Selective reporting bias in randomised controlled trials from two network meta-analyses: comparison of clinical trial registrations and their respective publications
Bibliographic record
Abstract
OBJECTIVE: To determine (i) the difference in the frequency of serious adverse events (SAEs) reported in trial registrations and their respective primary publications and (ii) the effect of adding SAE data from registries to a network meta-analysis (NMA) in changing the surface under the cumulative ranking (SUCRA) curve values of interventions. DESIGN: Secondary analysis of primary publications from two NMAs. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: We included randomised trials published in English after 2005 that were included in two NMAs of pharmacological interventions for Alzheimer's disease and chronic obstructive pulmonary disease. DATA EXTRACTION: Two reviewers independently searched multiple international trial registries for registration status and abstracted data from the included study publications and ClinicalTrials.gov. RESULTS: Of the 203 randomised trials included, 140 (69.0%) were registered with a trial registry and 72 (35.5%) posted results in the registry. The proportion of registered trials increased over time (38.5% in 2005 vs 78.6% in 2014). Of the publications with results posted in a trial registry, 14 (19.4%) had inconsistent reporting of overall SAEs; 7 (10.4%) studies did not report SAEs in the publication but did in the registry. In the 134 randomised trials with a prespecified primary outcome in the registry, 19 studies (9.4%) had a change in the primary outcome in the publication. Adding SAEs reported in registries to the NMAs did not affect the ranking of interventions. CONCLUSION: We identified inconsistent reporting of SAEs in randomised trials that were included in two NMAs. Findings highlight the importance of including trial registries in the grey literature search and verifying safety data before incorporating it into NMAs. STUDY REGISTRATION: osf.io/mk6dr.
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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 | 0.456 | 0.752 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.023 | 0.061 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".