Predicting dementia status from Mini-Mental State Exam scores using group-based trajectory modelling

Simple item page
dc.contributor.authorBrown, Cassandra Lynn
dc.contributor.supervisorPiccinin, Andrea Marie
dc.date.accessioned2012-08-24T18:29:26Z
dc.date.available2012-08-24T18:29:26Z
dc.date.copyright2012en_US
dc.date.issued2012-08-24
dc.degree.departmentDepartment of Psychology
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractBackground: Longitudinal studies enable the study of within person change over time in addition to between person differences. In longitudinal studies of older adult populations even when not the question of interest, identifying participants with dementia is desirable, and often necessary. Yet in practice, the time to collect information from each participant may be limited. Therefore, some studies include only a brief general cognitive measure of which the Mini Mental State Examination (MMSE) is the most commonly used (Raina et al., 2009). The current study explores whether group-based trajectory modeling of MMSE scores with a selection of covariates can identify individuals who have or will develop dementia in an 8 year longitudinal study. Methods: The sample included 651 individuals from the Origins of Variance in the Oldest Old study of Swedish twins 80 years old or older (OCTO-Twin). Participants had completed the MMSE every two years, and cases of dementia were diagnosed according to DSM-III criteria. The accuracy of using the classes formed in growth mixture modeling and latent class growth modeling as indicative of dementia status was compared to that of more standard methods, the typical 24/30 cut score and a logistic regression. Results: A three-class quadratic model with covariate effects on class membership was found to best characterize the data. The classes were characterized as High Performing Late Decline, Rapidly Declining, and Decreasing Low Performance, and were labeled as such. Comparing the diagnostic accuracy of the latent trajectory groups against simple methods; the sensitivity of the final model was lower but it was the same or superior in specificity, positive predictive value, negative predictive value, and allowed a more fine-grained analysis of participant risk. Conclusions: Group-based trajectory models may be helpful for grouping longitudinal study participants, particularly if sensitivity is not the primary concern.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/4164
dc.languageEnglisheng
dc.language.isoenen_US
dc.rights.tempAvailable to the World Wide Weben_US
dc.subjectDementiaen_US
dc.subjectGrowth mixture modelen_US
dc.titlePredicting dementia status from Mini-Mental State Exam scores using group-based trajectory modellingen_US
dc.typeThesisen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Brown_Cassandra_MSc_2012.pdf
Size:
545.7 KB
Format:
Adobe Portable Document Format
Description:
Main article
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.74 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Electronic Theses and Dissertations (ETD)
 
University of Victoria Libraries

We acknowledge and respect the Lək̓ʷəŋən (Songhees and Esquimalt) Peoples on whose territory the university stands, and the Lək̓ʷəŋən and W̱SÁNEĆ Peoples whose historical relationships with the land continue to this day.