Where Are N-of-1 Trials Headed?

Nikles, Jane

doi:10.1007/978-94-017-7200-6_17

Cited by 7 publications

(5 citation statements)

References 33 publications

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“…In the context of N-of-1 trials (which could be understood as a specific kind of SCED more common in medical research; Nikles & Mitchell, 2015, Tate & Perdices, 2019, it has been recommended that "all statistical methods planned-from visual representation to meta-analysis-should be described in the protocol" (Porcino et al, 2020, p. 10), including effect sizes, statistical significance, ways of performing sensitivity analyses, and how heterogeneity between participants will be assessed. In the SCED context, the importance of explicitly describing the expected data pattern and the expected effect of the intervention has also been recently emphasized (Maggin et al, 2020).…”

Section: A Priori Aspects To Include In the Data Analytic Planmentioning

confidence: 99%

A Priori Justification for Effect Measures in Single-Case Experimental Designs

Manolov¹,

Moeyaert²,

Fingerhut³

2020

Preprint

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Due to the complex nature of single-case experimental design data, numerous metrics are available to quantify and evaluate the effectiveness of an intervention. An inappropriate choice of the metric can result in a misrepresentation of the intervention effectiveness and this can have far-reaching implications for theory, practice and policymaking. As guidelines for reporting appropriate justification for a specific metric selection are missing, the first aim is to identify the relevant dimensions for metric selection and justification prior to data gathering. For this purpose, an overview of metrics quantifying intervention effectiveness is provided and a literature review of 122 empirical SCED research articles is presented. The second aim is to use these dimensions to construct a user-friendly tool guiding applied researchers when they justify their metric selection prior to data collection. The use of this tool is illustrated in the context of a preregistered protocol. This study is the first study that attempts to propose reporting guidelines to justify the effect size metric choice, before collecting the data, to avoid selective reporting of the largest quantifications of effect. A proper justification, less prone to confirmation bias, and transparent and explicit reporting can enhance the credibility of the single-case design study findings.

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Section: A Priori Aspects To Include In the Data Analytic Planmentioning

confidence: 99%

A Priori Justification for Effect Measures in Single-Case Experimental Designs

Manolov¹,

Moeyaert²,

Fingerhut³

2020

Preprint

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“…Representing results and selecting the most appropriate statistical analysis. To access an overview of statistical analysis available for SCEDs, readers can refer to Manolov and Moeyaert 60,61 or to the specific options for N‐of‐1 trials 1,14 . Abbreviations: MBD, multiple‐baseline design; SCED, single‐case experimental design.…”

Section: Baseline Features Are Crucial To All Scedsmentioning

confidence: 99%

“…to answer the question ‘does this intervention help?’ 12 ), whenever feasible, multiple (i.e. aggregated) N‐of‐1 trials should be preferred to other SCEDs and to group randomized controlled trials (RCTs), because they (1) have a higher level of evidence according to the Oxford Centre for Evidence‐Based Medicine; 12 (2) increase power at the group level; 13 (3) provide a personalized statistical significance at the single‐participant level; (4) rely on multiple trials on the same participant, so they are more relevant to the participant than decisions based on the mean expected responses, inferred from the best quality RCT meta‐analysis; (5) may contribute to increased participant confidence and commitment to long‐term management 14 . As high‐quality guidelines already cover all the important themes for N‐of‐1 trials, 1,14 and because N‐of‐1 methodology is well recognized, 15,16 this paper will focus mainly, but not exclusively, on MBD.…”

Section: Figurementioning

confidence: 99%

Single‐case experimental designs for child neurological rehabilitation and developmental disability research

Krasny-Pacini

2023

Develop Med Child Neuro

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Single‐case experimental designs (SCEDs) are a group of methodologies of growing interest, aiming to test the effectiveness of an intervention at the single‐participant level, using a rigorous and prospective methodology. SCEDs may promote flexibility on how we design research protocols and inform clinical decision‐making, especially for personalized outcome measures, inclusion of families with challenging needs, measurement of children's progress in relation to parental implementation of interventions, and focus on personal goals. Design options for SCEDs are discussed in relation to an expected on/off effect of the intervention (e.g. school/environmental adaptation, assistive technology devices) or, alternatively, on an expected carry‐on/maintenance of effects (interventions aiming to develop or restore a function). Randomization in multiple‐baseline designs and ‘power’ calculations are explained. The most frequent reasons for not detecting an intervention effect in SCEDs are also presented, especially in relation to baseline length, trend, and instability. The use of SCEDs on the front and back ends of randomized controlled trials is discussed. What this paper adds Single‐case experimental designs (SCEDs) may promote flexibility on how we design research protocols. Randomization in multiple‐baseline designs allows ‘power’ calculations based on randomization tests. Whenever feasible, N‐of‐1 trials should be preferred to other SCEDs and to group randomized controlled trials.

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“…Comparisons of treatment efficacy should thus be performed within blocks. This randomly determined sequence is equivalent to the N-of-1 trials used in the health sciences (Nikles & Mitchell, 2015), where the several random-order AB blocks are called multiple crossovers. It is possible (and in the health sciences common) to replicate the series across several participants, each with its own randomly determined sequence.…”

Section: The Proposals Are Applicable To Different Single-case Experi...mentioning

confidence: 99%

Assessing Consistency in Single-Case Data Features Using Modified Brinley Plots

Manolov¹,

Tanious²

2020

Preprint

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The current text deals with the assessment of consistency of data features from experimentally similar phases and consistency of effects in single-case experimental designs. Although consistency is frequently mentioned as a critical feature, few quantifications have been proposed so far: namely, under the acronyms CONDAP (consistency of data patterns in similar phases) and CONEFF (consistency of effects). Whereas CONDAP allows assessing the consistency of data patterns, the proposals made here focus on the consistency of data features such as level, trend, and variability, as represented by summary measures (mean, ordinary least squares slope, and standard deviation, respectively). The assessment of consistency of effect is also made in terms of these three data features. The summary measures are represented as points on a modified Brinley plot and their similarity is assessed via quantifications of distance. Both absolute and relative measures of consistency are proposed: the former expressed in the same measurement units as the outcome variable and the latter as a percentage. Illustrations with real data sets (multiple baseline, ABAB, and alternating treatments designs) show the wide applicability of the proposals. We developed a user-friendly website to offer both the graphical representations and the quantifications.

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Where Are N-of-1 Trials Headed?

Cited by 7 publications

References 33 publications

A Priori Justification for Effect Measures in Single-Case Experimental Designs

A Priori Justification for Effect Measures in Single-Case Experimental Designs

Single‐case experimental designs for child neurological rehabilitation and developmental disability research

Assessing Consistency in Single-Case Data Features Using Modified Brinley Plots

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