The Consistency and Transferability of Economic Evaluations of Disease-Modifying Treatments for Multiple Sclerosis: Analysis of Cost-Effectiveness Assessments from Australia, England, Canada and Scotland
Bibliographic record
Abstract
OBJECTIVE: Our study examines the consistency and transferability of cost-effectiveness values, specifically the incremental cost-effectiveness ratio (ICER), focusing on several disease-modifying therapies (DMTs) for relapsing-remitting multiple sclerosis (RRMS) approved by four of the most established Health Technology Assessment (HTA) bodies. METHODS: Data of economic evaluation results for DMTs were extracted from the HTA reports published by four agencies. Descriptive statistics were employed to identify correlations between the accepted ICERs within and across agencies. Two different currency conversion approaches were employed to investigate the feasibility and efficiency for transferring accepted ICERs across jurisdictions. RESULTS: The study analysed ten DMTs that received positive recommendations from four agencies: the National Institute for Health and Care Excellence (NICE), Scottish Medicines Consortium (SMC), Pharmaceutical Benefits Advisory Committee (PBAC) and Canada's Drug Agency (CDA). The consistency of accepted ICER values for the class of DMTs was observed within the four agencies; however, the commonality of accepted ICER values was only observed between some agencies, such as the CDA and NICE, as well as the PBAC and SMC. Purchasing power parity-adjusted ICER values were found to provide better transference of ICER outcomes as compared to the exchange-rate method. CONCLUSION: This study highlighted the consistency of the economic evaluation for DMTs within the four agencies. We also identified a particularly strong alignment between NICE and the CDA, as well as the PBAC and SMC. Additionally, the examination of two ICER value conversion methods pinpointed key factors that could impact the transference of ICER values.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".