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Separation of Longitudinal Change from ReTest Effect using a MultipleGroup Latent Growth Model

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Title: Separation of Longitudinal Change from ReTest Effect using a MultipleGroup Latent Growth Model


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 (n2,428) aged 65-83

13
Outcome Measure
  • Speed of Processing Composite
  • Ball, et al. Jama, 2002 2882271-81.
  • 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
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15
Measurement Schedule
16
Speed as a Function of Age (Baseline only, All
Participants)
17
Conflicting Estimates of Change
18
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

19
Cross-Sequential Cohort Design
20
Hypothesized and Observed Longitudinal Course
21
Mean Scores On Repeat Testing(Non-Speed Trained
Group)
22
Parameterization of Multiple Group LGM
23
Parameterization of Multiple Group LGM
24
Parameterization of Multiple Group LGM
25
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26
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Results Cohort-Specific and Model Implied
Trajectories
28
Hypothesized Longitudinal Course
29
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

30
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.

31
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
32
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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