Role of industry funders in oncology RCTs published in high-impact journals and its association with trial conclusions and time to publication
Bibliographic record
Abstract
Background: Previous studies have shown that industry funded trials are associated with pro-industry conclusions and publication bias. Less is known about the role of industry funders and their influence on trial conclusions and time to publication. Methods: We identified all industry funded RCTs published in six high-impact clinical journals between 2014 and 2016 to estimate the prevalence of the role of industry funders in trial design, data collection, data analyses, data interpretation and manuscript writing. Ordinal logistic regression was used to assess the association between the role of industry funders and trial conclusions, which was classified on a five-point scale. Cox proportional-hazards were used to examine the effect of role of funder on time to publication. Results: Of the 255 eligible RCTs, industry funders had a role in trial design in 179 (70.2%) trials, data collection in 160 (62.7%) trials, data analyses in 173 (67.8%) trials, data interpretation in 135 (52.9%) trials and manuscript writing in 168 (65.9%) trials. Trials with any role of industry funders had 3.6 times (95% CI 2.0-6.6) higher odds of having positive conclusions compared with those without role of industry funders. In trials with any role of industry funders, positive trials were published more rapidly than negative trials (hazard ratio = 4.3; 95% CI 2.7-6.7, P < 0.001), while for trials without role of industry funders, there was no association (hazard ratio = 1.07; 95% CI 0.57-1.99, P = 0.84). Conclusion: The involvement of industry funders is common in all stages of clinical trials and was associated with more positive conclusions and more rapid publication of RCTs with positive results.
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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 | 0.350 | 0.827 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".