Many statistical methods have been developed that treat within-subject correlation that accompanies the clustering of subjects in longitudinal data settings as a nuisance parameter, with the focus of analytic interest being on mean outcome or profiles over time. However, there is evidence that in certain settings, underlying variability in subject measures may also be important in predicting future health outcomes of interest. Here, we develop a method for combining information from mean profiles and residual variance to assess associations with categorical outcomes in a joint modeling framework. We consider an application to relating word recall measures obtained over time to dementia onset from the Health and Retirement Survey. Copyright © 2012 John Wiley & Sons, Ltd.