Financial Conflicts of Interest Are Common and Frequently Influence Conclusions of Economic Analyses Presented at the American Society of Hematology Annual Meeting.
Bibliographic record
Abstract
Abstract Abstract 810 Economic analyses of pharmaceutical agents are important determinants of health reimbursement decisions and are essential components of comparative effectiveness research. The American Society of Hematology (ASH) annual meeting is an important forum for presentation of economic analyses of hematology-oncology drugs. We hypothesized that economic analyses sponsored by pharmaceutical companies would be more likely to support that company's product. We conducted this study to determine the frequency of financial conflicts of interest in economic analyses presented at the ASH annual meeting and to examine whether such conflicts influenced study outcomes and directly or indirectly supported a specific product (an example of indirect support is a study on costs of febrile neutropenia sponsored by a pharmaceutical company that manufactures granulocyte colony-stimulating factor). ASH annual meeting abstracts from 2006-2008 were searched for economic analyses using following search terms: ‘cost', ‘economic', ‘dollar', ‘cost-effective', and ‘cost-benefit'. All abstracts in the ‘Health Services and Outcomes Research' category were also reviewed for economic analyses. Information was collected on the type of economic analysis, health technology assessed, author affiliation, the presence of conflict of interest and study conclusion. A total of 124 original economic analyses were identified. The majority of studies (52%) were conducted in the US, followed by Canada (11%) and UK (7%). Most studies were presented as a poster (61%). Eighty-seven studies (70%) evaluated a pharmaceutical product. First author affiliations included academic institutions (67%), consulting company employee (23%) and employee of sponsoring corporate (10%). Eighty-eight of 124 studies (71%) had at least one author with a financial conflict of interest. Studies with a conflict of interest were more likely to evaluate a pharmaceutical product than studies without a conflict of interest (81% vs. 44%, p<0.001). First authors of abstracts with a conflict of interest were less likely to be affiliated with an academic institution compared with abstracts without a conflict of interest (53% vs. 100%, p<0.001). The conclusions of 87 of 88 economic analyses with a conflict of interest favored the sponsor's product either directly (72%) or indirectly (27%). In conclusion, financial conflicts of interest are common in economic analyses presented at the ASH annual meeting. Almost all economic analyses with a financial conflict of interest support their sponsor's products. We could not exclude a publication bias, wherein economic analyses that did not favor a sponsor's product were less likely to be submitted for presentation. Economic analyses have important health policy implications and conflicts of interest should be carefully considered when interpreting the conclusions of economic analyses. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.182 | 0.565 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.005 |
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".