Assumptions of Expected Benefits in Randomized Phase III Trials Evaluating Systemic Treatments for Cancer
Bibliographic record
Abstract
BACKGROUND: In designing phase III randomized clinical trials (RCTs), the expected magnitude of the benefit of the experimental therapy (δ) determines the number of patients required and the number of person-years of follow-up. We conducted a systematic review to evaluate how reliably δ approximates the observed benefit (B) in RCTs that evaluated cancer treatment. METHODS: RCTs evaluating systemic therapy in adult cancer patients published in 10 journals from January 1, 2005, through December 31, 2009, were identified. Data were extracted from each publication independently by two investigators. The related-samples Sign test was used to determine whether the median difference between δ and B was statistically significant in different study subsets and was two-sided. RESULTS: A total of 253 RCTs met the eligibility criteria and were included in the analysis. Regardless of whether benefit was defined as proportional change (median difference between δ and B = -13.0%, 95% confidence interval [CI] = -21.0% to -8.0%), absolute change (median difference between δ and B = -8.0%, 95% CI = -9.9% to -5.1%), or median increase in a time-to-event endpoint (median difference between δ and B = -1.4 months, 95% CI = -2.1 to -0.8 months), δ was consistently and statistically significantly larger than B (P < .001, for each, respectively). This relationship between δ and B was independent of year of publication, industry funding, management by cooperative trial groups, type of control arm, type of experimental arm, disease site, adjuvant treatment, or treatment for advanced disease, and likely contributed to the high proportion of negative RCTs (158 [62.5%] of 253 studies). CONCLUSIONS: Investigators consistently make overly optimistic assumptions regarding treatment benefits when designing RCTs. Attempts to reduce the number of negative RCTs should focus on more realistic estimations of δ. Increased use of interim analyses, certain adaptive trial designs, and better biological characterization of patients are potential ways of mitigating this problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.756 | 0.881 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.017 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".