Treatment Burst Data Points and Single Case Design Studies: A Bayesian N-of-1 Analysis for Estimating Treatment Effect Size

• Published in: Perspectives on behavior science Vol. 43; no. 2; pp. 285 - 301
• Main Authors: Barnard-Brak, Lucy, Richman, David M., Watkins, Laci
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.06.2020; Springer Nature B.V
• Subjects: Alzheimer's disease; Bayesian analysis; Behavioral Science and Psychology; Clinical outcomes; Clinical trials; Design; Estimates; Extinction; Original Research; Psychology; Statistical methods; Single-case experimental designs; Estimating effect size; Single subject; Overlapping treatment data points
• ISSN: 2520-8969; 2520-8977; 2520-8977
• DOI: 10.1007/s40614-020-00258-8

Single-case experimental designs (SCED) evaluate treatment effects for each participant, but it is difficult to aggregate and quantify treatment effects across SCED participants receiving the same type of treatment. We applied Bayesian analytic procedures to SCED data aggregated across participants that have previously only been applied to large- N and group design studies of treatment effect sizes. For the current study, we defined transient elevated treatment data points as (1) above the range of the last five baseline sessions during the first three sessions of treatment (i.e., extinction burst); (2) within or above the range of baseline after the first three treatment sessions (i.e., recurrence burst); or (3) thinning phase data points above the last three prethinning treatment data points (i.e., thinning burst). Results indicated that the treatment effect sizes remained large regardless of controlling for transient elevated treatment data points. Finally, we examined the effects of reinforcer schedule thinning on estimates of treatment effect size. Results indicated a moderate negative impact of schedule thinning on treatment effect size with a 16% decrease in effect size. Recommendations for research and practice are provided, and the utility of using Bayesian analysis in single-case experimental designs is discussed.