Association between Oncologic Drugs Advisory Committee (ODAC) members’ financial conflicts of interest (FCOIs) and recommendations for drug approval by the U.S. Food and Drug Administration (FDA).
Bibliographic record
Abstract
6582 Background: FDA advisory committees influence decisions relating to the regulatory approval of drugs in the United States. Little is known about whether ODAC members’ FCOIs affect the FDA’s oncologic drug approval process. Methods: We consulted the FDA website for transcripts from ODAC meetings between January 2000 and December 2014. We included all meetings at which drugs used for prevention, treatment, or palliation of cancer were discussed. We restricted our analysis to meetings at which yes/no votes were cast for at least one question relating to an oncologic drug. We collected data on drug name, prevalence of self-reported FCOIs of voting members, type of FCOIs (with sponsor or competitor) and the number of members recused or with waivers allowing discussion but no voting. Association between votes favoring a drug, final drug approval and FCOIs of ODAC members were explored using logistic regression. Results: Eighty transcripts were available for analysis. ODAC voted favouring the drug in 50% of cases. In 6% of cases, ODAC voted against a drug, but it was subsequently approved. At least one FCOI was declared for at least one voting committee member in 59% of votes. At least one member was recused or given a waiver in 14% and 6% of votes, respectively. There has been a significant reduction in the proportion of voting members with FCOIs over time (41% in 2000 vs. 0% in 2014, trend p < 0.001). Voting members with any FCOIs were more likely to vote in favor of a drug (OR 1.34, p = 0.04). There was a near-significant interaction between the presence and type of FCOIs; FCOIs with the sponsor were associated with higher odds of voting in favor of a drug compared to FCOIs with a competitor (OR 1.89 vs. 0.97, interaction p = 0.052). Similar results were seen for the association of ODAC members’ FCOIs and final FDA approval (OR 1.42, p = 0.03). Conclusions: FCOIs among voting members of ODAC are common, but have decreased significantly over time. FCOIs, especially with the sponsor, are associated with higher odds of ODAC recommendation and of final FDA approval of oncologic drugs.
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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 | 0.025 | 0.200 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".