Purpose: The present work compares various methods for using baseline cognitive performance data to predict eventual cognitive status of longitudinal study participants at the University of Kentucky's Alzheimer's Disease Center. Methods: Cox proportional hazards models examined time to cognitive transition as predicted by risk strata derived from normal mixture modeling, latent class analysis, and a 1-SD thresholding approach. An additional comparator involved prediction directly from a numeric value for baseline cognitive performance. Results: A normal mixture model suggested 3 risk strata based on Consortium to Establish a Registry for Alzheimer's Disease (CERAD) T scores: high, intermediate, and low risk. Cox modeling of time to cognitive decline based on posterior probabilities for risk stratum membership yielded an estimated hazard ratio of 4.00 with 95% confidence interval 1.53-10.44 in comparing high risk membership to low risk; for intermediate risk membership versus low risk, the modeling yielded hazard ratio=2.29 and 95% confidence interval=0.98-5.33. Latent class analysis produced 3 groups, which did not have a clear ordering in terms of risk; however, one group exhibited appreciably greater hazard of cognitive decline. All methods for generating predictors of cognitive transition yielded statistically significant likelihood ratio statistics but modest concordance statistics. Conclusion: Posterior probabilities from mixture modeling allow for risk stratification that is data-driven and, in the case of CERAD T scores, modestly predictive of later cognitive decline. Incorporating other covariates may enhance predictions.
|Number of pages||9|
|Journal||Alzheimer Disease and Associated Disorders|
|State||Published - Dec 1 2021|
Bibliographical noteFunding Information:
The CERAD data was provided by the Sanders Brown Center on Aging at the University of Kentucky in association with the NIH-funded ADC grant under award number [P30 AG028383]. The authors thank Erin Abner, PhD and Richard Kryscio, PhD from the Sanders Brown Center for information and/or assistance. The authors thank 2 anonymous referees for their comments, which guided revision of the paper.
© 2021 Lippincott Williams and Wilkins. All rights reserved.
- Cox regression
- mixture model
- posterior probability
ASJC Scopus subject areas
- Clinical Psychology
- Geriatrics and Gerontology
- Psychiatry and Mental health