Analysis of Regional Timelines To Set Up a Global Phase III Clinical Trial in Breast Cancer: The Adjuvant Lapatinib and/or Trastuzumab Treatment Optimization Experience
Bibliographic record
Abstract
Abstract Learning Objectives Discuss methods for improving the efficiency of global clinical trials. Explain the need for national regulatory authorities and collaborative cancer groups to initiate efforts to quicken the activation process in their countries. Describe the activation process of phase III studies and its complex and heterogeneous regulation across different geographic and economic regions. Purpose. This study measured the time taken for setting up the different facets of Adjuvant Lapatinib and/or Trastuzumab Treatment Optimization (ALTTO), an international phase III study being conducted in 44 participating countries. Methods. Time to regulatory authority (RA) approval, time to ethics committee/institutional review board (EC/IRB) approval, time from study approval by EC/IRB to first randomized patient, and time from first to last randomized patient were prospectively collected in the ALTTO study. Analyses were conducted by grouping countries into either geographic regions or economic classes as per the World Bank's criteria. Results. South America had a significantly longer time to RA approval (median: 236 days, range: 21–257 days) than Europe (median: 52 days, range: 0–151 days), North America (median: 26 days, range: 22–30 days), and Asia-Pacific (median: 62 days, range: 37–75 days). Upper-middle economies had longer times to RA approval (median: 123 days, range: 21–257 days) than high-income (median: 47 days, range: 0–112 days) and lower-middle income economies (median: 57 days, range: 37–62 days). No significant difference was observed for time to EC/IRB approval across the studied regions (median: 59 days, range 0–174 days). Overall, the median time from EC/IRB approval to first recruited patient was 169 days (range: 26–412 days). Conclusion. This study highlights the long time intervals required to activate a global phase III trial. Collaborative research groups, pharmaceutical industry sponsors, and regulatory authorities should analyze the current system and enter into dialogue for optimizing local policies. This would enable faster access of patients to innovative therapies and enhance the efficiency of clinical research.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".