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

ABSTRACT

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.