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Using joint models to disentangle intervention effect types and baseline confounding: an application within an intervention study in prodromal Alzheimer’s disease with Fortasyn Connect
BACKGROUND: Many prodromal Alzheimer’s disease trials collect two types of data: the time until clinical diagnosis of dementia and longitudinal patient information. These data are often analysed separately, although they are strongly associated. By combining the longitudinal and survival data into a...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6659198/ https://www.ncbi.nlm.nih.gov/pubmed/31345172 http://dx.doi.org/10.1186/s12874-019-0791-z |
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author | van Oudenhoven, Floor M. Swinkels, Sophie H.N. Hartmann, Tobias Soininen, Hilkka van Hees, Anneke M.J. Rizopoulos, Dimitris |
author_facet | van Oudenhoven, Floor M. Swinkels, Sophie H.N. Hartmann, Tobias Soininen, Hilkka van Hees, Anneke M.J. Rizopoulos, Dimitris |
author_sort | van Oudenhoven, Floor M. |
collection | PubMed |
description | BACKGROUND: Many prodromal Alzheimer’s disease trials collect two types of data: the time until clinical diagnosis of dementia and longitudinal patient information. These data are often analysed separately, although they are strongly associated. By combining the longitudinal and survival data into a single statistical model, joint models can account for the dependencies between the two types of data. METHODS: We illustrate the major steps in a joint modelling approach, motivated by data from a prodromal Alzheimer’s disease study: the LipiDiDiet trial. RESULTS: By using joint models we are able to disentangle baseline confounding from the intervention effect and moreover, to investigate the association between longitudinal patient information and the time until clinical dementia diagnosis. CONCLUSIONS: Joint models provide a valuable tool in the statistical analysis of clinical studies with longitudinal and survival data, such as in prodromal Alzheimer’s disease trials, and have several added values compared to separate analyses. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-019-0791-z) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6659198 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-66591982019-08-01 Using joint models to disentangle intervention effect types and baseline confounding: an application within an intervention study in prodromal Alzheimer’s disease with Fortasyn Connect van Oudenhoven, Floor M. Swinkels, Sophie H.N. Hartmann, Tobias Soininen, Hilkka van Hees, Anneke M.J. Rizopoulos, Dimitris BMC Med Res Methodol Research Article BACKGROUND: Many prodromal Alzheimer’s disease trials collect two types of data: the time until clinical diagnosis of dementia and longitudinal patient information. These data are often analysed separately, although they are strongly associated. By combining the longitudinal and survival data into a single statistical model, joint models can account for the dependencies between the two types of data. METHODS: We illustrate the major steps in a joint modelling approach, motivated by data from a prodromal Alzheimer’s disease study: the LipiDiDiet trial. RESULTS: By using joint models we are able to disentangle baseline confounding from the intervention effect and moreover, to investigate the association between longitudinal patient information and the time until clinical dementia diagnosis. CONCLUSIONS: Joint models provide a valuable tool in the statistical analysis of clinical studies with longitudinal and survival data, such as in prodromal Alzheimer’s disease trials, and have several added values compared to separate analyses. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-019-0791-z) contains supplementary material, which is available to authorized users. BioMed Central 2019-07-25 /pmc/articles/PMC6659198/ /pubmed/31345172 http://dx.doi.org/10.1186/s12874-019-0791-z Text en © The Author(s) 2019 Open Access This 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 van Oudenhoven, Floor M. Swinkels, Sophie H.N. Hartmann, Tobias Soininen, Hilkka van Hees, Anneke M.J. Rizopoulos, Dimitris Using joint models to disentangle intervention effect types and baseline confounding: an application within an intervention study in prodromal Alzheimer’s disease with Fortasyn Connect |
title | Using joint models to disentangle intervention effect types and baseline confounding: an application within an intervention study in prodromal Alzheimer’s disease with Fortasyn Connect |
title_full | Using joint models to disentangle intervention effect types and baseline confounding: an application within an intervention study in prodromal Alzheimer’s disease with Fortasyn Connect |
title_fullStr | Using joint models to disentangle intervention effect types and baseline confounding: an application within an intervention study in prodromal Alzheimer’s disease with Fortasyn Connect |
title_full_unstemmed | Using joint models to disentangle intervention effect types and baseline confounding: an application within an intervention study in prodromal Alzheimer’s disease with Fortasyn Connect |
title_short | Using joint models to disentangle intervention effect types and baseline confounding: an application within an intervention study in prodromal Alzheimer’s disease with Fortasyn Connect |
title_sort | using joint models to disentangle intervention effect types and baseline confounding: an application within an intervention study in prodromal alzheimer’s disease with fortasyn connect |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6659198/ https://www.ncbi.nlm.nih.gov/pubmed/31345172 http://dx.doi.org/10.1186/s12874-019-0791-z |
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