Harm effects in non-registered versus registered randomized controlled trials of medications: a retrospective cohort study of clinical trials
Bibliographic record
Abstract
BACKGROUND: Trial registration aims to address potential bias from selective or non-reporting of findings, and therefore has a vital role in promoting transparency and accountability of clinical research. In this study, we aim to investigate the influence of trial registration on estimated harm effects in randomized controlled trials of medication interventions. METHODS: We searched PubMed for systematic reviews and meta-analyses of randomized trials on medication harms indexed between January 1, 2015, and January 1, 2020. To be included in the analyses, eligible meta-analyses should have at least five randomized trials with distinct registration statuses (i.e., prospectively registered, retrospectively registered, and non-registered) and 2 by 2 table data for adverse events for each trial. To control for potential confounding, trials in each meta-analysis were analyzed within confounder-harmonized groups (e.g., dosage) identified using the Directed Acyclic Graph method. The harm estimates arising from the trials with different registration statuses were compared within the confounder-harmonized groups using hierarchical linear regression. Results are shown as ratio of odds ratio (OR) and 95% confidence interval (CI). RESULTS: The dataset consists of 629 meta-analyses of harms with 10,069 trials. Of these trials, 74.3% were registered, and 23.9% were not registered, and for those registered, 70.6% were prospectively registered, while 26.3% were retrospectively registered. In comparison to prospectively registered trials, both non-registered trials (ratio of OR = 0.82, 95%CI 0.68 to 0.98, P = 0.03) and retrospectively registered trials (ratio of OR = 0.75, 95%CI 0.66 to 0.86, P < 0.01) had lower OR for harms based on 69 and 126 confounders-harmonized groups. The OR of harms did not differ between retrospectively registered and non-registered trials (ratio of OR = 1.02, 95%CI 0.85 to 1.23, P = 0.83) based on 76 confounders-harmonized groups. CONCLUSIONS: Medication-related harms may be understated in non-registered trials, and there was no obvious evidence that retrospective registration had a demonstrable benefit in reducing such selective or absent reporting. Prospective registration is highly recommended for future trials.
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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 | 0.275 | 0.505 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".