Nonregistration, Discontinuation, and Nonpublication of Randomized Trials: A Systematic Review
Bibliographic record
Abstract
Importance: Previous work found that 25% to 30% of randomized clinical trials (RCTs) with protocols approved in 2012 or between 2000 and 2003 were discontinued prematurely, most commonly due to inadequate participant recruitment. To minimize research waste, RCTs should be registered and their results made available. Objectives: To assess the fate of RCTs approved by ethics committees in 2016 in terms of nonregistration, discontinuation, and nonpublication, and to examine RCT characteristics associated with discontinuation due to poor recruitment and nonpublication of RCT results. Evidence review: As a prespecified project of the Adherence to SPIRIT Recommendations (ASPIRE) study, this systematic review had access to 347 RCT protocols approved in 2016 by research ethics committees in the UK, Switzerland, Germany, and Canada. Eligible RCTs were defined as prospective studies randomly assigning participants to interventions to study effects on health outcomes. RCTs were excluded that never started, were ongoing at time of follow-up, were duplicates, or were labeled as pilot, feasibility, or phase 1 trials. Key trial characteristics were extracted from the approved trial protocols. In July 2024, pairs of reviewers systematically searched for trial registrations and results publications. When the status of either was unclear, the corresponding ethics committee or the principal investigator was contacted for clarification. Findings: Of 347 included RCTs, 20 (5.8%) were unregistered, 108 (31.1%) were discontinued, most often due to poor recruitment (49 [45.4%]), and 276 (79.5%) made their results publicly available. Results from industry-sponsored trials were more often available than non-industry-sponsored trials (166 of 181 [92.3%] vs 110 of 166 [66.3%]). This difference was attributable to a higher prevalence of industry-sponsored trials that reported results in trial registries (153 of 181 [84.5%]) vs nonindustry RCTs (17 of 166 [10.2%]). Multivariable logistic regression indicated that industry-sponsored trials were less frequently discontinued due to poor recruitment than non-industry-sponsored RCTs (adjusted odds ratio, 0.32 [95% CI, 0.15-0.71]). Conclusions and relevance: Findings from this systematic review indicated that nonregistration, premature discontinuation due to poor recruitment, and nonpublication of RCT results remained major challenges, especially for non-industry-sponsored trials. To mitigate these challenges, requirements enforced by funders and ethics committees also taking into account legal obligations should be considered and empirically evaluated.
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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 | 0.401 | 0.741 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".