Using adaptive designs for decision making within the optima trial: optimal personalized treatment of early breast cancer using multi-parameter tests
Bibliographic record
Abstract
OPTIMA has an adaptive design seeking to advance development of personalised medicine in breast cancer by assessing the value of multi-parameter tests, such as Oncotype DX, in a UK population of intermediate risk. OPTIMA prelim, the feasibility phase, aims to recruit 300 patients to evaluate performance and health-economics of a number of multi-parameter tests to identify test(s) to be used in the main trial and to establish the acceptability to patients and clinicians of randomisation. Patients are randomised to the standard arm or to the "test-directed treatment" arm according to the result of Oncotype DX test. The decision to roll forward into the main trial will be determined by the willingness of patients to be randomised, concordance and cost of the multi-parameter tests. Cost-effectiveness models will be based on the model developed in preparation for the OPTIMA trial, updated with contemporary evidence from the feasibility study and appropriate external data, e.g. the Ontario OncotypeDX field evaluation (prospective cohort study). OPTIMA prelim opened in Sept 2012 and has 56 patients registered (46 randomised). TSC and DMEC agreed decision rules and encouraged external collaboration to provide additional confidence and power for any decisions. The success of OPTIMA relies on the integration of a multi-disciplinary team of methodologists, clinical experts and patients at all stages of the trial. The complexities of using adaptive design methodology and decision making to roll forward into the main trial are challenging but provide the most efficient use of patients and costs.
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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 | 0.009 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".