Imbalance in treatment discontinuation without progression between experimental and control arms among randomized trials in advanced breast cancer.
Bibliographic record
Abstract
1110 Background: In Randomized Controlled Trials (RCT), treatment discontinuation (TD) can result in censoring. Imbalanced frequency of TD between arms could impact interpretation of results. Here, we quantify TD between the experimental and control arms among RCT supporting registration of drugs for advanced breast cancer. Methods: We identified RCT supporting US Food and Drug Administration's approval of drugs for advanced breast cancer between 2013 and 2023. We extracted data for the number of participants with TD for reasons other than disease progression (PD), death, or completion of treatment. Odds ratio (OR) for TD comparing experimental to control arms were determined for each trial. To assess progression-free survival (PFS) translation into an overall survival (OS) benefit, we calculated hazard ratio (HR) for PFS/OS. Then we assessed whether there was quantitative association between the OR for TD and the ratio of HR for PFS/OS. Quantitative significance was defined according to the criteria of Burnand. Results: Analysis included 22 RCT comprising 13853 participants and supporting approval for 18 distinct drugs. TD was reported in 2212 (16.0%) participants. Among these participants, the leading causes of TD were withdrawal of consent (27.8%), site termination (20.7%), adverse events (17.2%), and loss of follow-up (10.9%). There was a statistically significant imbalance in the experimental arm in 4 RCT (18%) (Table), which was observed for everolimus, ribociclib (with fulvestrant), alpelisib, and neratinib. The OR for TD showed quantitative, but not statistically significant negative association with the ratio of the HR for PFS/OS (Beta -0.393; p=0.18). Conclusions: TD without PD, death, or completion of treatment occurs in a substantial proportion of participants of several RCT supporting approval of drugs for advanced breast cancer. all the statistically significant imbalanced TD in the RCT were in experimental arms. Quantitative association between imbalanced TD and the ratio of HR for PFS/OS suggests imbalanced TD may impact ability to translate improvements in PFS to OS. Results of RCT with imbalance in TD should be interpreted cautiously. [Table: see text]
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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 | 0.510 | 0.632 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.013 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".