Absolute benefits of experimental medical therapies in phase III randomized clinical trials (RCTs) in breast and colorectal cancer
Bibliographic record
Abstract
6513 Background: New treatments are approved based on results of RCTs, which have become larger and powered to detect smaller absolute benefits (Booth et al, J Clin Oncol 26, 2008).The purpose of this study was to evaluate temporal trends in absolute benefits of experimental medical therapies in RCTs. Methods: RCTs with 200 or more participants evaluating medical therapies for breast and colorectal cancer were identified by searching Medline from 1975 to 2007. For each trial, absolute benefits were determined as: (i) the difference in either 3-year event-free survival (EFS) or 5-year overall survival (OS) for adjuvant trials, or in median survival for metastatic trials, as per usual practice; and (ii) the area between time-to-event curves for the treatment arms relative to total area, up to a given time. Monthly incremental cost of new therapies approved by the FDA between 1995 and 2007 were determined for treatments for metastatic disease. Results: We identified 236 eligible RCTs, with 57% evaluating adjuvant treatments for breast (N=102) and colorectal cancer (N=33). Median absolute benefits of experimental treatments decreased substantially in the last decade (Table) and were in the range of only 1.3 to 2.7% when analyzed as the relative area between time-to-event curves. Among RCTs evaluating treatment for metastatic cancer there was no change in absolute benefits with time, but incremental monthly cost of new approved treatments increased with time (1995: median $U.S. 65; 2007: median $U.S. 6560, p<0.0001). Conclusions: Evaluating absolute benefit as the relative area between time-to-event curves does not depend on the shape of the curves and is preferred. Over time, RCTs evaluating adjuvant treatment for breast and colorectal cancer show decreasing absolute benefit. New costly treatments may not assure meaningful clinical benefits. [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 | 0.273 | 0.470 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".