Estimating statistical power for event-related potential studies using the late positive potential.
Estimating statistical power for event-related potential studies using the late positive potential.
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使用晚期积极潜力估算事件相关潜在研究的统计能力。
DOI：
10.1111/psyp.13482
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发表时间：
2020-02
期刊：
影响因子：
3.7
通讯作者：
Versace F
中科院分区：
文献类型：
--
作者：
Gibney KD;Kypriotakis G;Cinciripini PM;Robinson JD;Minnix JA;Versace F
晚期正电位（LPP）是一种常用的测量方法，用于研究事件相关电位（ERP）范式中受试者的情绪过程。尽管它在情感神经科学中得到广泛应用，但目前对于如何使用 LPP 适当地支持 ERP 研究还没有黄金标准。本研究调查了试验数量、受试者数量和效应大小如何影响 LPP 分析中的统计功效。使用蒙特卡罗模拟 ERP 实验，其中包含不同数量的试验、受试者和已知强度的综合效应，我们测量了在 1,489 个实验中获得统计显着效应的概率，每个实验重复 1,000 次。可以预见的是，我们的结果表明，统计功效随着试验和受试者数量的增加以及效应量的增加而增加。我们还发现，与受试者间设计相比，受试者内设计可以通过较少数量的受试者和试验以及较低的效应量来实现更高水平的统计功效。此外，我们发现，随着受试者被添加到实验中，效应大小和统计功效之间关系的斜率增加并向左移动，直到在较高效应大小下功效渐近至接近 100%。这表明，与更稳健的效应量 (>1.5 μV) 相比，添加更多受试者可以大大提高较低效应量 (<1 μV) 的统计功效。我们根据参与者观看情感图像时收集的真实数据运行一系列新的模拟实验，确认了基于合成效果的模拟结果。
The late positive potential (LPP) is a common measurement used to study emotional processes of subjects in event-related potential (ERP) paradigms. Despite its extensive use in affective neuroscience, there is presently no gold standard for how to appropriately power ERP studies using the LPP. The present study investigates how the number of trials, number of subjects, and magnitude of the effect size affect statistical power in analyses of the LPP. Using Monte Carlo simulations of ERP experiments with varying numbers of trials, subjects, and synthetic effects of known magnitude, we measured the probability of obtaining a statistically significant effect in 1,489 experiments repeated 1,000 times each. Predictably, our results showed that statistical power increases with increasing numbers of trials and subjects and at larger effect sizes. We also found that higher levels of statistical power can be achieved with lower numbers of subjects and trials and at lower effect sizes in within-subject than in between-subjects designs. Furthermore, we found that as subjects are added to an experiment, the slope of the relationship between effect size and statistical power increased and shifted to the left until the power asymptoted to nearly 100% at higher effect sizes. This suggests that adding more subjects greatly increases statistical power at lower effect sizes (<1 μV) compared with more robust (>1.5 μV) effect sizes. We confirmed the results from the simulations based on the synthetic effects by running a new series of simulated experiments based on real data collected while participants looked at emotional images.
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