Evaluating the Benefits of New Drugs in Health Technology Assessment Using Multiple Criteria Decision Analysis: A Case Study on Metastatic Prostate Cancer With the Dental and Pharmaceuticals Benefits Agency (TLV) in Sweden
Bibliographic record
Abstract
Background. Multiple criteria decision analysis (MCDA) has been identified as a prospective methodology for assisting decision makers in evaluating the benefits of new medicines in health technology assessment (HTA); however, limited empirical evidence exists from real-world applications. Objective. To test in practice a recently developed MCDA methodological framework for HTA, the Advance Value Framework, in a proof-of-concept case study with decision makers. Methods. A multi-attribute value theory methodology was adopted applying the MACBETH questioning protocol through a facilitated decision-analysis modelling approach as part of a decision conference with four experts. Settings. The remit of the Swedish Dental and Pharmaceutical Benefits Agency (Tandvårds- och läkemedelsförmånsverket [TLV]) was adopted but in addition supplementary value dimensions were considered. Patients. Metastatic castrate-resistant prostate cancer patients were considered having received prior chemotherapy. Interventions. Abiraterone, cabazitaxel, and enzalutamide were evaluated as third-line treatments. Measurements. Participants’ value preferences were elicited involving criteria selection, options scoring, criteria weighting, and their aggregation. Results. Eight criteria attributes were finally included in the model relating to therapeutic impact, safety profile, socioeconomic impact, and innovation level with relative importance weights 44.5%, 33.3%, 14.8%, and 7.4% per cluster, respectively. Enzalutamide scored the highest overall weighted preference value score, followed by abiraterone and cabazitaxel. Dividing treatments’ overall weighted preference value scores by their costs derived “costs per unit of value” for ranking the treatments based on value-for-money grounds. Limitations. Study limitations included lack of comparative clinical effects across treatments and the small sample of participants. Conclusion. The Advance Value Framework has the prospects of facilitating the evaluation process in HTA and health care decision making; additional research is recommended to address technical challenges and optimize the use of MCDA for policy making.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".