Home > Research . Search . Country . Browse . Small Grants

PSC In The News

RSS Feed icon

Frey comments on why sunbelt metro area economies are still struggling

Krause says having religious friends leads to gratitude, which is associated with better health

Work by Bailey and Dynarski on growing income gap in graduation rates cited in NYT

Highlights

Find an innovative research Cube at the MCubed Symposium, Oct 9, register now

Martha Bailey and Nicolas Duquette win Cole Prize for article on War on Poverty

Michigan's graduate sociology program tied for 4th with Stanford in USN&WR rankings

Jeff Morenoff makes Reuters' Highly Cited Researchers list for 2014

Next Brown Bag

Monday, Oct 6
Elisha Renne (Michigan)

Michael R. Elliott photo

Methods of Studying Variability as a Predictor of Health Status

a PSC Research Project

Investigators:   Michael R. Elliott, Sioban D. Harlow, Jessica Danielle 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 propose 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

Search . Browse