Treatment Burst Data Points and Single Case Design Studies: A Bayesian N-of-1 Analysis for Estimating Treatment Effect Size
Bayesian N-of-1 gives SCED researchers one sturdy effect size that ignores short-lived spikes.
01Research in Context
What this study did
The authors built a new way to measure change in single-case studies. They used Bayesian N-of-1 models. The method keeps big treatment effects even after you delete short-lived spikes.
They tested the model with simulated data that looked like real ABA sessions. Schedule thinning was part of the test.
What they found
Large treatment effects stayed large after the model removed brief jumps in the data. When the schedule was thinned, the effect size dropped by sixteen percent.
This means the tool can tell the difference between lasting change and a quick blip.
How this fits with other research
Older papers worried about picking the wrong effect-size index. Campbell (2004) showed four common indices can point in different directions on the same data. The new Bayesian method gives one stable number instead.
Sen (2022) found five regression formulas that swing Cohen’s d from tiny to huge. The Bayesian model avoids that scatter by using one clear rule set.
Falligant et al. (2022) saw big resurgence spikes when reinforcement was cut fast. The Bayesian tool treats those spikes as noise and keeps the real trend.
Why it matters
You can now add a single number to your SCED graph that survives extinction bursts and thinning spikes. Share it with parents, teachers, or funders who want “proof” beyond visual analysis. Try the free code in the paper next time you write up a case.
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02At a glance
03Original abstract
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.
Perspectives on Behavior Science, 2020 · doi:10.1007/s40614-020-00258-8