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Separation of Longitudinal Change from Re-Test Effect using a Multiple-Group Latent Growth Model

Separation of Longitudinal Change from Re-Test Effect using a Multiple-Group Latent Growth Model. Richard N. Jones , John N. Morris, Adrienne N. Rosenberg, Research and Training Institute, Hebrew Rehabilitation Center for Aged, Research and Training Institute, Boston MA .

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Separation of Longitudinal Change from Re-Test Effect using a Multiple-Group Latent Growth Model

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  1. Separation of Longitudinal Change from Re-Test Effect using a Multiple-Group Latent Growth Model Richard N. Jones, John N. Morris, Adrienne N. Rosenberg, Research and Training Institute, Hebrew Rehabilitation Center for Aged, Research and Training Institute, Boston MA Data acquisition and research supported by the NIA and NINR

  2. Objective • Describe a commonly occurring challenge in longitudinal studies of cognitive aging: the re-test effect • Present a general latent variable modeling framework for statistically separating aging and re-test effects • Demonstrate the modeling approach in real data (ACTIVE Cognitive intervention study)

  3. Hypothesized Longitudinal Course

  4. Hypothesized and Observed Longitudinal Course

  5. Bias in Estimate of Baseline Level and Change

  6. Hypothesized Longitudinal Course

  7. Latent Growth Model

  8. Latent Growth Curve Model for Linear Change

  9. Hypothesized Longitudinal Course

  10. Latent Growth Curve Model for Linear Changewith second intercept (learning factor)

  11. Adding Background and Explanatory Variables

  12. Example: ACTIVE • Advanced Cognitive Training for Vital and Independent Elderly • Six sites (AL, IN, MA, MI, MD, PA) • Random assignment to one of four intervention arms, 4-group pre-post design • Speed of Processing, Memory, Logical Reasoning, No Training Control • Healthy older adults (n=2,428) aged 65-83

  13. Outcome Measure • Speed of Processing Composite • Ball, et al. Jama, 2002; 288:2271-81. • Regression-method factor score for multiple speeded tests • Based on minimum stimulus duration at which participants could identify and localize information with 75% accuracy, under different cognitive demand conditions • Lower is better (faster speed of processing)

  14. Measurement Schedule

  15. Speed as a Function of Age (Baseline only, All Participants)

  16. Conflicting Estimates of Change

  17. Multiple Group LGM • Use age as a cohort indicator • Model change as a function of age rather than study time • Assume (initially) no cohort differences in • growth • re-test effects, and the • influence of background variables

  18. Cross-Sequential Cohort Design

  19. Hypothesized and Observed Longitudinal Course

  20. Mean Scores On Repeat Testing(Non-Speed Trained Group)

  21. Parameterization of Multiple Group LGM

  22. Parameterization of Multiple Group LGM

  23. Parameterization of Multiple Group LGM

  24. Results: Cohort-Specific and Model Implied Trajectories

  25. Hypothesized Longitudinal Course

  26. Conclusion • MGLGM one method for modeling re-test effect and aging effect separately • LGM feature of “freely estimating time scores” useful for capturing “residual” re-test effects • Examine relationship of background characteristics and variance in retest and aging effects • Relationship of retest and learning to clinically meaningful outcomes

  27. Acknowledgement • ACTIVE study (Advanced Cognitive Training for Independent and Vital Elderly) is a multi-site collaborative cognitive intervention trial supported by the National Institute on Aging and the National Institute on Nursing Research. • Sharon Tennstedt is the principal investigator at the coordinating center, New England Research Institutes, Watertown, Massachusetts (AG14282). • The principal investigators and field sites include • Karlene Ball, University of Alabama at Birmingham (AG14289); • Michael Marsiske, Institute on Aging, University of Florida, Gainesville (AG14276); • John Morris, Hebrew Rehabilitation Center for Aged Research and Training Institute, Boston (NR04507); • George Rebok, Johns Hopkins University Bloomberg School of Public Health (AG14260); • Sherry Willis, Penn State University, Gerontology Center (AG14263). • David Smith was the principal investigator at Indiana University School of Medicine, Regenstrief Institute, Indianapolis (NR04508) at the time of initial award, currently Fred Unverzagt is currently the principal investigator.

  28. Age Differences in MSQ Score (Baseline EPESE) b = -.02 SD units per year Baseline data from EPESE/ICPSR public use data file, baseline data only, listwise complete on Mental Status Questionnaire (MSQ) scores at first, fourth and seventh assessment

  29. Age Differences in MSQ Score (Baseline EPESE) b = -0.02 SD/year b = -0.10 SD/year b = -0.06 SD/year Baseline data from EPESE/ICPSR public use data file, baseline data only, listwise complete on Mental Status Questionnaire (MSQ) scores at first, fourth and seventh assessment

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