Assessing the economics of biologic and small molecule therapies for the treatment of moderate to severe ulcerative colitis in Japan: a cost per responder analysis of upadacitinib
Bibliographic record
Abstract
AIM: Patients with moderately to severely active ulcerative colitis have an increasing number of advanced therapy options including several biologics and Janus kinase inhibitors. Though data on efficacy and safety of these advanced therapies are available, less is known about the potential economic implications of their utilization in Japan. We evaluated the relative value of these advanced therapies in Japan using a locally developed cost per responder model. METHODS: A model was developed using relevant clinical endpoints and treatment costs to calculate cost per responder of all advanced therapies used for moderately to severely active ulcerative colitis treatment in Japan. Cost per responder was assessed in biologic-naïve and biologic-exposed populations, respectively. The model incorporated induction and maintenance therapy pathways as patients progressed through based on efficacy rates (clinical response, clinical remission and endoscopic improvement). Total costs for induction and maintenance included: drug acquisition, drug administration and serious adverse event management (as necessary) for responders, with additional rescue treatment cost only for non-responders. RESULTS: Upadacitinib showed lower cost per clinical response and cost per clinical remission across both biologic-naïve and biologic-exposed populations with only one exemption in cost per clinical remission in biologic-naïve population. In addition, upadacitinib demonstrated lower cost per endoscopic improvement in both populations. Janus kinase inhibitors outperformed with lower cost per responder than other mediations across all outcomes and patient populations with the exception of tofacitinib for clinical remission in biologic-exposed UC population. LIMITATIONS: Comparative data used in this analysis have been derived from network meta-analysis, not from direct comparison. CONCLUSIONS: The results of this cost per responder analysis suggest upadacitinib is a cost-effective option for the first- and second-line treatment of moderately to severely active ulcerative colitis in Japan.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".