École de psychoéducation, Université de Montréal, C.P. 6128, succursale Centre-Ville, Montreal, QC, H3C 3J7, Canada.
Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Montreal, Canada.
Behav Res Methods. 2023 Feb;55(2):843-854. doi: 10.3758/s13428-022-01858-9. Epub 2022 Apr 25.
Researchers and practitioners often use single-case designs (SCDs), or n-of-1 trials, to develop and validate novel treatments. Standards and guidelines have been published to provide guidance as to how to implement SCDs, but many of their recommendations are not derived from the research literature. For example, one of these recommendations suggests that researchers and practitioners should wait for baseline stability prior to introducing an independent variable. However, this recommendation is not strongly supported by empirical evidence. To address this issue, we used Monte Carlo simulations to generate graphs with fixed, response-guided, and random baseline lengths while manipulating trend and variability. Then, our analyses compared the type I error rate and power produced by two methods of analysis: the conservative dual-criteria method (a structured visual aid) and a support vector classifier (a model derived from machine learning). The conservative dual-criteria method produced fewer errors when using response-guided decision-making (i.e., waiting for stability) and random baseline lengths. In contrast, waiting for stability did not reduce decision-making errors with the support vector classifier. Our findings question the necessity of waiting for baseline stability when using SCDs with machine learning, but the study must be replicated with other designs and graph parameters that change over time to support our results.
研究人员和从业者经常使用单病例设计(SCD)或 n-of-1 试验来开发和验证新的治疗方法。已经发布了标准和指南,以提供有关如何实施 SCD 的指导,但它们的许多建议并非来自研究文献。例如,其中一项建议表明,研究人员和从业者应在引入独立变量之前等待基线稳定。然而,这一建议并没有得到实证证据的有力支持。为了解决这个问题,我们使用蒙特卡罗模拟生成了具有固定、响应引导和随机基线长度的图形,同时操纵趋势和可变性。然后,我们的分析比较了两种分析方法的 I 型错误率和功效:保守的双重标准方法(一种结构化的视觉辅助工具)和支持向量分类器(一种源自机器学习的模型)。当使用响应引导决策(即等待稳定)和随机基线长度时,保守的双重标准方法产生的错误较少。相比之下,等待稳定并不会减少支持向量分类器的决策错误。我们的研究结果对使用机器学习进行 SCD 时是否需要等待基线稳定提出了质疑,但必须使用其他设计和随时间变化的图形参数进行复制研究,以支持我们的结果。