Cost-effectiveness of golimumab for the treatment of patients with moderate-to-severe ulcerative colitis in Quebec using a patient level state transition microsimulation
Bibliographic record
Abstract
Objective: To conduct cost-effectiveness analyses comparing the addition of golimumab to the standard of care (SoC) for treatment of patients with moderate-to-severe ulcerative colitis (UC) who are refractory to conventional therapies in Quebec (Canada). Methods: An individual patient state transition microsimulation model was developed to project health outcomes and costs over 10 years, using a payer perspective. The incremental benefit estimates for golimumab were driven by induction response and risk of a flare. Flare risks post-induction were derived for golimumab from the PURSUIT maintenance trial and extension study, while those for SoC were derived from the placebo arms of the Active Ulcerative Colitis Trials (ACT) 1 and 2. Other inputs were derived from multiple sources, including retrospective claims analyses and literature. Costs are reported in 2014 Canadian dollars. A 5% annual discount rate was applied to costs and quality-adjusted life-years (QALYs). Results: Compared with SoC, golimumab was projected to increase the time spent in mild disease or remission states, decrease flare rates, and increase QALYs. These gains were achieved with higher direct medical costs. The incremental cost-effectiveness ratio for golimumab vs SoC was $63,487 per QALY. Limitations: The long-term flare projections for SoC were based on the data available from the ACT 1 and 2 placebo arms, as data were not available from the PURSUIT maintenance or extension trial. Additionally, the study was limited to only SoC and golimumab, due to the availability of individual patient data to analyze. Conclusion: This economic analysis concluded that treatment with golimumab is likely more cost-effective vs SoC when considering cost-effectiveness acceptability thresholds from $50,000–$100,000 per QALY.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".