Use of placebo in cancer medicine: The experience of a National Clinical Trials organization
Bibliographic record
Abstract
6104 Background: The use of placebos in cancer clinical trials requires careful evaluation. Factors that must be considered include the impact of placebo on endpoint measurement, the efficacy of placebo relative to standard of care treatment, patient altruism/acceptance of a non-active intervention and the resulting increase in complexity of study conduct with respect to randomization, drug supply, data management, analysis and the unblinding process. Methods: We reviewed the experience of the National Cancer Institute of Canada Clinical Trials Group with the use of placebo in the randomized phase III setting from 1982–2005. Results: Since 1982, 34 studies were identified that utilized a placebo as part of study design. Data is presented below according to the type of study and date of study activation. The numbers in brackets represent those studies in which placebo was used alone in the control arm. Supportive care studies were the most common type of study employing a placebo as part of study design and constituted almost 50% of our Group’s experience. Therapeutic studies involving placebo were conducted in multiple sites including breast (4), lung (6), myeloma (1), melanoma (1), ovary (1) and pancreas (1). Conclusion: Phase III studies involving a placebo constitute an important part of our clinical trial activity and cross the spectrum of supportive care, therapeutic and prevention trials. The use of placebo in cancer studies may increase due to the relative ease of blinding in studies that evaluate targeted, oral therapies with minimal toxicities as well as the need for unbiased assessment of increasingly used endpoints such as time to progression. [Table: see text] No significant financial relationships to disclose.
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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 | 0.657 | 0.524 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".