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Longitudinal partially ordered data analysis for preclinical sarcopenia
Author(s) -
Ip Edward H.,
Chen ShyhHuei,
BandeenRoche Karen,
Speiser Jaime L.,
Cai Li,
Houston Denise K.
Publication year - 2020
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8667
Subject(s) - sarcopenia , partially ordered set , context (archaeology) , inference , set (abstract data type) , random effects model , construct (python library) , subclinical infection , mathematics , computer science , algorithm , medicine , combinatorics , artificial intelligence , paleontology , meta analysis , biology , programming language
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.