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The Use of Bayesian Latent Class Cluster Models to Classify Patterns of Cognitive Performance in Healthy Ageing

机译：贝叶斯潜在类别聚类模型对健康老龄化认知表现模式的分类

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The main focus of this study is to illustrate the applicability of latent class analysis in the assessment of cognitive performance profiles during ageing. Principal component analysis (PCA) was used to detect main cognitive dimensions (based on the neurocognitive test variables) and Bayesian latent class analysis (LCA) models (without constraints) were used to explore patterns of cognitive performance among community-dwelling older individuals. Gender, age and number of school years were explored as variables. Three cognitive dimensions were identified: general cognition (MMSE), memory (MEM) and executive (EXEC) function. Based on these, three latent classes of cognitive performance profiles (LC1 to LC3) were identified among the older adults. These classes corresponded to stronger to weaker performance patterns (LC1>LC2>LC3) across all dimensions; each latent class denoted the same hierarchy in the proportion of males, age and number of school years. Bayesian LCA provided a powerful tool to explore cognitive typologies among healthy cognitive agers.
机译：这项研究的主要重点是说明潜在类别分析在老龄化过程中对认知表现的评估中的适用性。主成分分析（PCA）用于检测主要认知维度（基于神经认知测试变量），贝叶斯潜在类别分析（LCA）模型（无约束）用于探讨社区居住的老年人的认知表现模式。探索了性别，年龄和学年数作为变量。确定了三个认知维度：一般认知（MMSE），记忆（MEM）和执行（EXEC）功能。基于这些，在老年人中确定了三种潜在的认知表现谱（LC1至LC3）。这些类别对应于所有维度上从强到弱的性能模式（LC1> LC2> LC3）。每个潜在的班级在男性比例，年龄和学年数上都表示相同的等级。贝叶斯LCA提供了一个强大的工具，可以探索健康的认知年龄人群的认知类型。

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