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.

翻译：背景：真实的认知纵向衰退可能被重复测试所掩盖，这种现象称为练习效应（PEs）。我们开发了一个建模框架，通过基线对齐参与者，并独立于年龄相关变化估计特定访视的练习效应。方法：使用真实数据（N=175），我们通过线性混合效应模型估计了受试者内相关性，并将这些参数应用于模拟健康对照（HC）和精神分裂症患者（SZ）的纵向轨迹。模拟包含了老龄化、诊断差异和累积练习效应指标。我们分别拟合包含和不包含练习效应的广义估计方程（GEEs）以比较模型性能。结果：忽略练习效应的模型高估了认知稳定性，并减弱了HC与SZ组间的差异。纳入特定访视的练习效应改善了真实轨迹的恢复，并更准确地区分了老龄化效应与学习相关增益。交互作用模型进一步发现，练习效应可能因诊断或基线年龄而异。结论：若未建模，练习效应会显著偏倚纵向估计。所提出的基于对齐的GEE框架提供了一种原则性方法来估计练习效应，并在模拟和实际场景中提高了准确性。关键词：练习效应；重复测试；序列测试；纵向测试；轻度认知障碍；认知变化。