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Enregistrement W193065223 · doi:10.1212/wnl.80.7_supplement.p01.211

Comparison of Patients Treated with Natalizumab and Interferon-beta/Glatiramer Using Propensity-Matched Multiple Sclerosis Registry Data (P01.211)

2013· article· en· W193065223 sur OpenAlexaff
Timothy Spelman, Fabio Pellegrini, Annie Zhang, Robert Hyde, Amy Pace, Shibeshih Belachew, María Trojano, Heinz Wiendl, Ludwig Kappos, Freek Verheul, François Grand’Maison, Guillermo Izquierdo, Helmut Butzkueven, M. MSBase Australian Investigators, TOP Investigators

Notice bibliographique

RevueNeurology · 2013
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Sclerosis Research Studies
Établissements canadiensClinique Neuro-Outaouais
Organismes subventionnairesnon disponible
Mots-clésNatalizumabMedicinePropensity score matchingInternal medicineGlatiramer acetateObservational studyExpanded Disability Status ScaleMultiple sclerosisDiseaseImmunology

Résumé

récupéré en direct d'OpenAlex

OBJECTIVE: Investigate time to first multiple sclerosis (MS) relapse using matched patient samples from the contemporaneously recruited MSCOMET and TYSABRI ® Observational Program (TOP) cohorts. BACKGROUND: Comparison of treatments using nonrandomized data is difficult because imbalances in patient characteristics may introduce bias. Propensity score matching is a statistical technique used to correct for imbalance of known covariates in nonrandomly selected cohorts. DESIGN/METHODS: MSCOMET is a longitudinal MSBase registry substudy assessing patients treated with interferon beta (IFN) and glatiramer acetate (GA). TOP is a natalizumab observational registry. Currently, 694 patients from 14 countries (median follow-up time of 12 months) and 3976 patients from 15 countries (median follow-up time of 17 months) are enrolled in MSCOMET and TOP, respectively. Baseline characteristics used for 1:1 propensity matching included sex, age, disease duration, Expanded Disability Status Scale scores, and prebaseline treatment and relapse activity. Matching success was assessed via analysis of standardized differences, with the mean difference between matched groups expressed as a percentage of the average covariate standard deviation. Additional factors associated with time to first relapse were investigated using a clustered marginal Cox model. RESULTS: TOP patients (n=569) were matched to MSCOMET patients (n=569). For each variable the standardized difference was <10%, indicating excellent balance in all baseline characteristics. Among MSCOMET patients, relapse risk was increased 2.73-fold (95% CI, 2.10–3.55-fold) over TOP patients. In the nonmatched sample, relapse risk was 1.68-fold (95% CI, 1.10–2.19-fold) greater among MSCOMET than TOP patients. Within MSCOMET, relapse risk did not differ between GA- and IFN-treated patients. CONCLUSIONS: Natalizumab treatment was associated with a significantly reduced risk of relapse compared with IFN and GA in a contemporaneously and propensity matched comparison of patients across 2 observational studies. While inferior to randomized clinical trials, the propensity score matching technique could be useful when head-to-head randomized trial evidence is lacking. Supported by: Biogen Idec Inc. and Elan Pharmaceuticals, Inc. Disclosure: Dr. Spelman hs received research support from Novartis. Dr. Pellegrini has received personal compensation for activities with Biogen Idec. Dr. Zhang has received personal compensation for activities with Biogen Idec Inc. as an employee. Dr. Zhang holds stock and/or stock options in Biogen Idec. Dr. Zhang has received research support from Biogen Idec. Dr. Hyde has received personal compensation for activities with Biogen Idec as an employee. Dr. Hyde has received compensation for serving as international medical affairs director of Biogen Idec. Dr. Hyde holds stock and/or stock options in Biogen Idec. Dr. Pace receives personal compensation from Biogen Idec Inc. as an employee. Dr. Pace receives stock from Biogen Idec Inc. Dr. Belachew has received personal compensation for activities with Biogen Idec as an employee. Dr.Trojano has received personal compensation for activities with Sanofi-Aventis Pharmaceuticals, Inc., Biogen Idec, Novartis, and Bayer Schering. Dr. Trojano has received research supprot from Merck Serono, Biogen Idec, and Novartis. Dr. Wiendl has received personal compensation for activities with Bayer, Biogen Idec, Elan Corporation, Medac, Merck Serono, Novo Nordisk, Sanofi-Aventis Pharmaceuticals, Inc., Schering, Teva Neuroscience, Bayer Vital/Schering, and Novartis. Dr. Wiendl has received research support from Bayer, Biogen Idec, Elan Corporation, Medac, Merck Serono, Novo Nordisk, Medac, Sanofi-Aventis Pharmaceuticals, Inc., Schering, and Teva Neuroscience. Dr. Kappos has receied personal compensation for activities with Actelion, Advancell, Allozyne, BaroFold, Bayer Health Care Pharmaceuticals, Bayer Schering Pharma, Bayhill, Biogen Idec, BioMarin, CLC Behring, Elan, Genmab, Genmark, GeNeuro SA and GlaxoSmithKline. Dr. Kappos has received research support from has received research support from Acorda, Actelion, Allozyne, BaroFold, Bayer HealthCare, Bayer Schering, Bayhill Therapeutics, Biogen Idec, Boehringer Ingelheim, Eisai, Elan, Genmab, GlaxoSmithKline, Glenmark, Merck Serono, MediciNova and Nova. Dr. Verheul has received personal compensation for activities with Merck Serono, Biogen Idec, and Novartis. Dr. Grand-Maison has received personal compensation for activities with Sanofi-Aventis Pharmaceuticals, Inc., Bayer Pharmaceuticals, Serono Inc., Biogen Idec, Genzyme Corporation, and Novartis. Dr. Grand-Maison has received research support from Sanofi-Aventis Pharmaceuticals, Inc., Serono Inc., Biogen Idec, Genzyme Corporation, Bayer Pharmaceuticals, and Novartis. Dr. Izquierdo has received personal compensation for activities with Biogen Idec, Merck & Co., Inc., Bayer, Sanofi-Aventis Pharmaceuticals, Inc., Teva Neuroscience, and Novartis. Dr. Butzkueven has received personal compensation for activities with Biogen Idec, Novartis for scientific advisory boards, from Novartis, CSL (Australia) as a speaker, conference travel support from Biogen Idec, Novartis. Dr. Butzkueven has received research support from Merck Serono, Biogen Idec, Novartis. Dr. Spelman hs received research support from Novartis. Dr. Spelman hs received research support from Novartis.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,021

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,008
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,196
Tête enseignante GPT0,343
Écart entre enseignants0,147 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2013
Routes d'admission1
Résumé présentoui

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