ABSTRACT
Existing data from the Boston Naming Test were analyzed using standard statistical methods and with a General Processing Tree (GPT) model in an attempt to differentiate between patients with Alzheimer's Disease (AD) and Cerebrovascular dementia (CVD), matched for severity, and age-matched healthy controls. The GPT approach enables the estimation of parameters reflecting underlying cognitive processes (e.g., perceptual analysis, lexical access) based on categorical data. Compared to traditional analyses of proportion correct and of errors, the analysis with the GPT model was more sensitive in detecting differences between patient groups, as well as the source of these differences. Among the differences were two, evident in a comparison between very mild AD and very mild CVD patients: standard analyses did not reveal a significant difference between these groups; but the GPT analysis revealed that the CVD patients had significantly higher estimates than the AD patients on parameters reflecting lexical access and phonological realization.
Subject(s)
Alzheimer Disease/psychology , Cognition , Dementia, Vascular/psychology , Aged , Alzheimer Disease/diagnosis , Case-Control Studies , Data Interpretation, Statistical , Dementia, Vascular/diagnosis , Female , Humans , Male , Models, Statistical , Sensitivity and Specificity , Severity of Illness Index , Verbal LearningABSTRACT
Data from the immediate recall task of the Consortium to Establish a Registry for Alzheimer's Disease neuropsychological test battery were disaggregated into nine subject groups and analyzed with traditional statistics as well as with a general processing tree (GPT) model of free recall. The groups represented four levels of severity of Alzheimer's and vascular dementia, as well as a ninth group of healthy elderly controls. It was demonstrated that the patterns of success and failure of recall to individual items across successive trials contained much more information than the marginal trial-to-trial performance scores traditionally used in scoring the test. The GPT model analyzed recall performance in terms of three levels of item storage: unstored, intermediate, and long-term. Associated with the intermediate and long-term storage levels were respective retrieval parameters. Statistical methods enable one to estimate the parameters for each group, and the analyses revealed group differences in long-term storage that were not evident in a statistical analysis of the marginal trial-to-trial performance scores.