PDF(Pubmed)
Abstract：
UNASSIGNED: The term Behavioral and Psychological Symptoms of Dementia (BPSD) covers a group of phenomenologically and medically distinct symptoms that rarely occur in isolation. Their therapy represents a major unmet medical need across dementias of different types, including Alzheimer\'s disease. Understanding of the symptom occurrence and their clusterization can inform clinical drug development and use of existing and future BPSD treatments.
UNASSIGNED: The primary aim of the present study was to investigate the ability of a commonly used principal component analysis to identify BPSD patterns as assessed by Neuropsychiatric Inventory (NPI).
UNASSIGNED: NPI scores from the Aging, Demographics, and Memory Study (ADAMS) were used to characterize reported occurrence of individual symptoms and their combinations. Based on this information, we have designed and conducted a simulation experiment to compare Principal Component analysis (PCA) and zero-inflated PCA (ZI PCA) by their ability to reveal true symptom associations.
UNASSIGNED: Exploratory analysis of the ADAMS database revealed overlapping multivariate distributions of NPI symptom scores. Simulation experiments have indicated that PCA and ZI PCA cannot handle data with multiple overlapping patterns. Although the principal component analysis approach is commonly applied to NPI scores, it is at risk to reveal BPSD clusters that are a statistical phenomenon rather than symptom associations occurring in clinical practice.
UNASSIGNED: We recommend the thorough characterization of multivariate distributions before subjecting any dataset to Principal Component Analysis.
UNASSIGNED: The primary aim of the present study was to investigate the ability of a commonly used principal component analysis to identify BPSD patterns as assessed by Neuropsychiatric Inventory (NPI).
UNASSIGNED: NPI scores from the Aging, Demographics, and Memory Study (ADAMS) were used to characterize reported occurrence of individual symptoms and their combinations. Based on this information, we have designed and conducted a simulation experiment to compare Principal Component analysis (PCA) and zero-inflated PCA (ZI PCA) by their ability to reveal true symptom associations.
UNASSIGNED: Exploratory analysis of the ADAMS database revealed overlapping multivariate distributions of NPI symptom scores. Simulation experiments have indicated that PCA and ZI PCA cannot handle data with multiple overlapping patterns. Although the principal component analysis approach is commonly applied to NPI scores, it is at risk to reveal BPSD clusters that are a statistical phenomenon rather than symptom associations occurring in clinical practice.
UNASSIGNED: We recommend the thorough characterization of multivariate distributions before subjecting any dataset to Principal Component Analysis.
摘要：
■痴呆的行为和心理症状（BPSD）一词涵盖了一组在现象学和医学上不同的症状，很少单独发生。他们的治疗代表了不同类型痴呆症的主要未满足的医疗需求，包括老年痴呆症.了解症状发生及其聚类可以为临床药物开发和使用现有和未来的BPSD治疗提供信息。
■本研究的主要目的是调查通过神经精神量表（NPI）评估的常用主成分分析识别BPSD模式的能力。
■来自老龄化的NPI分数，人口统计,和记忆研究（ADAMS）用于表征报告的单个症状及其组合的发生。根据这些信息,我们设计并进行了一项模拟实验，以比较主成分分析（PCA）和零膨胀PCA（ZIPCA）揭示真实症状关联的能力。
■对ADAMS数据库的探索性分析显示NPI症状评分的多变量分布重叠。仿真实验表明，PCA和ZIPCA无法处理具有多个重叠模式的数据。尽管主成分分析方法通常应用于NPI分数，提示BPSD聚类是一种统计现象,而不是临床实践中出现的症状关联,存在风险.
■我们建议在对任何数据集进行主成分分析之前对多变量分布进行彻底表征。
■本研究的主要目的是调查通过神经精神量表（NPI）评估的常用主成分分析识别BPSD模式的能力。
■来自老龄化的NPI分数，人口统计,和记忆研究（ADAMS）用于表征报告的单个症状及其组合的发生。根据这些信息,我们设计并进行了一项模拟实验，以比较主成分分析（PCA）和零膨胀PCA（ZIPCA）揭示真实症状关联的能力。
■对ADAMS数据库的探索性分析显示NPI症状评分的多变量分布重叠。仿真实验表明，PCA和ZIPCA无法处理具有多个重叠模式的数据。尽管主成分分析方法通常应用于NPI分数，提示BPSD聚类是一种统计现象,而不是临床实践中出现的症状关联,存在风险.
■我们建议在对任何数据集进行主成分分析之前对多变量分布进行彻底表征。
