609008/ https://www.ncbi.nlm.nih.gov/pubmed/28947911 http://dx.doi.org/10.1186/s12982-017-0066-2 |
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author | Jarvis, Christopher Di Tanna, Gian Luca Lewis, Daniel Alexander, Neal Edmunds, W. John |
author_facet | Jarvis, Christopher Di Tanna, Gian Luca Lewis, Daniel Alexander, Neal Edmunds, W. John |
author_sort | Jarvis, Christopher |
collection | PubMed |
description | BACKGROUND: Cluster randomised trials (CRTs) often use geographical areas as the unit of randomisation, however explicit consideration of the location and spatial distribution of observations is rare. In many trials, the location of participants will have little importance, however in some, especially against infectious diseases, spillover effects due to participants being located close together may affect trial results. This review aims to identify spatial analysis methods used in CRTs and improve understanding of the impact of spatial effects on trial results. METHODS: A systematic review of CRTs containing spatial methods, defined as a method that accounts for the structure, location, or relative distances between observations. We searched three sources: Ovid/Medline, Pubmed, and Web of Science databases. Spatial methods were categorised and details of the impact of spatial effects on trial results recorded. RESULTS: We identified ten papers which met the inclusion criteria, comprising thirteen trials. We found that existing approaches fell into two categories; spatial variables and spatial modelling. The spatial variable approach was most common and involved standard statistical analysis of distance measurements. Spatial modelling is a more sophisticated approach which incorporates the spatial structure of the data within a random effects model. Studies tended to demonstrate the importance of accounting for location and distribution of observations in estimating unbiased effects. CONCLUSIONS: There have been a few attempts to control and estimate spatial effects within the context of human CRTs, but our overall understanding is limited. Although spatial effects may bias trial results, their consideration was usually a supplementary, rather than primary analysis. Further work is required to evaluate and develop the spatial methodologies relevant to a range of CRTs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12982-017-0066-2) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5609008 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-56090082017-09-25 Spatial analysis of cluster randomised trials: a systematic review of analysis methods Jarvis, Christopher Di Tanna, Gian Luca Lewis, Daniel Alexander, Neal Edmunds, W. John Emerg Themes Epidemiol Review BACKGROUND: Cluster randomised trials (CRTs) often use geographical areas as the unit of randomisation, however explicit consideration of the location and spatial distribution of observations is rare. In many trials, the location of participants will have little importance, however in some, especially against infectious diseases, spillover effects due to participants being located close together may affect trial results. This review aims to identify spatial analysis methods used in CRTs and improve understanding of the impact of spatial effects on trial results. METHODS: A systematic review of CRTs containing spatial methods, defined as a method that accounts for the structure, location, or relative distances between observations. We searched three sources: Ovid/Medline, Pubmed, and Web of Science databases. Spatial methods were categorised and details of the impact of spatial effects on trial results recorded. RESULTS: We identified ten papers which met the inclusion criteria, comprising thirteen trials. We found that existing approaches fell into two categories; spatial variables and spatial modelling. The spatial variable approach was most common and involved standard statistical analysis of distance measurements. Spatial modelling is a more sophisticated approach which incorporates the spatial structure of the data within a random effects model. Studies tended to demonstrate the importance of accounting for location and distribution of observations in estimating unbiased effects. CONCLUSIONS: There have been a few attempts to control and estimate spatial effects within the context of human CRTs, but our overall understanding is limited. Although spatial effects may bias trial results, their consideration was usually a supplementary, rather than primary analysis. Further work is required to evaluate and develop the spatial methodologies relevant to a range of CRTs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12982-017-0066-2) contains supplementary material, which is available to authorized users. BioMed Central 2017-09-21 /pmc/articles/PMC5609008/ /pubmed/28947911 http://dx.doi.org/10.1186/s12982-017-0066-2 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 | Review Jarvis, Christopher Di Tanna, Gian Luca Lewis, Daniel Alexander, Neal Edmunds, W. John Spatial analysis of cluster randomised trials: a systematic review of analysis methods |
title | Spatial analysis of cluster randomised trials: a systematic review of analysis methods |
title_full | Spatial analysis of cluster randomised trials: a systematic review of analysis methods |
title_fullStr | Spatial analysis of cluster randomised trials: a systematic review of analysis methods |
title_full_unstemmed | Spatial analysis of cluster randomised trials: a systematic review of analysis methods |
title_short | Spatial analysis of cluster randomised trials: a systematic review of analysis methods |
title_sort | spatial analysis of cluster randomised trials: a systematic review of analysis methods |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5609008/ https://www.ncbi.nlm.nih.gov/pubmed/28947911 http://dx.doi.org/10.1186/s12982-017-0066-2 |
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