Monte Carlo Simulations to Develop Guidelines for the Use of Single-Case Designs: A Tutorial

Despite being a cornerstone of the science of behavior analysis, researchers and practitioners often rely on tradition and consensus-based guidelines, rather than empirical evidence, to make decisions about single-case designs. One approach to develop empirically-based guidelines is to use Monte Carlo simulations for validation, but behavior analysts are not necessarily trained to apply this type of methodology. Therefore, the purpose of our technical article is to walk the reader through conducting Monte Carlo simulations to examine the accuracy, Type I error rate, and power of a visual aid for AB graphs using R Code. Additionally, the tutorial provides code to replicate the procedures with single-case experimental designs as well as with the Python programming language. Overall, a broader adoption of Monte Carlo simulations to validate guidelines should lead to an improvement in how researchers and practitioners use single-case designs.

Waiting for Baseline Stability in Single-Case Designs: Is It Worth the Time and Effort?

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 a Monte Carlo simulation to generate a total of 480,000 AB graphs with fixed, response-guided, and random baseline lengths. 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 more power when using response-guided decision-making (i.e., waiting for stability) with negligeable effects on Type I error rate. 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 to support our results.

N-of-1 Trials in Healthcare

Interest in N-of-1 trials and single-case designs is increasing worldwide, particularly due to the movement towards personalised medicine and patient-centred healthcare [...]

Tutorial: Artificial Neural Networks and Deep Learning to Analyze Single-Case Designs

Since the start of the 21st century, few advances have had as far reaching consequences in science as the widespread adoption of artificial neural networks in fields as diverse as fundamental physics, clinical medicine, and social networking. In behavior analysis, one promising area for the adoption of neural networks involves the analysis of single-case graphs. However, few behavior analysts have any training on the use of these methods, which may limit progress in this area. The purpose of our tutorial is to address this issue by providing a step-by-step description on using artificial neural networks to improve the analysis of single-case graphs. To this end, we trained a new model using simulated data to analyze multiple baseline graphs and compared its outcomes to those of visual analysis on a previously published dataset. In addition to showing that artificial neural networks may outperform visual analysis, the tutorial provides information to facilitate the replication and extension of this line of work to other datasets and designs.