Systematic Reviews and Economic Evaluations in Tecchnology Appraisals Conducted for Nice in the UK: A Game of Two Halves?
Notice bibliographique
Résumé
Rationale: Decision analytic models,as used in economic evaluations,require data on several clinical parameters.The gold standard approach is to conduct a systematic review of the relevant clinical literature, although reviews of economic evaluations indicate that this is rarely done.Technology appraisals for the National Institute for Health and Clinical Excellence (NICE), which are fully funded, represent the best case scenario for the close integration of economic evaluations and systematic reviews. Objectives: To assess the extent to which the systematic review of the clinical literature informs the economic evaluation in NICE technology appraisals Methods: All NICE technology assessment reports (TARs) published between January 2003 and July 2006 were considered. Data were abstracted on the TAR topics, the primary measure of clinical effectiveness, the approach to pooling in the clinical review, the measure of economic benefit and the use, or non-use, of the systematic review in the economic evaluation. Results: Forty four TARs were published in the period studied, all of which contained a systematic review. Most of the economic evaluations (40) were cost-utility analyses, reflecting NICE's guidelines for economic evaluation. The other analyses were cost-effectiveness analyses (2) and cost-minimisation studies (2). In 21 cases the clinical data were not pooled in the review, owing to heterogeneity in the clinical data or the limited number of studies. In these cases the economists used alternative approaches for estimating the key effectiveness parameter in the model. The results of the review (when pooled) were always used when the primary clinical effectiveness measure corresponded with the measure of economic benefit (eg survival). However, since preferenced-based quality of life measures are rarely included in clinical trials, the results of the systematic review were never directly used in the cost-utility analyses. Nevertheless, the outputs of the systematic review were used when the data were useful in estimating components of the QALY (eg the life-years gained, or the frequencies of health states to which QALYs could be assigned). Problems occurred mainly when the clinical data were not pooled, or when the measure of clinical benefit could not be converted into health states to which QALYs could be assigned. Conlusions: Economic evaluations can benefit from systematic reviews of the clinical literature. However, such reviews are not a panacea for conducting a good economic evaluation. Much of the relevant data for estimating QALYs are not contained in such reviews and the chosen method for summarising the clinical data may inhibit the assessment of economic benefit. Problems would be reduced if those undertaking the various components of technology appraisals discussed the data requirements for the economic model at an early stage.
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 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 | 0,761 | 0,903 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,009 |
| Méta-épidémiologie (sens large) | 0,017 | 0,008 |
| Bibliométrie | 0,027 | 0,029 |
| Études des sciences et des technologies | 0,005 | 0,054 |
| Communication savante | 0,039 | 0,058 |
| Science ouverte | 0,012 | 0,026 |
| Intégrité de la recherche | 0,047 | 0,031 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,019 | 0,006 |
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.
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 ».