Semiparametric Models for Practice Effects in Longitudinal Cognitive Trajectories: Application to an Aging Cohort Study
Y. Xu, T. Wu, A. Van Dyne, E. Lee, L. Eyler, and X. Tu

TL;DR
This paper introduces a new modeling framework that accounts for practice effects in longitudinal cognitive studies, improving the accuracy of cognitive decline estimates and distinguishing true aging effects from learning gains.
Contribution
The study develops an alignment-based GEE framework that estimates visit-specific practice effects independently of age-related change, enhancing analysis of cognitive trajectories.
Findings
Ignoring practice effects inflates stability estimates.
Including practice effects improves trajectory recovery.
Practice effects vary by diagnosis and age.
Abstract
Background: True cognitive longitudinal decline can be obscured by repeated testing, which is called practice effects (PEs). We developed a modeling framework that aligns participants by baseline and estimates visit-specific PEs independently of age-related change. Method: Using real data (), we estimated within-subject correlations via linear mixed-effects modeling and applied these parameters to simulate longitudinal trajectories for healthy controls (HC) and individuals with schizophrenia (SZ). Simulations incorporated aging, diagnostic differences, and cumulative PE indicators. Generalized estimating equations (GEEs) were fit with and without PEs to compare model performance. Results: Models that ignored PEs inflated estimates of cognitive stability and attenuated HC--SZ group differences. Including visit-specific PEs improved recovery of true trajectories and more…
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Taxonomy
TopicsSchizophrenia research and treatment · Mental Health Research Topics · Dementia and Cognitive Impairment Research
