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

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)

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Latent Growth Curve Model for Linear Change

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B ac kgro u n dV ariab le s

Adding Background and Explanatory Variables

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

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)

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Measurement Schedule

Assessment Study Year Baseline 0 (intervention) Post-Test 0.23 Follow-up 1 1.00 Follow-up 2 2.00

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

Conflicting Estimates of Change

EstimatedModel Annual Change

Baseline age-diff. +0.19 Repeated Measures†

Post-Test Part -3.80FU1 -> FU2 -0.01

†speed-trained subjects excluded

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

Cross-Sequential Cohort Design

Year of Obs '95 '96 '97Observation 1 2 3------------------------Cohort Age 1 65 66 67 2 66 67 68 3 67 68 69

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GEE model using ordinal time adjustment for baseline age

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

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NOTE: "Age" is age at baseline assessment. Model relevant tonon-Speed trained subjects only.

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Parameterization of Multiple Group LGM

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Parameterization of Multiple Group LGM

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Parameterization of Multiple Group LGM

Model 1: Maximum Likelihood Estimation, Complete Sample Analysis Assuming MAR; Excluding those who received speed training (4 y’s, 3 ’s) N=1,801; Number of groups = 19 (n=31 to 131) Model 2(df), P 277.353 (218) P=.004 CFI, TFI 0.985, 0.992 RMSEA (90% CI) 0.054 (.032-.072) Model Part Estimate SE P Time Steps for Recall Effect post-test 0.23 -- -- first annual follow-up 0.28 (.01) <.001 second annual follow-up 0.34 (.02) <.001 Latent Variable Means/Intercepts Baseline -1.83 (0.10) <.001 Age-related change +0.18 (0.01) <.001 Retest effect -3.01 (0.22) <.001 Regressions Re-test effect on Baseline 0.44 (0.07) <.001 Age-related change on Baseline -0.01 (0.00) <.001

Model 2: ...adding educational attainment (years of education centered at grade 12) to model Model 2(df), P 335.963 (291) P=.04 CFI, TFI 0.976, 0.984 RMSEA (90% CI) 0.057 (.016-.083) Model Part Estimate SE P Time Steps for Recall Effect post-test 0.23 -- -- first annual follow-up 0.29 (.02) <.001 second annual follow-up 0.33 (.02) <.001 Latent Variable Means/Intercepts Baseline -1.50 (0.15) <.001 Age-related change +0.18 (0.02) <.001 Retest effect -2.88 (0.32) <.001 Regressions Re-test effect on Baseline 0.51 (0.12) <.001 Age-related change on Baseline -0.01 (0.01) 0.061 Baseline on years of education -0.19 (0.05) <.001 Re-test on years of education 0.02 (0.07) 0.766 Age-related change on education 0.00 (0.00) 0.394

Results: Cohort-Specific and Model Implied Trajectories

Mode l-Im p lied A ge -R e lated C hange

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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

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.

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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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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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