Randomized clinical trial quality has improved over time but is still not good enough: an analysis of 176,620 randomized controlled trials published between 1966 and 2018
Bibliographic record
Abstract
Abstract Background Many randomized controlled trials (RCTs) are biased and difficult to reproduce due to methodological flaws and poor reporting. There is increasing attention for responsible research practices including reporting guidelines, but it is unknown whether these efforts have improved RCT quality (i.e. reduced risk of bias). We therefore mapped trends over time in trial publication, trial registration, reporting according to CONSORT, and characteristics of publication and authors. Methods Meta-information of 176,620 RCTs published between 1966 and 2018 was extracted. Risk of bias probability (four domains: random sequence generation, allocation concealment, blinding of patients/personnel, and blinding of outcome assessment) was assessed using validated risk-of-bias machine learning tools. In addition, trial registration and reporting according to CONSORT were assessed with automated searches. Characteristics were extracted related to publication (number of authors, journal impact factor, medical discipline) and authors (gender and Hirsch-index). Findings The annual number of published RCTs substantially increased over four decades, accompanied by increases in the number of authors (5.2 to 7.8), institutions (2.9 to 4.8), female authors (20 to 42%, first authorship; 17 to 29%, last authorship), and Hirsch-indices (10 to 14, first authorship; 16 to 28, last authorship). Risk of bias remained present in most RCTs but decreased over time for the domains allocation concealment (63 to 51%), random sequence generation (57 to 36%), and blinding of outcome assessment (58 to 52%). Trial registration (37 to 47%) and CONSORT (1 to 20%) rapidly increased in the latest period. In journals with higher impact factor (>10), risk of bias was consistently lower, higher levels of trial registration more frequent, and mentioning CONSORT. Interpretation The likelihood of bias in RCTs has generally decreased over the last decades. This may be driven by increased knowledge and improved education, augmented by mandatory trial registration, and more stringent reporting guidelines and journal requirements. Nevertheless, relatively high probabilities of bias remain, particularly in journals with lower impact factors. This emphasizes that further improvement of RCT registration, conduct, and reporting is still urgently needed. Funding This study was funded by The Netherlands Organisation for Health Research and Development (445001002).
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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 | 0.146 | 0.359 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.025 | 0.041 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".