The use of early decision modelling and value of information analysis in an adaptive trial design: results from the OPTIMA preliminary study
Bibliographic record
Abstract
The use of decision modelling early in the research and development process for new healthcare technologies may improve research efficiency. Value of information analysis (VOIA) provides a useful tool for assessing the value of conducting further research. To test the feasibility of early modelling within an adaptive randomised controlled trial (RCT), where analysis of preliminary trial data is used to inform a stop-go decision and subsequent trial design. The OPTIMA prelim trial randomised patients with early breast cancer to standard care or test-directed care using Oncotype DX. Additional testing was conducted using five alternative competing multi-parameter tests. A probabilistic decision model was built to assess the cost-effectiveness. VOIA was used to assess the optimal ongoing research strategy to inform an NHS reimbursement decision. 302 patients were randomised and available for analysis. The cost-effectiveness results suggested multi-parameter tumour testing was likely to be cost-effective. VOIA was able to prioritise tests for inclusion within the ongoing RCT despite the rapid turnaround time required for analysis. The results were highly dependent on modelling assumptions that were unavoidable early in the test development pipeline. Despite difficulties in communicating the unfamiliar concepts underpinning VOIA to the Trial Management Group, it was seen as an informative tool that influenced design decisions. Early economic decision modelling and VOIA provides a novel approach to aid the trial design decision making process. It should be considered in future research proposals as a means of improving the return on public research investment within the NHS.
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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 | 0.054 | 0.360 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".