PDF(Pubmed)
Abstract：
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.
摘要：
研究人员和从业者经常使用单例设计(SCD)，或n-of-1试验，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，但他们的许多建议并非来自研究文献。例如,其中一项建议表明,研究人员和从业人员应在引入自变量之前等待基线稳定性.然而,这一建议没有得到经验证据的有力支持.为了解决这个问题，我们使用蒙特卡罗模拟来生成具有固定，响应引导,和随机基线长度，同时操纵趋势和变异性。然后，我们的分析比较了两种分析方法产生的I型错误率和功效:保守双标准方法(结构化视觉辅助)和支持向量分类器(源自机器学习的模型).保守的对偶标准方法在使用响应引导决策时产生的错误较少（即，等待稳定性)和随机基线长度。相比之下,等待稳定性并没有减少支持向量分类器的决策错误。我们的发现质疑在使用SCD和机器学习时等待基线稳定性的必要性，但是这项研究必须与其他设计和图形参数重复，这些参数会随着时间的推移而变化，以支持我们的结果。
