Randomized trials in oncology stopped early for benefit: A systematic review
Bibliographic record
Abstract
6513 Background: Investigators finding a large treatment effect in an interim analysis may terminate a randomized trial (RCT) earlier than planned. A systematic review (Montori et. al., JAMA 2005; 294: 2203–2209) found that RCTs stopped early for benefit are poorly reported and may overestimate the true treatment affect. The extent to which RCTs in oncology stopped early for benefit share similar concerns remains unclear. Methods: We selected RCTs in oncology which had been reported in the original systematic review and reviewed the study characteristics, features related to the decision to monitor and stop the study early (sample size, interim analyses, monitoring and stopping rules), and the number of events and the estimated treatment effects. Results: We found 29 RCTs in malignant hematology (n=6) and oncology (n=23), 52% published in 2000–2004 and 41% in 3 high-impact medical journals (New England Journal of Medicine, Lancet, JAMA). The majority (79%) of trials reported a planned sample size and, on average, recruited 67% of the planned sample size (SD 31%). RCTs reported (1) the planned sample size (n=20), (2) the interim analysis at which the study was terminated (n=16), and (3) whether the decision to stop the study prematurely was informed by a stopping rule (n=16); only 13 reported all three. There was a highly significant correlation between the number of events and the treatment effect (r=0.68, p=0.0007). The odds of finding a large treatment effect (a relative risk < median of 0.54, IQR 0.3–0.7) when studies stopped after a few events (no. events < median of 54 events, IQR 22–125) was 6.2 times greater than when studies stopped later. Conclusions: RCTs in oncology stopped early for benefit tend to report large treatment effects that may overestimate the true treatment effect, particularly when the number of events driving study termination is small. Also, information pertinent to the decision to stop early was inconsistently reported. Clinicians and policymakers should interpret such studies with caution, especially when information about the decision to stop early is not provided and few events occurred. No significant financial relationships to disclose.
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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 | 0.650 | 0.977 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.151 | 0.023 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.005 | 0.005 |
| 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; both teacher heads 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".