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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".