Dangerous Omissions: The Consequences of Ignoring Decision Uncertainty
Bibliographic record
Abstract
Purpose: To demonstrate that decisions based on expected net benefit (NB) will be inappropriate when agencies cannot commission or mandate additional research. To establish the opportunity costs of decision uncertainty and illustrate their impact on appropriate decision rules in cost-effectiveness analysis. Background: The decision to adopt a technology should be based on expected NB and the uncertainty around that estimate should inform the simultaneous decision to demand or commission further research. However, institutions with the remit for making adoption decisions often lack the remit to demand that further research is conducted and are separated from those responsible for commissioning research. Such decision-makers do not directly control future research, so the adoption decision is their only policy instrument. Methods: The prospects of acquiring further evidence about a technology are not independent of the adoption decision. The incentives for conducting future research can be reduced and further experimental research may be regarded as unethical once a technology is adopted. The opportunity cost of adoption should include the forgone expected value of information (EVI) for current and future patients. The opportunity costs of rejecting an apparently cost-effective technology will be the additional NB forgone by current and future patients. Using decision theory and value of information analysis we are able to establish appropriate decision rules which take account of these opportunity costs and an assessment of the uncertainty surrounding when new evidence may become available. Results: We show that in many circumstances the opportunity costs of adoption (EVI forgone) can exceed the opportunity costs of rejection (NB forgone): a technology which appears cost-effective should be rejected. We show that the incremental cost-effectiveness ratio ICER must always be less than the threshold and must be reduced to compensate for more decision uncertainty. This introduces incentives on sponsors to provide more evidence or reduce the price of technologies. We also integrate over the uncertainty surrounding the arrival of further evidence and present appropriate decision rules in terms of expected opportunity loss. Conclusions: Decisions based on expected NB do not account for the full opportunity costs of a decision to adopt a technology. Failure to account for these opportunity costs results in decision rules that are too permissive and will undermine the evidence base for clinical practice.
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 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 | 0.219 | 0.574 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".