경증 알츠하이머병 증상패턴에 대한 베이지안 잠재계층모형 분석

초록

Certain behavioral and psychological symptoms of patients with mild Alzheimer's disease are interrelated, so that differences in symptom patterns among patients can cause other syndromes within Alzheimer's disease. The purpose of this study is to use the Bayesian latent class model to identify latent variables that affect the six symptoms observed in Alzheimer's disease patients. In addition, we try to confirm the weak-identifiability that appears while using the Bayesian latent class model. As a specific method, the Markov chain Monte Carlo method was utilized for parameter estimation, and the latent class identifiability display (LCID) and τ-measure method were used to confirm the problem of weak-identifiability. For model selection, we use the visual method, log dds ratio check (LORC) plot to determine how many latent classes are optimal.

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