Comparing the clinical trial characteristics of industry–funded trials and non– industry–funded trials
Bibliographic record
Abstract
Importance: In randomized controlled trials (RCTs), small sample size and lack of blinding can cause biased and spurious results. Whether and how study characteristics differ based on a trial's funder is an important area to study. Objective: To compare study characteristics of RCTs funded by industry with study characteristics of RCTs not funded by industry. Design, Setting and Participants: We systematically reviewed all RCTs published between 2015 and 2019 in the New England Journal of Medicine (NEJM), Lancet, and Journal of the American Medical Association (JAMA). Our primary data sources were ClinicalTrials.gov and MEDLINE. Data extraction included manual review and use of natural language processing. Main Outcomes and Measures: We compared the rate of blinding, use of placebo, and sample size. We used natural language processing to analyze the sentiment of the study's conclusion as reported in the abstract. As proxies for knowledge dissemination, we calculated the AltMetric scores and number of times the article was cited (citation count). Results: We identified 1533 RCTs published by NEJM, Lancet, and JAMA between 2015 and 2019. Of these RCTs, 697 were funded by industry. Trials funded by industry were more likely to be blinded (n=378, 54% vs n=318, 38%), more likely to include a placebo (n=317, 45% vs n=196, 23%), more likely to post their results on ClinicalTrials.gov (78%, 443 of 570 vs 41%, 207 of 501) compared to trials that were not industry funded. Industry–funded RCTs had a smaller sample size than non–industry–funded RCTs (median=557 [IQR: 230, 1369] vs 648 [IQR: 301, 1916], P<0.01). Trials funded by industry had more citations than non–industry–funded trials (285 vs 145, p < 0.01), while per–manuscript Almetric scores were similar between both groups (229 vs 226, p=0.2). Conclusions and Relevance: These data highlight important variability in key metrics of trial quality and call attention to specific areas of improvement, especially for non–industry–funded 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 | 0.609 | 0.870 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| 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".