Cargando…

A training manual for event history analysis using longitudinal data

OBJECTIVE: This research note reports on the activities of the Multi-centre Analysis of the Dynamics of Internal Migration And Health (MADIMAH) project aimed at collating and testing of a set of tools to conduct longitudinal event history analyses applied to standardised Health and Demographic Surve...

Descripción completa

Detalles Bibliográficos
Autores principales: Bocquier, Philippe, Ginsburg, Carren, Collinson, Mark A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6694584/
https://www.ncbi.nlm.nih.gov/pubmed/31412914
http://dx.doi.org/10.1186/s13104-019-4544-1
_version_ 1783443855270477824
author Bocquier, Philippe
Ginsburg, Carren
Collinson, Mark A.
author_facet Bocquier, Philippe
Ginsburg, Carren
Collinson, Mark A.
author_sort Bocquier, Philippe
collection PubMed
description OBJECTIVE: This research note reports on the activities of the Multi-centre Analysis of the Dynamics of Internal Migration And Health (MADIMAH) project aimed at collating and testing of a set of tools to conduct longitudinal event history analyses applied to standardised Health and Demographic Surveillance System (HDSS) datasets. The methods are illustrated using an example of longitudinal micro-data from the Agincourt HDSS, one of a number of open access datasets available through the INDEPTH iShare2 data repository. The research note documents the experience of the MADIMAH group in analysing HDSS data and demonstrates how complex analyses can be streamlined and conducted in an accessible way. These tools are aimed at aiding analysts and researchers wishing to conduct longitudinal data analysis of demographic events. RESULTS: The methods demonstrated in this research note may successfully be applied by practitioners to longitudinal micro-data from HDSS, as well as retrospective surveys or register data. The illustrations provided are accompanied by detailed, tested computer programs, which demonstrate the full potential of longitudinal data to generate both cross-sectional and longitudinal standard descriptive estimates as well as more complex regression estimates.
format Online
Article
Text
id pubmed-6694584
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-66945842019-08-19 A training manual for event history analysis using longitudinal data Bocquier, Philippe Ginsburg, Carren Collinson, Mark A. BMC Res Notes Research Note OBJECTIVE: This research note reports on the activities of the Multi-centre Analysis of the Dynamics of Internal Migration And Health (MADIMAH) project aimed at collating and testing of a set of tools to conduct longitudinal event history analyses applied to standardised Health and Demographic Surveillance System (HDSS) datasets. The methods are illustrated using an example of longitudinal micro-data from the Agincourt HDSS, one of a number of open access datasets available through the INDEPTH iShare2 data repository. The research note documents the experience of the MADIMAH group in analysing HDSS data and demonstrates how complex analyses can be streamlined and conducted in an accessible way. These tools are aimed at aiding analysts and researchers wishing to conduct longitudinal data analysis of demographic events. RESULTS: The methods demonstrated in this research note may successfully be applied by practitioners to longitudinal micro-data from HDSS, as well as retrospective surveys or register data. The illustrations provided are accompanied by detailed, tested computer programs, which demonstrate the full potential of longitudinal data to generate both cross-sectional and longitudinal standard descriptive estimates as well as more complex regression estimates. BioMed Central 2019-08-14 /pmc/articles/PMC6694584/ /pubmed/31412914 http://dx.doi.org/10.1186/s13104-019-4544-1 Text en © The Author(s) 2019 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 Note
Bocquier, Philippe
Ginsburg, Carren
Collinson, Mark A.
A training manual for event history analysis using longitudinal data
title A training manual for event history analysis using longitudinal data
title_full A training manual for event history analysis using longitudinal data
title_fullStr A training manual for event history analysis using longitudinal data
title_full_unstemmed A training manual for event history analysis using longitudinal data
title_short A training manual for event history analysis using longitudinal data
title_sort training manual for event history analysis using longitudinal data
topic Research Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6694584/
https://www.ncbi.nlm.nih.gov/pubmed/31412914
http://dx.doi.org/10.1186/s13104-019-4544-1
work_keys_str_mv AT bocquierphilippe atrainingmanualforeventhistoryanalysisusinglongitudinaldata
AT ginsburgcarren atrainingmanualforeventhistoryanalysisusinglongitudinaldata
AT collinsonmarka atrainingmanualforeventhistoryanalysisusinglongitudinaldata
AT bocquierphilippe trainingmanualforeventhistoryanalysisusinglongitudinaldata
AT ginsburgcarren trainingmanualforeventhistoryanalysisusinglongitudinaldata
AT collinsonmarka trainingmanualforeventhistoryanalysisusinglongitudinaldata