Department of Life Sciences, Brunel University London, United Kingdom.
Department of Psychology, University of North Texas, Denton, Texas, USA.
J Trauma Stress. 2020 Dec;33(6):1144-1153. doi: 10.1002/jts.22614. Epub 2020 Nov 17.
Single-case experimental designs (SCEDs) involve obtaining repeated measures from one or a few participants before, during, and, sometimes, after treatment implementation. Because they are cost-, time-, and resource-efficient and can provide robust causal evidence for more large-scale research, SCEDs are gaining popularity in trauma treatment research. However, sophisticated techniques to analyze SCED data remain underutilized. Herein, we discuss the utility of SCED data for trauma research, provide recommendations for addressing challenges specific to SCED approaches, and introduce a tutorial for two Bayesian models-the Bayesian interrupted time-series (BITS) model and the Bayesian unknown change-point (BUCP) model-that can be used to analyze the typically small sample, autocorrelated, SCED data. Software codes are provided for the ease of guiding readers in estimating these models. Analyses of a dataset from a published article as well as a trauma-specific simulated dataset are used to illustrate the models and demonstrate the interpretation of the results. We further discuss the implications of using such small-sample data-analytic techniques for SCEDs specific to trauma research.
单案例实验设计（SCED）涉及在治疗实施之前、期间和有时之后从一个或几个参与者那里获得重复测量。由于它们具有成本效益、时间效益和资源效益，并且可以为更大规模的研究提供强有力的因果证据，因此 SCED 在创伤治疗研究中越来越受欢迎。然而，分析 SCED 数据的复杂技术仍未得到充分利用。本文讨论了 SCED 数据在创伤研究中的应用，为解决 SCED 方法特有的挑战提供了建议，并介绍了两种贝叶斯模型的教程，即贝叶斯中断时间序列（BITS）模型和贝叶斯未知变化点（BUCP）模型，可用于分析通常样本量较小、自相关的 SCED 数据。提供了软件代码，以方便指导读者估计这些模型。使用来自已发表文章的数据集和特定于创伤的模拟数据集的分析来说明模型，并演示结果的解释。我们进一步讨论了使用这种小样本数据分析技术对特定于创伤研究的 SCED 的影响。
