Bayesian Time-Series Models in Single Case Experimental Designs: A Tutorial for Trauma Researchers

Abstract

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.