Longitudinal partially ordered data analysis for preclinical sarcopenia.
Health ABC
aging
longitudinal analysis
muscle mass
poset
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
30 10 2020
30 10 2020
Historique:
received:
29
06
2019
revised:
24
05
2020
accepted:
27
05
2020
pubmed:
12
7
2020
medline:
22
6
2021
entrez:
12
7
2020
Statut:
ppublish
Résumé
Sarcopenia is a geriatric syndrome characterized by significant loss of muscle mass. Based on a commonly used definition of the condition that involves three measurements, different subclinical and clinical states of sarcopenia are formed. These states constitute a partially ordered set (poset). This article focuses on the analysis of longitudinal poset in the context of sarcopenia. We propose an extension of the generalized linear mixed model and a recoding scheme for poset analysis such that two submodels-one for ordered categories and one for nominal categories-that include common random effects can be jointly estimated. The new poset model postulates random effects conceptualized as latent variables that represent an underlying construct of interest, that is, susceptibility to sarcopenia over time. We demonstrate how information can be gleaned from nominal sarcopenic states for strengthening statistical inference on a person's susceptibility to sarcopenia.
Identifiants
pubmed: 32652653
doi: 10.1002/sim.8667
pmc: PMC8386024
mid: NIHMS1729637
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
3313-3328Subventions
Organisme : Foundation for the National Institutes of Health
ID : P30AG21332
Organisme : NIA NIH HHS
ID : U24 AG059624
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001420
Pays : United States
Organisme : Foundation for the National Institutes of Health
ID : 1UL1TR001420-01
Organisme : NIA NIH HHS
ID : P30 AG021332
Pays : United States
Informations de copyright
© 2020 John Wiley & Sons, Ltd.
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