Setting the Bar in Phase II Trials: The Use of Historical Data for Determining “Go/No Go” Decision for Definitive Phase III Testing
Bibliographic record
Abstract
PURPOSE: Phase II trials aim to determine whether a cancer treatment is sufficiently promising to justify phase III study. Whether an agent is declared promising in a phase II trial depends on prespecified "null" and "alternative" rates of an outcome of interest such as tumor response. In some cases, the null must be determined with reference to historical data. We sought to determine the proportion of phase II trials that require historical data to establish the null and to determine how these historical estimates were derived. EXPERIMENTAL DESIGN: We conducted a systematic review of phase II trials published in the Journal of Clinical Oncology or Cancer in the 3 years to June 2005. Data were extracted following a prespecified protocol. RESULTS: We retrieved 251 papers, of which 117 were found to be ineligible; 70 of 134 included trials (52%) were defined as requiring historical data for design. Nearly half (32, 46%) of these papers did not cite the source of the historical data used, and just 9 (13%) clearly gave a single historical estimate as the rationale for the null. Trials that failed to cite prior data appropriately were significantly more likely to declare an agent to be active (82% versus 33%; P=0.005). No study incorporated statistical methods to account for either sampling error or possible differences in case mix between the phase II sample and the historical cohort. CONCLUSIONS: Many phase II trials require historical data to determine null response rates. Simple guidelines may improve design and reporting of such trials.
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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 | 0.742 | 0.905 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.009 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.015 | 0.037 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".