Assessing Consistency in Single-Case A-B-A-B Phase Designs

Tanious, René; De, Tamal Kumar; Michiels, Bart; Noortgate, Wim Van den; Onghena, Patrick

doi:10.1177/0145445519837726

Cited by 23 publications

(40 citation statements)

References 86 publications

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“…We will evaluate this by the consistency of data patterns approach. In addition, we will apply the consistency of the effects to assess the replication of the between-phase change across participants [ 61 ].…”

Section: Methodsmentioning

confidence: 99%

Evaluation of the Effectiveness of a Novel Brain-Computer Interface Neuromodulative Intervention to Relieve Neuropathic Pain Following Spinal Cord Injury: Protocol for a Single-Case Experimental Design With Multiple Baselines

Hesam‐Shariati¹,

Newton‐John²,

Singh³

et al. 2020

JMIR Res Protoc

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Background Neuropathic pain is a debilitating secondary condition for many individuals with spinal cord injury. Spinal cord injury neuropathic pain often is poorly responsive to existing pharmacological and nonpharmacological treatments. A growing body of evidence supports the potential for brain-computer interface systems to reduce spinal cord injury neuropathic pain via electroencephalographic neurofeedback. However, further studies are needed to provide more definitive evidence regarding the effectiveness of this intervention. Objective The primary objective of this study is to evaluate the effectiveness of a multiday course of a brain-computer interface neuromodulative intervention in a gaming environment to provide pain relief for individuals with neuropathic pain following spinal cord injury. Methods We have developed a novel brain-computer interface-based neuromodulative intervention for spinal cord injury neuropathic pain. Our brain-computer interface neuromodulative treatment includes an interactive gaming interface, and a neuromodulation protocol targeted to suppress theta (4-8 Hz) and high beta (20-30 Hz) frequency powers, and enhance alpha (9-12 Hz) power. We will use a single-case experimental design with multiple baselines to examine the effectiveness of our self-developed brain-computer interface neuromodulative intervention for the treatment of spinal cord injury neuropathic pain. We will recruit 3 participants with spinal cord injury neuropathic pain. Each participant will be randomly allocated to a different baseline phase (ie, 7, 10, or 14 days), which will then be followed by 20 sessions of a 30-minute brain-computer interface neuromodulative intervention over a 4-week period. The visual analog scale assessing average pain intensity will serve as the primary outcome measure. We will also assess pain interference as a secondary outcome domain. Generalization measures will assess quality of life, sleep quality, and anxiety and depressive symptoms, as well as resting-state electroencephalography and thalamic γ-aminobutyric acid concentration. Results This study was approved by the Human Research Committees of the University of New South Wales in July 2019 and the University of Technology Sydney in January 2020. We plan to begin the trial in October 2020 and expect to publish the results by the end of 2021. Conclusions This clinical trial using single-case experimental design methodology has been designed to evaluate the effectiveness of a novel brain-computer interface neuromodulative treatment for people with neuropathic pain after spinal cord injury. Single-case experimental designs are considered a viable alternative approach to randomized clinical trials to identify evidence-based practices in the field of technology-based health interventions when recruitment of large samples is not feasible. Trial Registration Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12620000556943; https://bit.ly/2RY1jRx International Registered Report Identifier (IRRID) PRR1-10.2196/20979

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Section: Methodsmentioning

confidence: 99%

Evaluation of the Effectiveness of a Novel Brain-Computer Interface Neuromodulative Intervention to Relieve Neuropathic Pain Following Spinal Cord Injury: Protocol for a Single-Case Experimental Design With Multiple Baselines

Hesam‐Shariati¹,

Newton‐John²,

Singh³

et al. 2020

JMIR Res Protoc

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“…Visual analysis involves a time-series plot of the results and a systematic interpretation of six data aspects that should be evident in the plot: the level of the measurements within and between phases, possible trends, the variability within and between phases, the immediacy of the effect after the introduction of the treatment, the overlap of the measurements in different phases, and the consistency of data patterns across similar phases (Kratochwill et al, 2010). In addition, each of these data aspects may be quantified using a descriptive measure or an effect size statistic (Tanious, De, Michiels, Van Den Noortgate, & Onghena, 2019).…”

Section: Descriptive Versus Inferential Statisticsmentioning

confidence: 99%

Small Sample Size Solutions

Schoot¹,

Miočević²

2020

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Researchers often have difficulties collecting enough data to test their hypotheses, either because target groups are small or hard to access, or because data collection entails prohibitive costs. Such obstacles may result in data sets that are too small for the complexity of the statistical model needed to answer the research question. This unique book provides guidelines and tools for implementing solutions to issues that arise in small sample research. Each chapter illustrates statistical methods that allow researchers to apply the optimal statistical model for their research question when the sample is too small. This essential book will enable social and behavioral science researchers to test their hypotheses even when the statistical model required for answering their research question is too complex for the sample sizes they can collect. The statistical models in the book range from the estimation of a population mean to models with latent variables and nested observations, and solutions include both classical and Bayesian methods. All proposed solutions are described in steps researchers can implement with their own data and are accompanied with annotated syntax in R. The methods described in this book will be useful for researchers across the social and behavioral sciences, ranging from medical sciences and epidemiology to psychology, marketing, and economics.

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“…Regarding the consistency of data patterns in experimentally similar phases, only one quantification has been proposed so far. Tanious et al (2019a) developed the CONsistency of DAta Patterns (CONDAP) measure for quantifying the degree of consistency between data patterns in A-B-A-B phase designs based on the Manhattan distance. The basic premise of CONDAP is that if two data patterns are highly consistent, then the standardized average Manhattan distance should be accordingly low.…”

Section: Analyzing the Consistency Of Data Patterns In Scedsmentioning

confidence: 99%

“…Finally, this overall average Manhattan distance is then divided by the pooled standard deviation of the two phases as proposed by Van den Noortgate and Onghena (2008) to obtain the scale invariant CONDAP (whose value would be 1.16 for the current example). If all experimentally similar phases have a standard deviation of zero, then the denominator of CONDAP would be zero as well, and it is recommended to calculate the overall average Manhattan distance without standardization (Tanious et al, 2019a) Based on a systematic review of a sample of 119 applied A-B-A-B studies, Tanious, De, Michiels, van den Noortgate, and Onghena (2019b) proposed the following guidelines for interpreting CONDAP: very high, 0 ≤ CONDAP ≤ 0.5; high, 0.5 < CONDAP ≤ 1; medium, 1 < CONDAP < 1.5; low, 1.5 < CONDAP ≤ 2; very low, CONDAP > 2. The two data patterns in Figure 1 are thus medium consistent.…”

Section: Analyzing the Consistency Of Data Patterns In Scedsmentioning

confidence: 99%

“…A second -and arguably equally important-type of consistency is the consistency of the effects. In the context of A-B-A-B designs, Tanious et al (2019a) proposed the CONsistency of the EFFects (CONEFF) measure for assessing the change in each data aspect between similar phase changes. Future studies might focus on extending CONEFF to the designs presented in this article so that both types of consistency can be assessed.…”

Section: Limitations and Future Researchmentioning

confidence: 99%

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The Assessment of Consistency in Single-Case Experiments: Beyond A-B-A-B Designs

2019

Self Cite

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Quality standards for single-case experimental designs (SCEDs) recommend inspecting six data aspects: level, trend, variability, overlap, immediacy, and consistency of data patterns. The data aspect consistency has long been neglected by visual and statistical analysts of SCEDs despite its importance for inferring a causal relationship. However, recently a first quantification has been proposed in the context of A-B-A-B designs, called CONsistency of DAta Patterns (CONDAP). In the current paper, we extend the existing CONDAP measure for assessing consistency in designs with more than two successive A-B elements (e.g., A-B-A-B-A-B), multiple baseline designs, and changing criterion designs. We illustrate each quantification with published research.

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Assessing Consistency in Single-Case A-B-A-B Phase Designs

Cited by 23 publications

References 86 publications

Evaluation of the Effectiveness of a Novel Brain-Computer Interface Neuromodulative Intervention to Relieve Neuropathic Pain Following Spinal Cord Injury: Protocol for a Single-Case Experimental Design With Multiple Baselines

Evaluation of the Effectiveness of a Novel Brain-Computer Interface Neuromodulative Intervention to Relieve Neuropathic Pain Following Spinal Cord Injury: Protocol for a Single-Case Experimental Design With Multiple Baselines

Small Sample Size Solutions

The Assessment of Consistency in Single-Case Experiments: Beyond A-B-A-B Designs

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