Increasing the impact of budget impact analysis: incorporating uncertainty for decision-makers in small markets
Bibliographic record
Abstract
For decision-makers considering new medicines for reimbursement and public use, both value for money and affordability are important considerations. Whereas a cost-effectiveness model provides information about value for money, a budget impact assessment (BIA) is customized to a specific context and estimates the total investment needed; one part of affordability. Both analytic approaches have parameter uncertainty within them, yet comparatively little attention is given to parameter uncertainty in BIA. Currently, within BIA, uncertainty exploration is limited to point estimates for plausible scenarios, prompting the question: can a decision-maker be confident in point estimates? Within this paper, our intent is to revitalize the discussion of uncertainty in BIA. In the context of health technology assessments submitted to support reimbursement decision-making, we propose reliance on probabilistic sensitivity analysis conducted in the cost-effectiveness model. If assumptions made in a cost-effectiveness model are valid, probabilistic cost estimates from the model, with the same perspective adopted as the BIA, should also inform BIA. Mean and variance of population outcomes, given parameter uncertainty in model inputs, are estimable from model outputs. As sufficiently large random samples are drawn from a population, the distribution of sample means will follow an approximately normal distribution. Therefore, when drawing samples from the model to inform estimates of budget impact, the assumption of an approximately normal distribution for costs is reasonable. We propose that the variance in mean costs from the cost-effectiveness model also reflects the variance in budget impact estimates and should be used to estimate budget impact confidence intervals.
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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 | 0.090 | 0.265 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".