Cost-Effectiveness Analysis of Vedolizumab Compared with Other Biologics in Anti-TNF-Naïve Patients with Moderate-to-Severe Ulcerative Colitis in Japan
Bibliographic record
Abstract
BACKGROUND: Vedolizumab (VDZ) was approved by the Japanese Ministry of Health, Labor and Welfare in 2018 for the treatment of patients with moderate-to-severe active ulcerative colitis (UC). The comparative cost-effectiveness of VDZ compared with other biologics is unknown in Japan. This information could be useful for decision makers at the time of repricing biologics for the treatment of patients with moderate-to-severe UC. OBJECTIVE: The aim was to assess the cost-effectiveness of VDZ versus other branded biologics for the treatment of patients with moderate-to-severe UC who were anti-tumor necrosis factor (TNF)-naïve, from the Japanese public healthcare payer perspective. METHODS: A hybrid decision tree/Markov model was developed to predict the number of patients who achieved response and remission at the end of the induction phase and sustained it during the maintenance phase, translating this into quality-adjusted life-years (QALYs) and costs. Treatment-related adverse events, discontinuation and surgery, and their impact on QALYs and costs were also modeled. A systematic literature review and network meta-analysis were conducted to estimate the comparative efficacy of each treatment versus placebo. Rates of adverse events, surgery, surgery complications, and utilities were from the literature. Costs (2018 Japanese yen) were obtained from the Japanese National Health Insurance drug price list and medical fee table and local claims databases. Clinical and economic outcomes were projected over a lifetime and discounted at 2% annually. RESULTS: Over a lifetime, VDZ yielded greater QALYs and cost savings compared with golimumab and was cost-effective compared with adalimumab and infliximab (incremental cost-effectiveness ratios ¥4,821,940 and ¥4,687,692, respectively). Deterministic and probabilistic analyses supported the robustness of the findings in the base-case analysis, indicating that VDZ was either dominant or cost-effective in most scenarios and replications. The main limitations of this analysis include excluding tofacitinib and infliximab biosimilar as comparators, health-state utility estimates were obtained from population studies in the United Kingdom, and the impact of subsequent (i.e., second-line) biologic treatment was not evaluated. CONCLUSION: Our analysis suggests that VDZ is dominant or cost-effective compared with other branded biologics for the treatment of anti-TNF-naïve patients with moderate-to-severe UC in Japan.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".