Are the Subgroup Analyses in Stroke Clinical Trials Credible? – Protocol for a Systematic Review
Bibliographic record
Abstract
Abstract Background: Clinicians and decision-makers are mostly interested in how the overall effect in a randomized controlled trial impact patient differently due to differential treatment effects. Subgroup analyses of randomized trials examine if the overall treatment effect is consistent among the subgroup or to identify groups that modify the intervention effects. Well defined and performed subgroup analyses led to critical policy decisions; however, inappropriately performed subgroup analyses had resulted in incorrect decisions with negative consequences. This systematic review examines the reporting quality and the subgroup analyses’ credibility in stroke clinical trials. Methods and analysis: We will extract relevant studies from PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), and Web of Science using three concepts in the Medical Subject Headings (MESH) heading. Two reviewers will independently screen the title, abstract, and full text of relevant studies. We will examine the risk of bias and the credibility of reported subgroup analyses using two validated instruments, i.e., Cochrane Risk-of-Bias tool for randomized trials version 2 (RoB 2) and the Instrument for assessing the Credibility of Effect Modification Analyses (ICEMAN), respectively. Random effects regression will be used to evaluate study characteristics associated with the credibility of subgroup analyses in the included studies.Expected outcomes: This research aims to review the quality of subgroup analyses’ results and reporting. The research’s results will also provide critical methodological contributions to the statistical literature of clinical trials. Ethics and dissemination: This systematic review does not require primary patient data and does not require ethical approval. The review’s results will be published in a peer-review and scientific conferences.Trial registration number: CRD42020223133
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.205 | 0.360 |
| Meta-epidemiology (narrow) | 0.008 | 0.008 |
| Meta-epidemiology (broad) | 0.022 | 0.022 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.087 | 0.024 |
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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".