Network Meta-Analysis for Single-Case Design Studies: An Illustration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Single-case designs (SCDs) are used to evaluate the effects of interventions on individual participants. By repeatedly measuring participants under different conditions, SCD studies focus on individual effects rather than on group summaries. The main limitation of SCDs remains its generalisability to wider populations, reducing the relevance of their findings for practice and policy making. With this limitation in mind, methodological developments for synthesising SCD data from different studies that investigate the same research question have intensified in the past decades (e.g. multilevel modelling). However, these techniques are restricted to comparing two interventions at a time and can only incorporate evidence from studies that directly compare the two treatments of interest. These limitations could be addressed by using network meta-analysis that incorporates both direct and indirect evidence to simultaneously compare multiple interventions. Despite its potential, network meta-analytical techniques have yet to be applied to SCD data. Thus, in this paper, we argue that network meta-analysis can be a valuable tool to synthesise SCD data. We demonstrate the use of network meta-analysis in SCD data using a real dataset, and we conclude by reflecting on the challenges that SCD researchers might face when applying network meta-analysis methods to their data.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
| grok | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
| opus | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it