Full-information models for multiple psychometric tests: annualized rates of change in normal aging and dementia.

The rates of change for five widely used psychometric tests were analyzed to compare how much more variance reduction can be achieved using full-information methods relative to the single-equation methods previously used in dementia research. Nondemented controls and subjects with Alzheimer disease (AD), probable/ possible vascular dementia (VD), or mixed dementia (MD) were evaluated. A cohort design was followed, with follow-up of three demented groups and one normal control group; data were analyzed in a multiple-equation regression model estimated with full-information methods. The study was conducted at Alzheimer's Disease Research Center sites at the University of California, Irvine, and at the University of Southern California. In all, 226 patients and controls who had completed initial assessment and at least one annual reassessment were included in the study. Dependent variables were annualized rates of change in the Mini-Mental State Examination (MMSE), the Short-Blessed Dementia Rating Scale (DRS), the Consortium to Establish a Registry for Alzheimer's Disease drawings test (CD), the WAIS-R Block Design test (WRB), and the Boston Naming Test (BNT). Independent variables were dementia severity, diagnosis (AD, VD, MD, or control), sex, age, marital status, education, and age at onset. Full-information methods reduced the variance in the change scores by > or = 25% compared with previous studies. The model's prediction of a test's rate of change was almost entirely due to dementia stage and diagnosis. The effects of other explanatory variables (sex, marital status, age, and education) were weak and statistically insignificant. When the effects of other independent variables were controlled, AD and MD patients were found to decline at significantly faster rates than VD patients. Full-information methods, relative to single-equation methods, substantially reduce the variance of rates of change for multiple psychometric tests. They do so by simultaneously considering the correlated error terms in the regression for each dependent psychometric change score variable. The robustness of these results to minor variations in follow-up time suggests that annualization is a reasonably valid procedure for making change scores comparable. This study's results suggest that change scores in psychometric tests provide information that can be used to aid differential diagnosis. However, the large variances of change scores preclude many other uses. Finally, since standardization of psychometric change scores translates all tests to the same scale (0-100%), standardized change scores are easier to interpret. The analysis of standardized change scores deserves further investigation.