A Comparison of Methods for Predicting Future Cognitive Status: Mixture Modeling, Latent Class Analysis, and Competitors

Frank Appiah, Richard J. Charnigo

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish
Pages (from-to)306-314
Number of pages9
JournalAlzheimer Disease and Associated Disorders
Issue number4
StatePublished - Dec 1 2021

Bibliographical note

Funding 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.

Publisher Copyright:
© 2021 Lippincott Williams and Wilkins. All rights reserved.


  • Cox regression
  • cognition
  • mixture model
  • posterior probability
  • transition

ASJC Scopus subject areas

  • Clinical Psychology
  • Gerontology
  • Geriatrics and Gerontology
  • Psychiatry and Mental health


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