Comparison of outcomes of phase II trials (P2Ts) and subsequent randomized control trials (RCTs) using identical therapeutic regimens
Bibliographic record
Abstract
6000 Background: RCTs are cornerstones of evidence-based clinical oncology practice. RCTs are often based on promising results from P2Ts. However, it is not clear whether these results translate into positive RCTs. Methods: We searched for all RCTs of chemotherapy or combined chemotherapy and radiation therapy in solid malignancies published in the English language literature from July 1998 to June 2003. RCTs of neoadjuvant or adjuvant therapy were excluded. Eligible RCTs were reviewed to identify preceding P2Ts. To be included in the analysis, P2Ts and RCTs must have used identical therapeutic regimens in same patient populations. RCTs were considered to be positive if the experimental regimen was significantly better than the control in terms of primary endpoints. Response rates from both P2Ts and RCTs were retrieved. The following variables were also collected from P2Ts: number of patients, whether P2Ts were randomized and/or multi-centered, and the impact factor of the journal in which a P2T was published. Logistic regressions were performed to evaluate influences of these variables on outcomes of subsequent RCTs. Results: Of 181 RCTs identified, 47 used same therapeutic regimens as those in 59 preceding P2Ts. Ten (21.3%) of 47 RCTs are considered positive RCTs. Response rates were at least equal to those in P2Ts in only 9 (19.1%) RCTs. The mean difference in response rates between P2Ts and RCTs was 13% (absolute difference, range: 0 - 37.8%). The only statistically significant predictor of a positive RCT is the number of patients entered in preceding P2Ts. For every 10-patient increment in the size of a P2T, the odds of observing a positive RCT increases by 1.22 times (p = 0.042). Conclusions: Promising results from P2Ts frequently do not translate into positive RCTs. Response rates in most RCTs are lower than those in preceding P2Ts. The only significant predictor of a positive RCT is the number of patients in preceding P2Ts. 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 | 0.336 | 0.644 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.026 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".