🤖 AI Summary
In longitudinal cognitive studies, practice effects (PEs) from repeated testing confound genuine cognitive decline. This paper proposes a semiparametric modeling framework that aligns individual trajectories to baseline and estimates visit-specific PEs independently, thereby disentangling cognitive aging from learning effects. Innovatively, it incorporates interaction terms between diagnostic group and baseline age to modulate PEs, while accounting for within-subject correlation via linear mixed models and validating results through generalized estimating equations (GEE) in both simulation and empirical analyses. Results demonstrate that omitting PEs substantially overestimates cognitive stability and attenuates between-group differences. In contrast, modeling visit-specific PEs markedly improves recovery accuracy of true cognitive trajectories, yielding significant gains in model fit and predictive performance across both simulated and real-world datasets.
📝 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 ($N=175$), 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 accurately distinguished aging effects from learning-related gains. Interaction models further identified that PEs may differ by diagnosis or by age at baseline.
Conclusion: Practice effects meaningfully bias longitudinal estimates if left unmodeled. The proposed alignment-based GEE framework provides a principled method to estimate PEs and improves accuracy in both simulated and real-world settings.
Keywords: practice effects; repeat testing; serial testing; longitudinal testing; mild cognitive impairment; cognitive change.