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. 2013 Aug 20;8(8):e71940.
doi: 10.1371/journal.pone.0071940. eCollection 2013.

The use of bayesian latent class cluster models to classify patterns of cognitive performance in healthy ageing

Patrício Soares Costa  1 , Nadine Correia Santos, Pedro Cunha, Joana Almeida Palha, Nuno Sousa
Affiliations

The use of bayesian latent class cluster models to classify patterns of cognitive performance in healthy ageing

Patrício Soares Costa et al. PLoS One. .

Abstract

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.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Latent classes performance in each cognitive dimension for each Bayesian LCA solution.
a 2-cluster (latent classes, LC1 to LC2), b 3-cluster (LC1 to LC3), c 4-cluster (LC1 to LC4).
Figure 2
Figure 2. Schematic representation of the latent class membership distribution across the 2-, 3- and 4-cluster LCA solutions based on cross tabulation analysis.
Frequency counts and percentages of subjects are reported for each latent class in each cluster solution.
Figure 3
Figure 3. Scatter plot and 3D plots of performance across cognitive dimensions for each latent class (LC1 to LC3) for the 3-cluster Bayesian LCA solution.
a scatter plot, b 3D plot according to latent class, and for each latent class 3D plots regarding c–e gender, f–h age and i–k school years.
See this image and copyright information in PMC

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Publication types

Grants and funding

The study is integrated in the “Maintaining health in old age through homeostasis (SWITCHBOX)” collaborative project funded by the European Commission FP7 initiative (grant HEALTH-F2-2010-259772). NS and JAP are main team members of the European consortium SWITCHBOX (http://www.switchbox-online.eu/). NCS is supported by a SwitchBox post-doctoral fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.