Analytic approaches for informing research funding decisions : an exploration of their role and value using case studies of cancer clinical trials
Bibliographic record
Abstract
Patient-level evidence obtained from clinical trials is essential in assessing the cost-effectiveness of health care technologies. Given the increasing demand for primary evidence and limited public resources for health care research, research funding organisations are routinely called to make decisions on which clinical trials to fund. Such decisions need to be informed by evidence on the likely costs and benefits of competing research programmes. Two main analytic approaches have been proposed to provide such evidence, ‘payback of research’ and ‘value of information’. \nThis work applied the ‘payback’ and ‘value of information’ methodologies to case studies representing proposals for clinical trials in cancer. This application gave estimates of the value of undertaking the trials and offered an insight into the strengths, limitations and usefulness of the methods. \n‘Payback of research’ and ‘value of information’ can help with different funding decisions in the context of different funding streams, they are practical to undertake and can be readily incorporated into the existing research funding processes. It is suggested that the methods should be used as part of existing deliberative processes, to provide additional assurance that limited public resources are allocated to clinical trials which are likely to result in benefits to the population. \n
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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 | 0.246 | 0.503 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".