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Record W4318832711 · doi:10.1007/s40273-023-01244-z

Filgotinib for Treating Moderately to Severely Active Ulcerative Colitis: An Evidence Review Group Perspective of a NICE Single Technology Appraisal

2023· review· en· W4318832711 on OpenAlexaff
Antoinette D. I. van Asselt, Nigel Armstrong, Merel Kimman, Andrea Peeters, Kevin McDermott, Lisa Stirk, Charlotte Ahmadu, Tim M. Govers, Frank Hoentjen, Manuela Joore, Sabine Grimm

Bibliographic record

VenuePharmacoEconomics · 2023
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of Alberta
FundersHealth Technology Assessment ProgrammeNational Institute for Health and Care Research
KeywordsNiceMedicineExcellenceHealth technologyCritical appraisalPopulationSystematic reviewHealth careFamily medicineHealth economicsPopulation healthMEDLINEPublic healthAlternative medicineNursingPathologyPolitical scienceEnvironmental health

Abstract

fetched live from OpenAlex

), as part of the Single Technology Appraisal process, to submit evidence for the clinical effectiveness and cost effectiveness of filgotinib for treating moderately to severely active ulcerative colitis in adults who have had an inadequate response, loss of response or were intolerant to a previous biologic agent or conventional therapy. Kleijnen Systematic Reviews Ltd, in collaboration with Maastricht University Medical Centre+, was commissioned to act as the independent Evidence Review Group. This paper summarises the company submission, presents the Evidence Review Group's critical review on the clinical and cost-effectiveness evidence in the company submission, highlights the key methodological considerations and describes the development of the National Institute for Health and Care Excellence guidance by the Appraisal Committee. The company submission included one relevant study for the comparison of filgotinib versus placebo: the SELECTION trial. As there was no head-to-head evidence with any of the comparators, the company performed two separate network meta-analyses, one for the biologic-naïve population and one for the biologic-experienced population, and for both the induction and maintenance phases. The Evidence Review Group questioned the validity of the maintenance network meta-analysis because it assumed all active treatments to be comparators in this phase, which is not in line with clinical practice. The economic analysis used a number of assumptions that introduced substantial uncertainty, which could not be fully explored, for instance, the assumption that a risk of loss of response would be independent of health state and constant over time. Company and Evidence Review Group results indicate that at its current price, and disregarding confidential discounts for comparators and subsequent treatments, filgotinib dominates some comparators (golimumab and adalimumab in the company base case, all but intravenous and subcutaneous vedolizumab in the Evidence Review Group's base case) in the biologic-naïve population. In the biologic-experienced population, filgotinib dominates all comparators in both the company and the Evidence Review Group's base case. Results should be interpreted with caution as some important uncertainties were not included in the modelling. These uncertainties were mostly centred around the maintenance network meta-analysis, loss of response, health-related quality-of-life estimates and modelling of dose escalation. The National Institute for Health and Care Excellence recommended filgotinib within its marketing authorisation, as an option for treating moderately to severely active ulcerative colitis in adults when conventional or biological treatment cannot be tolerated, or if the disease has not responded well enough or has stopped responding to these treatments, and if the company provides filgotinib according to the commercial arrangement.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.859
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.597
GPT teacher head0.575
Teacher spread0.022 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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