S13 Methodological Considerations of Network Meta-Analyses: Improving Evaluation of Comparative Effectiveness and Safety of Advanced Therapies Treating Moderate to Severe Ulcerative Colitis
Notice bibliographique
Résumé
Background: The growing number of treatments for moderate to severe ulcerative colitis (UC) necessitates the use of network meta-analyses (NMAs) to better understand the comparative efficacy and safety of treatments not directly compared in clinical trials. However, different methods used across NMAs can make it difficult for reviewers to interpret their results. This work aims to provide an overview of key methodological considerations for conducting and reviewing NMAs in moderate to severe UC and suggests a preferred approach for the identified evidence base by refining methods from previous work. Methods: We reviewed NMAs published or submitted for health technology appraisals (TAs) through the National Institute for Health and Care Excellence (NICE) and identified method variations with implications for NMA results and their interpretation. We compared methodologies of previous NMAs to determine preferred methods to compare outcomes among randomized clinical trials evaluating biological therapies (adalimumab, golimumab, infliximab, ustekinumab, vedolizumab) or oral small molecules (filgotinib, ozanimod, tofacitinib) for adults with moderate to severe UC identified through systematic review. Results: We identified and implemented NMA methods relevant to our identified evidence base in UC based on NICE guidelines (Dias et al., 2011) and methodological decisions made in past TAs (NICE TA547, NICE TA342, NICE TA633, NICE TA828): (1) random-effects models with informative priors (Turner et al., 2015) instead of fixed-effect models; (2) analyzed clinical response and clinical remission jointly instead of as separate, uncorrelated endpoints; (3) adjusted maintenance data from treat-through trial designs to approximate re-randomized trial designs instead of splitting networks by trial design; and (4) compared adverse events (AEs) and serious AEs (SAEs) among both induction and longer-term maintenance phases, drawing conclusions from relative treatment effects instead of surface under the cumulative ranking (SUCRA) values. In this NMA with preferred methods, ozanimod had similar efficacy to all other authorized therapies and was significantly more efficacious than adalimumab for the induction of clinical response and clinical remission in bio-experienced patients. Vedolizumab was significantly superior to adalimumab for induction of clinical response and clinical remission in bio-experienced patients in a previous NMA using a fixed-effect model (ICER 2020), compared with no significant difference in our NMA with a random-effects model. We found no significant differences between any active therapy or placebo in AE or SAE rates for induction or maintenance. Based on SUCRA values, ozanimod ranked 2/12 for SAEs (second “best”) during maintenance, which was a marked improvement from analyses assessing the induction period only. Despite no evidence of actual relative treatment effect differences, this might point to ozanimod’s longer-term safety benefit, which was not captured in a previous NMA that only evaluated short-term safety (Lasa et al., 2022). Conclusions: Comparative results from NMAs can differ based on methods used. We refined our NMA using preferred methods from NICE technical guidelines relevant to trials identified in UC to facilitate a wholistic evaluation of the evidence base. Understanding the impact of different methodological choices on NMA results facilitates better interpretation of the comparative efficacy and safety of treatments to guide clinical decision-making.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,543 | 0,840 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,003 |
| Méta-épidémiologie (sens large) | 0,008 | 0,026 |
| Bibliométrie | 0,010 | 0,013 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,010 | 0,007 |
| Science ouverte | 0,007 | 0,006 |
| Intégrité de la recherche | 0,007 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».