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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Published inGeneral psychiatry Vol. 33; no. 5; p. e100263
Main Authors Vazquez Arreola, Elsa, Wilson, Jeffrey R, Chen, Ding-Geng
Format Journal Article
LanguageEnglish
Published England BMJ Publishing Group LTD 07.09.2020
BMJ Publishing Group
Subjects
Biostatistical Methods in Psychiatry
Estimates
Feedback
Longitudinal studies
Mental disorders
Mental health
Population
Psychiatry
Quality of life
Regression analysis
Social support
biostatistics
models, statistical
statistics as topic
longitudinal studies
Online AccessGet full text
ISSN2517-729X
2096-5923
2517-729X
DOI10.1136/gpsych-2020-100263

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Abstract 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.
AbstractList 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.
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.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.
Author Wilson, Jeffrey R
Vazquez Arreola, Elsa
Chen, Ding-Geng
AuthorAffiliation 1 School of Mathematical and Statistical Sciences , Arizona State University , Tempe , Arizona , USA
2 Department of Economics, W.P. Carey School of Business , Arizona State University , Tempe , Arizona , USA
3 School of Social Work and Department of Biostatistics , University of North Carolina at Chapel Hill , Chapel Hill , North Carolina , USA
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– name: 3 School of Social Work and Department of Biostatistics , University of North Carolina at Chapel Hill , Chapel Hill , North Carolina , USA
– name: 2 Department of Economics, W.P. Carey School of Business , Arizona State University , Tempe , Arizona , USA
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10.1002/jcop.20171
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Issue 5
Keywords biostatistics
models, statistical
statistics as topic
longitudinal studies
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SubjectTerms Biostatistical Methods in Psychiatry
Estimates
Feedback
Longitudinal studies
Mental disorders
Mental health
Population
Psychiatry
Quality of life
Regression analysis
Social support
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Title Analysis of correlated data with feedback for time-dependent covariates in psychiatry research
URI https://gpsych.bmj.com/content/33/5/e100263.full
https://www.ncbi.nlm.nih.gov/pubmed/32914055
https://www.proquest.com/docview/2887955386
https://www.proquest.com/docview/2441614380
https://pubmed.ncbi.nlm.nih.gov/PMC7477964
https://doaj.org/article/30c657a17aa04ea0a6f4ee9a709220c8
Volume 33
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