Waiting for baseline stability in single-case designs : is it worth the time and effort?
Article [Version of Record]
Abstract(s)
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 Monte Carlo simulations
to generate graphs with fxed, response-guided, and random baseline lengths while manipulating trend and variability. 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 classifer (a model derived from machine learning). The conservative
dual-criteria method produced fewer errors when using response-guided decision-making (i.e., waiting for stability) and
random baseline lengths. In contrast, waiting for stability did not reduce decision-making errors with the support vector
classifer. Our fndings question the necessity of waiting for baseline stability when using SCDs with machine learning, but
the study must be replicated with other designs and graph parameters that change over time to support our results.