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Methods of Studying Variability as a Predictor of Health Status

a PSC Research Project [ARCHIVE DISPLAY]

Investigators:   Michael R. Elliott, Sioban D. Harlow, Jessica Faul

The purpose of the proposed research is to develop methods to better understand how variability of health measures may be predictive of future health outcomes of interest. 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, at least in certain settings, the underlying variability in subject measures may also be important in predicting future health outcomes of interest. Hence we plan to develop methods that will better structure variability, decomposing it into short-term and long-term variance measures, and combining variance structures with mean structures such as mean longitudinal profile to more fully describe the information available in longitudinal datasets. In particular, we proposed methods to jointly model mean profile and variance in continuous longitudinal data, including methods that treat variance as heteroscedastic within individuals as well as between individuals. We also propose methods to jointly model short-term and long-term variance in continuous longitudinal data. We will apply these methods to the analysis of within-woman trends and variability in reproductive hormones and time between menstrual cycles to predict the progression of health outcomes through the transition to menopause, and to the analysis of within-person trends and variability in cognitive testing to predict cognitive decline and progression of dementia in older adults.

Funding Period: 09/15/2010 to 08/31/2012

This PSC Archive record is displayed for historical reference.

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