Feasibility of randomized controlled trials (RCTs) for drugs approved by the United States Food and Drug Administration (FDA) based on single arm studies.
Bibliographic record
Abstract
e19115 Background: US FDA introduced an Accelerated Approval (AA) pathway in 1992 to expedite access to promising new drugs, based on the assumption that RCTs would delay introduction or prove unfeasible. One resulting trade-off is an “interim” compromise in acceptable level of evidence: such approvals have increasingly relied on Overall Response Rates (ORR) from Single Arm Studies (SAS). FDA requires confirmation of benefits for such drugs in future RCTs, but that requirement may take years and often never met. Methods: We pooled drugs approved by FDA over 5 years based on ORR observed in SASs for solid tumors. We calculated the differences in ORR between the newly approved drugs and existing standard of care for each cancer sub-type, and designed hypothetical RCTs necessary to detect that difference. RCTs were designed based on power of 0.80, α-error of 5% (two-sided), and 1:1 randomization, using PS software (Vanderbilt University). We estimated accrual time for the RCTs using disease incidence and annual death rates for each cancer subtype in USA using Surveillance, Epidemiology & End Results records. Results: 28 of 129 (22%) FDA approved drugs for solid tumors, from 2015-2019, were based on SAS. Median sample size of 107 patients per approval (range 26-550). Drugs were approved based on median ORR of 38.9% (range 13-78%), compared to median ORR of 24.4% (range 5-62%) for existing standard of care [median difference in ORR 14.9% (range 6-45%)]. Using established statistical standards, median sample size required to conduct RCTs was 206 patients (range 44-1724); based on a conservative accrual rate of 5% of all eligible US patients, for 22 of 24 approvals RCTs with ORR as primary endpoint could have been completed within a timeframe equal to or less than the time used for undertaking the SAS. Drugs for 4 indications of 28 had a lower ORR compared to existing standard of care, while lacking evidence of superiority in any other survival outcome, raising important concerns about the approval process. Conclusions: Feasibility of conducting RCTs with an ORR endpoint within an acceptable time-frame does not appear to be a practical constraint for an overwhelming majority of drugs approved by FDA based on SAS alone. This finding questions the necessity of accepting a lower bar for efficacy and toxicity while approving drugs using the AA pathway, especially when supported by clinical equipoise with existing standards of care. ORR was lower than existing standard of care for 4 indications, putting rationale for these approvals into question.
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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 | 0.673 | 0.763 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".