149 Factors relating to nonpublication and publication bias in clinical trials in Canada: a qualitative interview study
Bibliographic record
Abstract
Objectives Clinical trials are essential for informing drug development and clinical practice, but many clinical trials are not published and positive trials are more likely to be published than negative trials. Systematic reviews have examined factors contributing to nonpublication of biomedical and health-related studies, based on reasons provided by investigators. However, the ambiguity of reasons commonly given for nonpublication, such as a lack of time or the low priority of a study, make these studies difficult to interpret. We conducted a qualitative interview study to investigate factors related to clinical trial reporting in Canada. The analysis reported in this abstract aimed to understand factors contributing to nonpublication and publication bias in clinical trials in Canada. We have separately published findings from these interviews relating to industry sponsor influence in clinical trial reporting. Method Our study used a qualitative research design involving semi-structured, in-depth interviews. We conducted the interviews between March 2019 and April 2021 with participants in the Canadian provinces of Alberta, British Columbia and Ontario. We used purposive sampling to include clinical trial investigators from a range of fields, past trial participants from trials of treatments for a variety of medical conditions, and others involved in the conduct, administration or ethical review of clinical trials. Analysis of interview transcripts was informed by grounded theory. Initial coding involved developing provisional codes to characterize processes relating to clinical trial reporting. Focused coding and memo-writing were used to identify key themes. Results The study included 17 clinical trial investigators, 1 clinical research coordinator, 3 research administrators, 3 research ethics board members, and 10 clinical trial participants. Several factors contribute to nonpublication and publication bias in trial research. A core theme was that reporting practices are shaped by incentives within the research system which favour publication of positive over negative trials. Investigators are discouraged from reporting by experiences or perceptions of difficulty in publishing negative findings but rewarded for publishing positive findings in various ways. Publication of positive trials may be more likely to lead to funding from industry sponsors and nonindustry funders. Research institutions play a role in incentivizing publication of positive trials, by rewarding researchers who attract funding and publish in prestigious journals, through promotion, bonuses, and recognition. Policies to promote trial reporting have been too weak and inconsistent to counterbalance the prevailing incentives that lead to nonpublication and publication bias. Conclusions While a range of factors contribute to nonpublication and publication bias, our study suggests that clinical trial reporting practices in Canada are shaped by incentives which favour publication of positive over negative trials. Canadian universities and research institutions could help change incentives by more widely adopting performance metrics that emphasize full reporting of trial results in journals or registries. It may also be valuable for research institutions to implement programs to support researchers to report results in trial registries in a timely manner, which could be modelled on strategies used at some US medical schools to improve compliance with regulatory requirements to report clinical trials. Health Canada could also play a central role in changing incentives by adopting regulatory measures to require timely reporting of results within a recognized clinical trial registry.
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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 | 0.074 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.032 | 0.027 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".