Pharmacological Acromegaly Treatment: Cost-Utility and Value of Information Analysis
Bibliographic record
Abstract
OBJECTIVES: To conduct a cost-utility analysis comparing drug strategies involving octreotide, lanreotide, pasireotide, and pegvisomant for the treatment of patients with acromegaly who have failed surgery, from a Brazilian public payer perspective. METHODS: A probabilistic cohort Markov model was developed. One-year cycles were employed. The patients started at 45 years of age and were followed lifelong. Costs, efficacy, and quality of life parameters were retrieved from the literature. A discount rate (5%) was applied to both costs and efficacy. The results were reported as costs per quality-adjusted life year (QALY), and incremental cost-effectiveness ratios (ICERs) were calculated when applicable. Scenario analyses considered alternative dosages, discount rate, tax exemption, and continued use of treatment despite lack of response. Value of information (VOI) analysis was conducted to explore uncertainty and to estimate the costs to be spent in future research. RESULTS: Only lanreotide showed an ICER reasonable for having its use considered in clinical practice (R$ 112,138/US$ 28,389 per QALY compared to no treatment). Scenario analyses corroborated the base-case result. VOI analysis showed that much uncertainty surrounds the parameters, and future clinical research should cost less than R$ 43,230,000/US$ 10,944,304 per year. VOI also showed that almost all uncertainty that precludes an optimal strategy choice involves quality of life. CONCLUSIONS: With current information, the only strategy that can be considered cost-effective in Brazil is lanreotide treatment. No second-line treatment is recommended. Significant uncertainty of parameters impairs optimal decision-making, and this conclusion can be generalized to other countries. Future research should focus on acquiring utility data.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".