Longitudinal partially ordered data analysis for preclinical sarcopenia

Edward H. Ip, Shyh Huei Chen, Karen Bandeen-Roche, Jaime L. Speiser, Li Cai, Denise K. Houston

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
JournalStatistics in Medicine
DOIs
StateAccepted/In press - 2020

Keywords

  • aging
  • Health ABC
  • longitudinal analysis
  • muscle mass
  • poset

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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