Data from each subject in a repeated-measures experiment form a time series, which may include trial-by-trial fluctuations arising from human factors such as practice or fatigue. Concerns about the statistical implications of such effects have increased the popularity of Generalized Additive Mixed Models (GAMMs), a powerful technique for modeling wiggly patterns. We question these statistical concerns and investigate the costs and benefits of using GAMMs relative to linear mixed-effects models (LMEMs). In two sets of Monte Carlo simulations, LMEMs that ignored time-varying effects were no more prone to false positives than GAMMs. Although GAMMs generally boosted power for within-subject effects, they reduced power for between-subject effects, sometimes to a severe degree. Our results signal the importance of proper subject-level randomization as the main defense against statistical artifacts due to by-trial fluctuations.

重复测量实验中每个受试者的数据形成一个时间序列，其中可能包括人为因素（例如练习或疲劳）引起的逐次试验波动。对此类影响的统计含义的担忧增加了通用加性混合模型（GAMM）的普及，GAMM是一种用于对摆动模式进行建模的强大技术。我们质疑这些统计问题，并调查相对于线性混合效应模型（LMEM）使用GAMM的成本和收益。在两组蒙特卡洛模拟中，忽略了时变效应的LMEM与GAMM相比更不容易出现误报。尽管GAMM通常会提高对象内效果的功率，但有时会严重降低对象间效果的功率。