Analysis of correlated data with feedback for time-dependent covariates in psychiatry research

In studies on psychiatry and neurodegenerative diseases, it is common to have data that are correlated due to the hierarchical structure in data collection or to repeated measures on the subject longitudinally. However, the feedback effect created due to time-dependent covariates in these studies is...

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Autores principales: Vazquez Arreola, Elsa, Wilson, Jeffrey R, Chen, Ding-Geng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2020
Materias:
Biostatistical Methods in Psychiatry
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7477964/
https://www.ncbi.nlm.nih.gov/pubmed/32914055
http://dx.doi.org/10.1136/gpsych-2020-100263
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author Vazquez Arreola, Elsa
Wilson, Jeffrey R
Chen, Ding-Geng
author_facet Vazquez Arreola, Elsa
Wilson, Jeffrey R
Chen, Ding-Geng
author_sort Vazquez Arreola, Elsa
collection PubMed
description In studies on psychiatry and neurodegenerative diseases, it is common to have data that are correlated due to the hierarchical structure in data collection or to repeated measures on the subject longitudinally. However, the feedback effect created due to time-dependent covariates in these studies is often overlooked and seldom modelled. This article reviews the methodological development of feedback effects with marginal models for longitudinal data and discusses their implementation.
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spelling pubmed-74779642020-09-09 Analysis of correlated data with feedback for time-dependent covariates in psychiatry research Vazquez Arreola, Elsa Wilson, Jeffrey R Chen, Ding-Geng Gen Psychiatr Biostatistical Methods in Psychiatry In studies on psychiatry and neurodegenerative diseases, it is common to have data that are correlated due to the hierarchical structure in data collection or to repeated measures on the subject longitudinally. However, the feedback effect created due to time-dependent covariates in these studies is often overlooked and seldom modelled. This article reviews the methodological development of feedback effects with marginal models for longitudinal data and discusses their implementation. BMJ Publishing Group 2020-09-07 /pmc/articles/PMC7477964/ /pubmed/32914055 http://dx.doi.org/10.1136/gpsych-2020-100263 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Biostatistical Methods in Psychiatry
Vazquez Arreola, Elsa
Wilson, Jeffrey R
Chen, Ding-Geng
Analysis of correlated data with feedback for time-dependent covariates in psychiatry research
title Analysis of correlated data with feedback for time-dependent covariates in psychiatry research
title_full Analysis of correlated data with feedback for time-dependent covariates in psychiatry research
title_fullStr Analysis of correlated data with feedback for time-dependent covariates in psychiatry research
title_full_unstemmed Analysis of correlated data with feedback for time-dependent covariates in psychiatry research
title_short Analysis of correlated data with feedback for time-dependent covariates in psychiatry research
title_sort analysis of correlated data with feedback for time-dependent covariates in psychiatry research
topic Biostatistical Methods in Psychiatry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7477964/
https://www.ncbi.nlm.nih.gov/pubmed/32914055
http://dx.doi.org/10.1136/gpsych-2020-100263
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