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

Abstract

Copyright © 2020 The Authors. Single case experimental designs (SCEDs) involve obtaining repeated measures from one or few participants before, during, and (sometimes) after treatment implementation. SCEDs are gaining popularity in trauma treatment research because they are cost-, time-, and resource-efficient and can provide robust causal evidence for more large-scale research. However, sophisticated techniques to analyse SCED data remain underutilized. The purpose of this tutorial paper is to 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 Bayesian unknown change-point (BUCP) model – that can be used to analyse the typically small sample, autocorrelated, SCED data. Software codes are provided for the ease of guiding the readers on 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 interpretation of results. We further discuss the implications of using such small sample data-analytic techniques for SCEDs specific to trauma research.