Expected value of information and decision making in HTA
Bibliographic record
Abstract
Decision makers within a jurisdiction facing evidence of positive but uncertain incremental net benefit of a new health care intervention have viable options where no further evidence is anticipated to: (1) adopt the new intervention without further evidence; (2) adopt the new intervention and undertake a trial; or (3) delay the decision and undertake a trial. Value of information methods have been shown previously to allow optimal design of clinical trials in comparing option (2) against option (1), by trading off the expected value and cost of sample information. However, this previous research has not considered the effect of cost of reversal on expected value of information in comparing these options. This paper demonstrates that, where a new intervention is adopted, the expected value of information is reduced under optimal decision making with costs of reversing decisions. Further, the paper shows that comparing expected net gain of optimally designed trials for option (2) vs (1) conditional on cost of reversal, and (3) vs (1) conditional on opportunity cost of delay allow systematic identification of an optimal decision strategy and trial design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".