BACKGROUND/STUDY CONTEXT: The potential of cluster analysis (CA) as a baseline predictor of multivariate gerontologic outcomes over a long period of time has not been previously demonstrated.
Methods: Restricting candidate variables to a small group of established predictors of deleterious gerontologic outcomes, various CA methods were applied to baseline values from 754 nondisabled, community-living persons, aged 70 years or older. The best cluster solution yielded at baseline was subsequently used as a fixed explanatory variable in time-to-event models of the first occurrence of the following outcomes: any disability in four activities of daily living, any disability in four mobility measures, and death. Each outcome was recorded through a maximum of 129 months or death. Associations between baseline ordinal cluster level and first occurrence of all three outcomes were modeled over a 10-year period with proportional hazards regression and compared with the associations yielded by the analogous latent class analysis (LCA) solution.
Results: The final cluster-defining variables were continuous measures of cognitive status and depressive symptoms, and dichotomous indicators of slow gait and exhaustion. The best solution yielded by baseline values of these variables was obtained with a K-means algorithm and cosine similarity and consisted of three clusters representing increasing levels of impairment. After adjustment for age, sex, ethnic group, and number of chronic conditions, baseline ordinal cluster level demonstrated significantly positive associations with all three outcomes over a 10-year period that were equivalent to those from the corresponding LCA solution.
Conclusion: These findings suggest that baseline clusters based on previously established explanatory variables have potential to predict multivariate gerontologic outcomes over a long period of time.