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Enregistrement W1996446569 · doi:10.1002/art.24355

Now that we know what's BeSt, what is good value for the money?

2009· letter· en· W1996446569 sur OpenAlexaffabout
Nick Bansback, Carlo A. Marra

Notice bibliographique

RevueArthritis Care & Research · 2009
Typeletter
Langueen
DomaineMedicine
ThématiqueRheumatoid Arthritis Research and Therapies
Établissements canadiensUniversity of British ColumbiaProvidence Health Care Research Institute
Organismes subventionnairesnon disponible
Mots-clésValue (mathematics)Need to knowValue for moneyBest valueEconomicsComputer scienceMathematicsBusinessStatisticsComputer securityPublic economicsMarketing

Résumé

récupéré en direct d'OpenAlex

Rheumatoid arthritis (RA) affects 1% of the population and is associated with disability and joint damage leading to reduced health-related quality of life (HRQOL), productivity loss, increased health care resource utilization, and premature mortality. Previous studies have shown that treatment for RA should be initiated as rapidly as possible and maintained to reduce the occurrence of irreversible joint damage (1). In the Tight Control of Rheumatoid Arthritis study (2), the impact of intensive management of patients with RA with frequent monitoring and drug changes in those who still had active disease was found to be much more effective than routine care. The Behandelstrategieen voor Reumatoide Artritis (BeSt) study advanced the evidence base with the inclusion of new treatment options and has become a pivotal study for changing treatment practice. However, implementing new treatment paradigms, particularly ones associated with increased initial costs, requires funding. In an economic climate of scarce resources, justifying rising costs is increasingly important. In this issue of Arthritis Care & Research, van den Hout et al (3) report on the economic evaluation that was conducted in conjunction with the BeSt study, a randomized clinical trial of 508 patients over 12 months (4). The study collected a plethora of economic outcomes. Resource utilization diaries including medications, physician visits, and hospitalizations were prospectively collected. Health state utility values (HSUVs), which are used to incorporate the impact of HRQOL in cost-effectiveness calculations, were measured using preference-based instruments, and changes in paid and unpaid productivity predominantly through absenteeism were collected using questionnaires. Appropriate and sophisticated methodology (5) was then used to investigate which of the 4 treatment arms was the most cost-effective. By collecting such a wealth of information and implementing the analysis, it sets a new benchmark for designing clinical trials in RA in an era of economic constraints. But how should policymakers, physicians, and patients interpret the results of this study? A primary result of this 24-month study is that the initial combination arm with infliximab generated the most quality-adjusted life years (QALYs; given no deaths within this time period, this essentially meant all improvements in QALYs were due to improved HRQOL), followed closely by the initial combination arm with prednisone (3). When comparing the costs between the 2 treatment strategies through reduced health care resources and improved productivity, in one analysis the infliximab arm was found to be cost saving. When a strategy gives more benefit at reduced cost, it is known as a dominating strategy. However, this is not the end of the story. Another analysis shows that, for the same comparison, the infliximab strategy has an incremental cost-utility ratio (ICUR) of €130,000 per QALY when compared with the prednisone strategy. How can the result vary so much? First, in economic evaluation, different methods exist in how to value economic units. For valuing productivity loss, 2 methods are commonly used: the human capital approach and the friction cost method. For example, using the friction cost method, the ICUR for the initial combination therapy containing infliximab strategy to the initial combination therapy containing prednisone strategy was €130,000 (95% confidence interval [95% CI] €27,000, €3,000,000) per QALY compared with €22,000 (95% CI € 330,000, €1,500,000) per QALY. In contrast to many RA clinical studies, a number of HSUVs were measured directly in the study. These HSUVs permitted extensive secondary analyses using these different methods in calculating QALYs. The fact that the different methods of assessing HSUVs came up with similar patterns of results was reassuring, although the ICURs differed, which is consistent with other findings (6). It does not answer which HSUV should be believed. Second, there are varying perspectives on which costs and outcomes to include. The societal perspective is commonly cited as the most appropriate method for conducting economic evaluation. However, many reimbursement authorities state in their guidelines that the payer perspecNick Bansback, MSc: Providence Health Research Institute, Vancouver, British Columbia, Canada; Carlo A. Marra, PharmD, PhD: University of British Columbia and Providence Health Research Institute, Vancouver, British Columbia, Canada. Address correspondence to Carlo A. Marra, PharmD, PhD, 2146 East Mall, University of British Columbia, Vancouver, British Columbia, Canada, V6T 1Z3. E-mail: carlo. marra@ubc.ca. Submitted for publication November 21, 2008; accepted in revised form December 8, 2008. Arthritis & Rheumatism (Arthritis Care & Research) Vol. 61, No. 3, March 15, 2009, pp 289–290 DOI 10.1002/art.24355 © 2009, American College of Rheumatology

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)0,013
score de la tête « metaresearch » (Gemma)0,081
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,022
Score d'incertitude au seuil0,073

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0130,081
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0030,001
Bibliométrie0,0030,003
Études des sciences et des technologies0,0040,011
Communication savante0,0210,025
Science ouverte0,0020,004
Intégrité de la recherche0,0120,017
Charge utile insuffisante (le modèle a refusé de juger)0,0220,006

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 Opus0,051
Tête enseignante GPT0,353
Écart entre enseignants0,303 · 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'étudeSans objet
Domainenon disponible
GenreCommentaire

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
Publié2009
Routes d'admission2
Résumé présentoui

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