Assumptions of Expected Benefits in Randomized Phase III Trials Evaluating Systemic Treatments for Cancer
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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.
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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 | 0.033 | 0.213 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it