ArticlePharmacoEconomics2025
Cost-Utility Analysis of Treatment Sequences for Moderate-to-Severe Crohn's Disease.
Article in PharmacoEconomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe clinical pathway for patients with moderate-to-severe Crohn's disease (CD) typically includes sequential pharmacologic treatment as well as surgery, but positioning of different therapies within these sequences remains challenging. Cost-utility analysis rarely captures these sequences and does not incorporate registry data on long-term effectiveness. In this study, we aim to overcome these limitations.
methodsWe developed an individual state transition model with four health states (active disease, remission, and remission due to surgery and death), five sequential treatment lines, and surgery. Efficacy data from network meta-analyses (NMA) for biologic naive and biologic exposed patients were combined with Dutch registry data to forecast long-term benefit, calculate costs, and estimate utilities. Analyses had a Dutch societal perspective with a lifetime time horizon. Costs were reported in 2023 euros and discounted with 3%. Effects were reported in quality-adjusted life years (QALYs) and discounted with 1.5%. The cost-per-QALY threshold was €20,000. Deterministic analyses for the base case, three scenarios (including recently published trials or price declines for ustekinumab), and one-way sensitivity analysis were run with 30,000 patients. The probabilistic sensitivity analysis was conducted by sampling 1000 patients in 1000 model runs.
resultsWhen opting for step-up sequences, the most cost-effective sequence (out of 156 sequences) starts with either azathiopurine/6-mp or methotrexate and is followed by combination therapy (infliximab + azathioprine) when patients discontinue their first line owing to disease activity or discontinuation. The most cost-effective top-down sequence (out of 72) starts with combination therapy (infliximab + azathioprine). After two lines of treatment, differences in cost-effectiveness between biologics become smaller. To be equally cost-effective as anti-tumor necrosis factor (TNF) combination therapy, a price decline for ustekinumab (biosimilars) of 81% is required or 50% to become the preferred option after combination therapy. Validation against external data suggested good predictive capabilities of the model.
conclusionsIntegrating NMA and registry data improves the quality of cost-effectiveness models for treatment sequences in CD. This open-source model can be easily updated for future therapies and holds the potential to become a standard model for use in clinical guideline development and the economic evaluation of new drugs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.