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)

Hypothesized Longitudinal Course

Hypothesized and Observed Longitudinal Course

Bias in Estimate of Baseline Level and Change

Hypothesized Longitudinal Course

Latent Growth Model

Latent Growth Curve Model for Linear Change

Hypothesized Longitudinal Course

Latent Growth Curve Model for Linear Change with second intercept (learning factor)

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: –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)

Measurement Schedule

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

Conflicting Estimates of Change

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

Hypothesized and Observed Longitudinal Course

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

Parameterization of Multiple Group LGM

Results: Cohort-Specific and Model Implied Trajectories

Hypothesized Longitudinal Course

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.

Age Differences in MSQ Score (Baseline EPESE) 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  = -.02 SD units per year

Age Differences in MSQ Score (Baseline EPESE) 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  = SD/year  = SD/year  = SD/year