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.

中文翻译：

---

临床前肌肉减少症的纵向部分有序数据分析。

肌肉减少症是一种以肌肉质量显着减少为特征的老年综合征。基于涉及三种测量的病症的常用定义，形成了肌肉减少症的不同亚临床和临床状态。这些状态构成了一个偏序集（poset）。本文重点分析肌肉减少症背景下的纵向偏位。我们提出了广义线性混合模型的扩展和用于偏序分析的重新编码方案，以便可以联合估计包含常见随机效应的两个子模型——一个用于有序类别，另一个用于名义类别。新的poset模型假设随机效应被概念化为潜在变量，这些潜在变量代表了潜在的兴趣结构，即随着时间的推移对肌肉减少症的易感性。