De-escalation trials do not always need to be noninferiority: a case for superiority design de-escalation trials in oncology
Bibliographic record
Abstract
De-escalation trials in oncology have received increased attention recently because there is a growing concern that patients with cancer are being overtreated-more patients (than would benefit) are being treated, earlier in the disease course, at a higher dose, for a longer duration, at a higher frequency. Thus, it is important to understand if less treatment allows us to achieve similar outcomes, a strategy referred to as de-escalation of treatment. However, one of the major concerns with such de-escalation strategies is the possibility of compromising treatment efficacy. Although de-escalated treatment with lesser therapeutic burden is a worthwhile goal in itself because it leads to less physical, financial, and time toxicities, de-escalation cannot come at a substantial compromise of treatment efficacy. The commonest way to test whether such de-escalation strategies are safe and do not lead to unacceptable compromise in efficacy is through a noninferiority design trial. Such trials require a larger sample size and, thus, more funding and longer time to be completed. However, de-escalation trials do not necessarily need to be a noninferiority design. In this article, I make a case for using a superiority design to test de-escalation strategies. This will avoid the limitations of noninferiority design and make de-escalation strategies more efficient to test.
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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 | 0.575 | 0.724 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 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".