Design of placebo-controlled randomized trials of anticancer agents: Ethical considerations based on a review of published trials
Bibliographic record
Abstract
BACKGROUND: Limited information exists about the design of placebo-controlled cancer trials. Through a systematic review of trials published in 2013, we describe placebo use in randomized trials testing anticancer agents and analyze strategies that increase exposure to the experimental regimen. METHODS: Trials were classified as add-on (placebo in combination with standard treatment) or placebo-only. Strategies to allow more than half of the participants to receive the experimental regimen were reviewed. The risk-benefit ratio of receiving the experimental agent was considered favorable if the difference in primary outcome was significant (p ≤ 0.05), neutral if there was no significant difference in the primary outcome and the experimental agent did not add substantial toxicity, and unfavorable otherwise. RESULTS: Eighty trials were included (32,694 participants). Most trials were add-on (69%). The risk-benefit outcome was favorable, neutral, and unfavorable to the experimental agent in 52%, 32%, and 16% of placebo-only trials and 25%, 53%, and 22%, respectively, of add-on trials. Four strategies increased exposure to the experimental regimen: one-way crossover (23%), uneven randomization (21%), three-arms (13%), and randomized discontinuation design (4%); these strategies were used more often in placebo-only trials. CONCLUSION: A minority of participants received placebo alone and strategies to increase experimental exposure were used commonly. Fewer than half of the studies had favorable outcomes, thus defending the use of placebo controls, when there is no established treatment. Strategies that increase patient exposure to experimental agents rather than placebo may expose them to non-beneficial, sometimes toxic, experimental agents.
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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 | 0.631 | 0.753 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.012 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.019 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".