Using Mixture Modelling to Construct Subgroups of Cognitive Aging in the Wisconsin Longitudinal Study

Principal Investigator(s):  View help for Principal Investigator(s) Sara M. Moorman, Boston College; Emily A. Greenfield, Rutgers, the State University of New Jersey; Kyle A. Carr, Boston College

Version:  View help for Version V1

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Project Citation: 

Moorman, Sara M., Greenfield, Emily A. , and Carr, Kyle A. . Using Mixture Modelling to Construct Subgroups of Cognitive Aging in the Wisconsin Longitudinal Study. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-11-15. https://doi.org/10.3886/E126561V1

Project Description

Summary:  View help for Summary Objectives: Longitudinal surveys of older adults increasingly incorporate assessments of cognitive performance. However, very few studies have used mixture modelling techniques to describe cognitive aging, identifying subgroups of people who display similar patterns of performance across discrete cognitive functions. We employ this approach to advance empirical evidence concerning inter-individual variability and intraindividual change in patterns of cognitive aging.

Method: We drew upon data from 3,713 participants in the Wisconsin Longitudinal Study (WLS). We used latent class analysis to generate subgroups of cognitive aging based on assessments of verbal fluency and episodic memory at ages 65 and 72. We also employed latent transition analysis to identify how individual participants moved between subgroups over the 7-year period.

Results: There were four subgroups at each point in time. Approximately three-quarters of the sample demonstrated continuity in the qualitative type of profile between ages 65 and 72, with 17.9% of the sample in a profile with sustained overall low performance at both ages 65 and 72. An additional 18.7% of participants made subgroup transitions indicating marked decline in episodic memory.

Discussion: Results demonstrate the utility of using mixture modelling to identify qualitatively and quantitatively distinct subgroups of cognitive aging among older adults. We discuss the implications of these results for the continued use of population health data to advance research on cognitive aging.
Funding Sources:  View help for Funding Sources National Institutes of Health (NIA R01 AG 057491)

Scope of Project

Subject Terms:  View help for Subject Terms Alzheimers disease; dementia; mental processes; Wisconsin; episodic memory; older adult; cognitive ability; verbal fluency; population health; cognitive aging; latent class analysis
Data Type(s):  View help for Data Type(s) survey data


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V1 [2020-11-15]

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