A systematic review of economic evaluations for <i>RPE65</i>-mediated inherited retinal disease including HTA assessment of broader value
Bibliographic record
Abstract
OBJECTIVE: To summarize the key methodological challenges identified by health technology assessment (HTA) agencies assessing gene therapy (GT) and consideration of broad elements of value. METHOD: -mediated inherited retinal disease (IRD) published in English were selected. HTA evaluations from Australia, Canada, Ireland, Scotland, England, and the United States were reviewed. An existing methodological framework was used to identify the challenges and considerations. RESULTS: Eight unique EEs were identified of which six were evaluated by HTA agencies. Incremental cost-effectiveness ratios ranged from $68,951 to $643,813 per quality-adjusted life-years (QALY) gained (healthcare perspective) and dominant to $480,130 per QALY gained (societal perspective). The key challenges were the lack of validated surrogate outcome, utility values and indirect costs from IRD patients, and limited evidence of the long-term treatment effect. Two HTA agencies reviewed a range of novel broader elements of value and whether they were associated with VN while other agencies discussed some elements of broader value. Caregiver disutility was included in some, but not all, evaluations. CONCLUSION: The methodological challenges were consistent with innovative interventions for rare diseases and managed using standard methods. Broader value was important to decision-makers but inconsistently applied across agencies. Possible reasons are limitations in the evidence available of the broader benefits that VN offers and how to incorporate these within an EE. A need exists for greater guidance and consistency across jurisdictions regarding the consideration of broader value that considers latest best practice.
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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 | 0.034 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".