Intersectional approaches to cognitive aging: a practical guide to modeling heterogeneous trajectories with
Jeongwon Choi1, Belinda Homer2, Sunmee Kim2
1Department of Psychology and Human Development, Vanderbilt University, Nashville, TN, United States.
Frontiers in Psychology
|July 31, 2026
Summary
This study introduces Generalized Linear Mixed Model Trees (GLMM-trees) for analyzing cognitive aging. This method identifies diverse aging patterns across intersecting social identities, offering new insights into heterogeneity.
Area of Science:
- Gerontology
- Sociology
- Biostatistics
Background:
- Understanding cognitive aging requires analyzing diverse subgroups and their unique trajectories.
- Traditional longitudinal models struggle to capture complex interactions between social identities.
- An intersectional lens is crucial for a comprehensive view of cognitive aging heterogeneity.
Purpose of the Study:
- To present a guide for applying Generalized Linear Mixed Model Trees (GLMM-trees) for cognitive aging research.
- To demonstrate how GLMM-trees can uncover subgroup-specific cognitive aging patterns.
- To enable data-driven detection of heterogeneity influenced by intersecting sociodemographic factors.
Main Methods:
- Application of Generalized Linear Mixed Model Trees (GLMM-trees), a recursive partitioning method.
- Integration of mixed-effects modeling with decision-tree algorithms.
- Utilizing longitudinal data from the U.S. Health and Retirement Study (HRS) across five waves.
Main Results:
- GLMM-trees successfully identified previously unobserved subgroups with distinct cognitive aging patterns.
- These subgroups were defined by intersecting factors like education, race, gender, and income.
- The method revealed differential influences of these subgroups on baseline cognition and cognitive change over time.
Conclusions:
- GLMM-trees provide a flexible and powerful tool for analyzing complex longitudinal data.
- This method facilitates the application of intersectional frameworks in aging research.
- GLMM-trees enable the discovery of unexpected heterogeneity in cognitive aging trajectories.
Keywords:
cognitive aginggeneralized linear mixed model trees (GLMM-trees)health and retirement study (HRS)intersectionalitylongitudinal analysisrecursive partitioningsocial determinants of healthsubgroup detectionMore Related Videos
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