Forecasting Pharmaceutical Prices for Economic Evaluations When There Is No Market: A Review
Bibliographic record
Abstract
BACKGROUND: Economic evaluation helps policy makers and healthcare payers make decisions on drug listing, coverage, and reimbursement. When economic evaluations are conducted before a product launch, the prices of the pharmaceuticals have to be forecast. OBJECTIVE: The aim of this study was to examine the methods of establishing proxy prices and their accuracies compared with actual market prices after the product launch. METHODS: We searched the literature for evaluations for drugs that were licensed in the US between 2010 and 2015. We reviewed the studies for the forecasting strategies used, and then estimated the difference between actual 2016 post-launch prices and what the proxy prices would be if the forecast was carried out in the US in 2016. RESULTS: We identified six such studies, with seven drugs. Four studies used substitute drugs as proxies for the study drug, and three used other methods. The range of the values of actual minus proxy price varied considerably, and no trend was observed. CONCLUSION: Forecasting drug prices is as precarious as forecasting in other areas of the economy. We urge caution in reviewing and accepting a cost-effectiveness ratio that is based on forecast prices.
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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 | 0.027 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".