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

Abstract

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.

Clinical Research: A Review of Study Designs, Hypotheses, Errors, Sampling Types, Ethics, and Informed Consent

“…However, in the absence of a study group, a single-case experimental design (SCED) was suggested as an alternative methodology that is equally reliable as a randomization study [ 3 ]. The single case study designs are called N-of-1 type clinical trials [ 4 , 5 ]. The N-of-1 study design is being increasingly applied in healthcare-related research.…”