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.

中文翻译：

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单案例实验设计中的贝叶斯时间序列模型：创伤研究人员教程

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名或几名参与者那里获得重复测量。因为它们具有成本、时间和资源效率，并且可以为更大规模的研究提供强有力的因果证据，所以 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。在此，我们讨论了 SCED 数据在创伤研究中的效用，为解决 SCED 方法特有的挑战提供了建议，并介绍了两个贝叶斯模型的教程——贝叶斯中断时间序列 (BITS) 模型和贝叶斯未知变化点。 BUCP) 模型——可用于分析典型的小样本、自相关、SCED 数据。提供了软件代码以方便指导读者估计这些模型。对来自已发表文章的数据集以及特定于创伤的模拟数据集的分析用于说明模型并演示结果的解释。我们进一步讨论了对特定于创伤研究的 SCED 使用这种小样本数据分析技术的影响。