Conducting clinical trials—costs, impacts, and the value of clinical trials networks: A scoping review
Notice bibliographique
Résumé
BACKGROUND: A significant barrier to conducting clinical trials is their high cost, which is driven primarily by the time and resources required to activate trials and reach accrual targets. The high cost of running trials has a substantial impact on their long-term feasibility and the type of clinical research undertaken. METHODS: A scoping review of the empirical literature on the costs associated with conducting clinical trials was undertaken for the years 2001-2015. Five reference databases were consulted to elicit how trials costs are presented in the literature. A review instrument was developed to extract the content of in-scope papers. Findings were characterized by date and place of publication, clinical disease area, and network/cooperative group designation, when specified. Costs were captured and grouped by patient accrual and management, infrastructure, and the opportunity costs associated with industry funding for trials research. Cost impacts on translational research and health systems were also captured, as were recommendations to reduce trial expenditures. Since articles often cited multiple costs, multiple cost coding was used during data extraction to capture the range and frequency of costs. RESULTS: A total of 288 empirical articles were included. The distribution of reported costs was: patient management and accrual costs (132 articles), infrastructure costs (118 articles) and the opportunity costs of industry sponsorship (72 articles). 221 articles reported on the impact of undertaking costly trials on translational research and health systems; of these, the most frequently reported consequences were to research integrity (52% of articles), research capacity (36% of articles) and running low-value trials (34% of articles). 254 articles provided recommendations to reduce trial costs; of these, the most frequently reported recommendations related to improvements in: operational efficiencies (33% of articles); patient accrual (24% of articles); funding for trials and transparency in trials reporting (18% of articles, each). CONCLUSION: Key findings from the review are: 1) delayed trial activation has costs to budgets and research; 2) poor accrual leads to low-value trials and wasted resources; 3) the pharmaceutical industry can be a pragmatic, if problematic, partner in clinical research; 4) organizational know-how and successful research collaboration are benefits of network/cooperative groups; and 5) there are spillover benefits of clinical trials to healthcare systems, including better health outcomes, enhanced research capacity, and drug cost avoidance. There is a need for more economic evaluations of the benefits of clinical research, such as health system use (or avoidance) and health outcomes in cities and health authorities with institutions that conduct clinical research, to demonstrate the affordability of clinical trials, despite their high cost.
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 | 0,104 | 0,429 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,005 | 0,006 |
| Bibliométrie | 0,035 | 0,042 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,018 | 0,014 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,006 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».