The Truth behind the Zeros
A New Approach to Principal Component Analysis of the Neuropsychiatric Inventory
Bibliographic Data
| ID | 19290643 |
|---|---|
| Authors | Kristoffer H Hellton (0000-0002-4779-486X, Norwegian Computing Center, Oslo, Norway, corresponding author), Jason L Cummings (0000-0001-8944-4158, Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, Nevada, USA), Audun Osland Vik-Mo (0000-0002-5162-3035, Stavanger University Hospital), Jan Erik Nordrehaug (0000-0002-8720-4692, University of Bergen), Dag Aarsland (0000-0001-6314-216X, Stavanger University Hospital), Geir Selbaek (0000-0001-6511-8219, Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Toensberg, Norway), Lasse M Giil (0000-0003-3520-7530, University of Bergen) |
| Year | 2021 |
| Volume | 56 |
| Issue | 1 |
| Pages | 70-85 |
| Publication date | 2021-01-02 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Multivariate Behavioral Research (JOURNAL) |
| Journal identifiers | ISSN: 0027-3171 • E-ISSN: 1532-7906 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00273171.2020.1736976 |
| PMID | 32329370 |
| OpenAlex | W3018226047 |
| Language | EN |
| References cited | 48 |
Psychiatric syndromes in dementia are often derived from the Neuropsychiatric Inventory (NPI) using principal component analysis (PCA). The validity of this statistical approach can be questioned, since the excessive proportion of zeros and skewness of NPI items may distort the estimated relations between the items. We propose a novel version of PCA, ZIBP-PCA, where a zero-inflated bivariate Poisson (ZIBP) distribution models the pairwise covariance between the NPI items. We compared the performance of the method to classical PCA under zero-inflation using simulations, and in two dementia-cohorts (N = 830, N = 1349). Simulations showed that component loadings from PCA were biased due to zero-inflation, while the loadings of ZIBP-PCA remained unaffected. ZIBP-PCA obtained a simpler component structure of "psychosis," "mood" and "agitation" in both dementia-cohorts, compared to PCA. The principal components from ZIBP-PCA had component loadings as follows: First, the component interpreted as "psychosis" was loaded by the items delusions and hallucinations. Second, the "mood" component was loaded by depression and anxiety. Finally, the "agitation" component was loaded by irritability and aggression. In conclusion, PCA is not equipped to handle zero-inflation. Using the NPI, PCA fails to identify components with a valid interpretation, while ZIBP-PCA estimates simple and interpretable components to characterize the psychopathology of dementia
Anxiety · Cognitive psychology · Dementia · Econometrics · Irritability · Mood · Pattern recognition (psychology) · Principal component analysis · Psychiatry · Psychosis · Statistics · Dementia and Cognitive Impairment Research · Internal Medicine · Mathematics · Medicine · Mental Health Research Topics · Psychology
Diagnostic and Statistical Manual of Mental Disorders
The Neuropsychiatric Inventory
Construct Validity
Analysis of a complex of statistical variables into principal components.
A Rationale and Test for the Number of Factors in Factor Analysis
Estimating the Dimension of a Model
Sample size in factor analysis.
The Application of Electronic Computers to Factor Analysis
Factors Influencing Four Rules For Determining The Number Of Components To Retain
On the Equivalence of Factors and Components
Component Analysis versus Common Factor Analysis
A Comparative Investigation of Rotation Criteria Within Exploratory Factor Analysis
Common Factor Analysis Versus Principal Component Analysis
Multiple factor analysis
The Scree Test For The Number Of Factors
| Highly cited | No |
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