Contribution of economic evaluation to decision making in early phases of product development: A methodological and empirical review
Bibliographic record
Abstract
BACKGROUND: Economic evaluation as an integral part of health technology assessment is today mostly applied to established technologies. Evaluating healthcare innovations in their early states of development has recently attracted attention. Although it offers several benefits, it also holds methodological challenges. OBJECTIVES: The aim of our study was to investigate the possible contributions of economic evaluation to industry's decision making early in product development and to confront the results with the actual use of early data in economic assessments. METHODS: We conducted a literature research to detect methodological contributions as well as economic evaluations that used data from early phases of product development. RESULTS: Economic analysis can be beneficially used in early phases of product development for various purposes including early market assessment, R&D portfolio management, and first estimations of pricing and reimbursement scenarios. Analytical tools available for these purposes have been identified. Numerous empirical works were detected, but most do not disclose any concrete decision context and could not be directly matched with the suggested applications. CONCLUSIONS: Industry can benefit from starting economic evaluation early in product development in several ways. Empirical evidence suggests that there is still potential left unused.
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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 | 0.112 | 0.312 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".