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Record W4361285195 · doi:10.1177/17474930231168517

The credibility of subgroup analyses reported in stroke trials is low: A systematic review

2023· review· en· W4361285195 on OpenAlexaff
Ayoola Ademola, Lehana Thabane, Joel Adekanye, Ayooluwanimi Okikiolu, Samuel Babatunde, Mohammed Almekhlafi, Bijoy K. Menon, Michael D. Hill, Kevin A. Hildebrand, Tolulope T. Sajobi

Bibliographic record

VenueInternational Journal of Stroke · 2023
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsMcMaster UniversityImpactUniversity of Calgary
FundersU.S. Army Medical Research Acquisition Activity
KeywordsMedicineSubgroup analysisInterquartile rangeRandomized controlled trialMeta-analysisStroke (engine)MEDLINEPublication biasClinical trialInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Subgroup analyses are widely used to evaluate the heterogeneity of treatment effects in randomized clinical trials. However, there is a limited investigation of the quality of prespecified and reported subgroup analyses in stroke trials. This study evaluated the credibility of subgroup analyses in stroke trials. METHODS AND ANALYSIS: We searched Medline/PubMed, Embase, the Cochrane Central Register of Controlled Trials, and the Web of Science from inception to 24 March 2021. Three reviewers screened, extracted, and analyzed the data from the publications. Primary publications of stroke trials that reported at least one subgroup effect and had published corresponding study protocols were included. The Instrument for Assessing the Credibility of Effect Modification Analyses (ICEMAN) was used to examine the quality of the subgroup effects reported, with each subgroup effect assigned a credibility rating ranging from very low to high. Subgroup effects with two or more "definitely no" responses received a low credibility rating. The risk of bias was assessed using the Cochrane Risk-of-Bias tool for randomized trials version 2. RESULTS: Seventy-four articles met the inclusion criteria and reported a combined total of 647 subgroup effects. The median sample size was 1264 (interquartile range (IQR): 380-3876), and the median number of subgroups prespecified in the protocol was 6 (IQR: 2-10). Sixty-one (82%) studies used the univariate test of interaction. Of the total 647 subgroup effects reported in these studies, 319 (49%) were reported in acute stroke trials, while 423 (65%) had low credibility. CONCLUSION: The quality of subgroup analysis reporting in stroke trials remains poor. More effort is needed to train trialists on the best methods for designing and performing subgroup analyses, and how to report the results. TRIAL REGISTRATION NUMBER: We prospectively registered the review with International Prospective Register for Systematic Reviews (registration number: CRD42020223133).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.349
metaresearch head score (Gemma)0.737
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.651
Threshold uncertainty score0.803

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3490.737
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0180.020
Bibliometrics0.0210.016
Science and technology studies0.0020.008
Scholarly communication0.0110.014
Open science0.0050.005
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.916
GPT teacher head0.673
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designSystematic review
DomainMethods
GenreReview

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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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