Oncologic Drugs Advisory Committee Recommendations and Approval of Cancer Drugs by the US Food and Drug Administration
Bibliographic record
Abstract
IMPORTANCE: The US Food and Drug Administration (FDA) advisory committees influence decisions relating to the regulatory approval of drugs in the United States. Outside of the field of oncology, prosponsor voting bias has been observed among members with financial conflicts of interest (FCOIs). OBJECTIVE: To explore factors associated with Oncologic Drugs Advisory Committee (ODAC) recommendations and the influence ODAC members' FCOIs on the drug approval process. DESIGN, SETTING, AND PARTICIPANTS: Retrospective analysis of 82 ODAC meeting transcripts between January 2000 and December 2014. Analysis was restricted to meetings at which votes were cast relating to oncologic drugs. The influence of methodology of trials supporting approval and frequency and type of self-reported FCOIs of voting members was explored using logistic regression. MAIN OUTCOMES AND MEASURES: ODAC recommendation for drug approval and subsequent FDA approval. RESULTS: Eighty-two transcripts of ODAC meetings between January 2000 and December 2014 were available for analysis. During the time period analyzed, ODAC members voted on 68 applications in 79 meetings (the remaining 3 meetings included voting questions regarding postmarketing safety or trial design). There was agreement between ODAC recommendations and final FDA approval; FDA approval was received for all 41 drugs that ODAC recommended approval. Additionally, the FDA approved 7 out of 41 agents that were not recommended for approval by ODAC (κ = 0.83). In 51 of 79 meetings, more than 1 trial was available to support the indication of a particular drug, and favorable ODAC recommendations were more likely when this was the case (odds ratio [OR], 1.82; 95% CI, 1.19-2.78; P = .01). Availability of randomized data did not appear to be important with selected single-arm phase 2 trials leading to recommendations for approval, especially in rare diseases. There has been a significant reduction in FCOIs over time (31 of 77 voting members [40%] in 2000 vs 0 of 20 voting members in 2014 [0%]; P < .001). Recommendations for approval were made in 28 of 47 meetings with members reporting FCOIs while among meetings with no reported FCOIs, recommendations for approval were made in 13 of 35 meetings (OR, 1.19; 95% CI, 0.97-1.46; P = .10). No significant association between ODAC recommendations and FDA approval was observed for members with FCOIs with the sponsor (OR, 1.79; 95% CI, 0.97-1.46; P = .19 and OR, 3.48; 95% CI, 0.84-14.35; P = .09, respectively) compared with members with FCOIs with competitors (OR, 1.06; 95% CI, 0.78-1.44; P = .72 and OR, 0.94; 95% CI, 0.69-1.28; P = .69, respectively). CONCLUSIONS AND RELEVANCE: Availability of multiple trials is associated with higher odds of ODAC recommendation and drug approval. Availability of randomized data appears less important. Declaration of FCOIs among ODAC members was frequent during the time period of interest but has decreased significantly over time. There is no apparent association between FCOIs and ODAC recommendations and subsequent FDA approval.
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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 | 0.084 | 0.363 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".