Estimation of historical control rate for a single arm de-escalation study – Application to the POSITIVE trial
Bibliographic record
Abstract
BACKGROUND: Although randomized controlled clinical trials are optimal to evaluate the effect of an experimental therapy, single-arm trials are required whenever randomization is unethical or not feasible, such as de-escalation studies. We propose using prospectively identified historical controls to place results of single-arm, de-escalation trials into context. METHODS: POSITIVE is a prospective, single-arm study in young women with hormone-receptor-positive early breast cancer to determine if temporarily interrupting adjuvant endocrine therapy in order to become pregnant increases the risk of a breast cancer event. After 272 women enrolled in POSITIVE, we identified a cohort of 1499 SOFT/TEXT patients potentially eligible to enroll in POSITIVE who did not interrupt endocrine therapy. Method I used the SOFT/TEXT cohort to calculate annualized hazard rates by a piecewise exponential model. Method II used the SOFT/TEXT cohort to group-match SOFT/TEXT patients to POSITIVE patients; sample sets of SOFT/TEXT patients were randomly drawn 5000 times to obtain sets having patient, disease, and treatment characteristics more balanced with POSITIVE participants. RESULTS: Compared with SOFT/TEXT, POSITIVE participants were younger, less likely to be overweight/obese, had fewer positive nodes, and fewer received aromatase inhibitor or chemotherapy. The estimated 3-year breast cancer free interval event rates were 9.5% (95% CI: 7.9%,11.1%) for Method I and 9.4% (95% CI: 7.8%,10.9%) for Method II, compared with 5.8% initially assumed when POSITIVE was designed. CONCLUSION: External control datasets should be identified before launching single-arm, de-escalation trials and methods applied during their conduct to provide context for interim monitoring and interpretation of the final analysis.
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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 | 0.376 | 0.513 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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; 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".