Evaluating the evidence behind the surrogate measures included in the FDA's table of surrogate endpoints as supporting approval of cancer drugs
Bibliographic record
Abstract
Background In July 2018, the FDA first published a table listing all surrogate measures that it has used, and may accept for future use, in regulatory approval. However, the strength of surrogacy for those measures was not formally assessed. Using the case example of breast cancer, we aimed to evaluate the strength of correlation of surrogate measures listed in the FDA's Table with overall survival. Methods This cross-sectional study of the FDA's Table of Surrogate Endpoints was conducted in May 2019. All surrogate measures listed in the FDA table as appropriate for accelerated or regular approval for breast cancer were extracted. We identified studies evaluating the correlation of treatment benefit in the surrogate with treatment benefit in overall survival and extracted results from the correlation analysis. Findings Five surrogate endpoints were listed for breast cancer in the FDA website: pathological complete response rates (pCR), event-free survival (EFS), disease-free survival (DFS), objective response rates (ORR), and progression-free survival (PFS), of which pCR was listed as appropriate only for accelerated approval, while the rest were considered appropriate for accelerated or regular approval. No correlation study evaluated the correlation of treatment effects on EFS with that on OS. The results from correlation studies evaluating pCR, DFS, ORR, and PFS suggest that the treatment effects on none of these surrogate measures were strongly correlated with treatment effects on OS ( r <0.85 or R 2 < 0.7, except for DFS in HER2 positive early breast cancer ( R 2 = 0.75) Interpretation Using breast cancer as an example, we evaluated the underlying evidence for the surrogate endpoints for solid tumors listed in the FDA's Table of Surrogate Endpoints and found weak or missing correlations of treatment effects on these surrogates with treatment effects on OS . Surrogate measures should be predictive of clinical benefit to be useful in supporting regular FDA approval. Funding Work on this project was funded by the Arnold Ventures. Dr. Kesselheim is also supported by the Harvard-MIT Center for Regulatory Science. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
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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 | 0.426 | 0.759 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".