Single-case experimental design yielded an effect estimate corresponding to a randomized controlled trial

Shadish, William R.; Rindskopf, David; Boyajian, Jonathan

doi:10.1016/j.jclinepi.2016.01.035

Cited by 9 publications

(10 citation statements)

References 32 publications

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“…Such models can deal with count data, which are ubiquitous in single-case research (Pustejovsky, 2018a), specifying a Poisson model (rather than a normal one) for the conditional distribution of the response variable (Shadish, Kyse, & Rindskopf, 2013). Other useful models are based on the binomial distribution, specifying a logistic model (Shadish et al, 2016), when the data are proportions that have a natural floor (0) and ceiling (100). Despite dealing with certain issues arising from single-case data, these models are not flawless.…”

Section: Regression-based Analysesmentioning

confidence: 99%

“…Rindskopf and Ferron suggested using logistic regression with an additional term for identifying the moment at which the response has gone halfway between the floor and the ceiling. Similarly, Shadish et al (2016) and Verboon and Peters (2018) used a logistic model for representing data with clear floor and ceiling effects. The information that can be obtained by fitting a generalized logistic model is in terms of the floor and ceiling levels, the rate of change, and the moments at which the change from the floor to the ceiling plateau starts and stops (Verboon & Peters, 2018).…”

Section: Fourth Issue: Out-of-bounds Forecastsmentioning

confidence: 99%

“…The information that can be obtained by fitting a generalized logistic model is in terms of the floor and ceiling levels, the rate of change, and the moments at which the change from the floor to the ceiling plateau starts and stops (Verboon & Peters, 2018). Shadish et al (2016) acknowledged that not all analysts are expected to be able to fit intrinsically nonlinear models and that choosing one model over another is always partly arbitrary, suggesting nonparametric smoothing as an alternative.…”

Section: Fourth Issue: Out-of-bounds Forecastsmentioning

confidence: 99%

“…Focusing on the need to improve MPD, the proposals by Rindskopf and Ferron (2014) and Verboon and Peters (2018) are not applicable, since the logistic model they present deals with considering the data of a baseline phase and an intervention phase jointly, whereas in MPD baseline trend is estimated and extrapolated in order to allow for a comparison between projected and observed patterns of the outcome variable (as suggested by Kratochwill et al, 2010, andSmolkowski, 2012, when performing visual analysis). In contrast, Shadish et al (2016) used the logistic model for representing the data within one of the phases in order to explore whether any within-phase change took place, but they were not aiming to use the within-phase model for extrapolating to the subsequent phase.…”

Section: Fourth Issue: Out-of-bounds Forecastsmentioning

confidence: 99%

“…In the present study, we discussed extrapolating linear trends because the MPD, our focal analytical technique, fits a straight line to the baseline data before extrapolating them. Nevertheless, it would be possible to fit a nonlinear (e.g., logistic) model to the baseline data (Shadish et al, 2016). Furthermore, there are many other alternative procedures for estimating and extrapolating trend, especially in the context of time-series analysis.…”

Section: Limitations In Relation To the Alternatives For Extrapolatinmentioning

confidence: 99%

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Extrapolating baseline trend in single-case data: Problems and tentative solutions

2018

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Single-case data often contain trends. Accordingly, to account for baseline trend, several data-analytical techniques extrapolate it into the subsequent intervention phase. Such extrapolation led to forecasts that were smaller than the minimal possible value in 40% of the studies published in 2015 that we reviewed. To avoid impossible predicted values, we propose extrapolating a damping trend, when necessary. Furthermore, we propose a criterion for determining whether extrapolation is warranted and, if so, how far out it is justified to extrapolate a baseline trend. This criterion is based on the baseline phase length and the goodness of fit of the trend line to the data. These proposals were implemented in a modified version of an analytical technique called Mean phase difference. We used both real and generated data to illustrate how unjustified extrapolations may lead to inappropriate quantifications of effect, whereas our proposals help avoid these issues. The new techniques are implemented in a user-friendly website via the Shiny application, offering both graphical and numerical information. Finally, we point to an alternative not requiring either trend line fitting or extrapolation.

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Section: Regression-based Analysesmentioning

confidence: 99%

Section: Fourth Issue: Out-of-bounds Forecastsmentioning

confidence: 99%

Section: Fourth Issue: Out-of-bounds Forecastsmentioning

confidence: 99%

Section: Fourth Issue: Out-of-bounds Forecastsmentioning

confidence: 99%

Section: Limitations In Relation To the Alternatives For Extrapolatinmentioning

confidence: 99%

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Extrapolating baseline trend in single-case data: Problems and tentative solutions

2018

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Internal and External Validity of the Comparative Interrupted Time‐series Design: A Meta‐analysis

Coopersmith¹,

Cook²,

Zurovac³

et al. 2022

J Policy Anal Manage

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This paper meta‐analyzes 12 heterogeneous studies that examine bias in the comparative interrupted time‐series design (CITS) that is often used to evaluate the effects of social policy interventions. To measure bias, each CITS impact estimate was differenced from the estimate derived from a theoretically unbiased causal benchmark study that tested the same hypothesis with the same treatment group, outcome data, and estimand. In 10 studies, the benchmark was a randomized experiment and in the other two it was a regression‐discontinuity study. Analyses revealed the average standardized CITS bias to be between −0.01 and 0.042 standard deviations; and all but one bias estimate from individual studies fell within 0.10 standard deviations of its benchmark, indicating that the near zero mean bias did not result from averaging many large single study differences. The low mean and generally tight distribution of individual bias estimates suggest that CITS studies are worth recommending for future causal hypothesis tests because: (1) over the studies examined, they generally resulted in high internal validity; and (2) they also promise high external validity because the empirical tests we synthesized occurred across a wide variety of settings, times, interventions, and outcomes.

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Behavioral Treatment for Speech and Language in Primary Progressive Aphasia and Primary Progressive Apraxia of Speech: A Systematic Review

Wauters,

Croot,

Dial

et al. 2023

Neuropsychol Rev

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Primary progressive aphasia (PPA) and primary progressive apraxia of speech (PPAOS) are neurodegenerative syndromes characterized by progressive decline in language or speech. There is a growing number of studies investigating speech-language interventions for PPA/PPAOS. An updated systematic evaluation of the treatment evidence is warranted to inform best clinical practice and guide future treatment research. We systematically reviewed the evidence for behavioral treatment for speech and language in this population. Reviewed articles were published in peer-reviewed journals through 31 May 2021. We evaluated level of evidence, reporting quality, and risk of bias using a modified version of the American Speech-Language Hearing Association (ASHA) Levels of Evidence, an appraisal point system, additional reporting quality and internal/external validity items, and, as appropriate, the Single Case Experimental DesignScale or the Physiotherapy Evidence Database – PsycBITERating Scale for Randomized and Non-Randomized Controlled Trials. Results were synthesized using quantitative summaries and narrative review. A total of 103 studies reported treatment outcomes for 626 individuals with PPA; no studies used the diagnostic label PPAOS. Most studies evaluated interventions for word retrieval. The highest-quality evidence was provided by 45 experimental and quasi-experimental studies (16 controlled group studies, 29 single-subject designs). All (k = 45/45) reported improvement on a primary outcome measure; most reported generalization (k = 34/43), maintenance (k = 34/39), or social validity (k = 17/19) of treatment for at least one participant. The available evidence supports speech-language intervention for persons with PPA; however, treatment for PPAOS awaits systematic investigation. Implications and limitations of the evidence and the review are discussed.

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Single-case experimental design yielded an effect estimate corresponding to a randomized controlled trial

Cited by 9 publications

References 32 publications

Extrapolating baseline trend in single-case data: Problems and tentative solutions

Extrapolating baseline trend in single-case data: Problems and tentative solutions

Internal and External Validity of the Comparative Interrupted Time‐series Design: A Meta‐analysis

Behavioral Treatment for Speech and Language in Primary Progressive Aphasia and Primary Progressive Apraxia of Speech: A Systematic Review

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