| Title |
The Use of Bayesian Latent Class Cluster Models to Classify Patterns of Cognitive Performance in Healthy Ageing
|
|---|---|
| Published in |
PLOS ONE, August 2013
|
| DOI | 10.1371/journal.pone.0071940 |
| Pubmed ID | |
| Authors |
Patrício Soares Costa, Nadine Correia Santos, Pedro Cunha, Joana Almeida Palha, Nuno Sousa |
| 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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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 1% |
| Belgium | 1 | 1% |
| Unknown | 74 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Doctoral Student | 12 | 16% |
| Student > Master | 11 | 14% |
| Researcher | 10 | 13% |
| Student > Ph. D. Student | 10 | 13% |
| Professor | 5 | 7% |
| Other | 14 | 18% |
| Unknown | 14 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 19 | 25% |
| Medicine and Dentistry | 10 | 13% |
| Neuroscience | 6 | 8% |
| Agricultural and Biological Sciences | 5 | 7% |
| Social Sciences | 4 | 5% |
| Other | 15 | 20% |
| Unknown | 17 | 22% |