Attitudes towards open‐label versus placebo‐control designs in oncology randomized trials: A survey of medical oncologists
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Randomized trials are considered the gold standard when assessing the efficacy of new therapeutic agents. In clinical situations where no standard of care therapy is approved, randomized trials usually compare experimental agents to either a placebo or an open-label nonintervention arm (i.e., best supportive care). We surveyed Canadian medical oncologists to understand their attitudes towards each design. METHODS: Members of the Canadian Association of Medical Oncologists were invited to participate in an anonymous online survey. Standardized case scenarios were used to determine participants' attitudes regarding the role of open-label versus placebo-controlled trials. RESULTS: A total of 322 medical oncologists and trainees were invited to participate and 86 responded (response rate 27%). Fifty-one (59%) believed that open-label trials are an acceptable alternative to placebo-controlled design when investigating a therapeutic agent in the adjuvant setting. Thirty-eight (49%) deemed it acceptable to compare the investigational agent to an open-label arm instead of a placebo to assess progression-free survival in the metastatic setting. Twenty-eight (38%) of respondents felt that open-label design was acceptable when assessing the quality of life endpoint. Most physicians were unsure whether the US Food and Drug Administration require a placebo-controlled arm in oncology trials. CONCLUSION: Canadian medical oncologists participating in this survey are divided in their opinions regarding the acceptability of an open-label design in randomized-controlled trials, where no standard therapy is approved. Clearer guidance from regulatory bodies on the adequacy of different trial designs is needed.
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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 | 0.147 | 0.265 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".