Evaluation of the Medical Research Future Fund Clinical Trials Activity: A report on the review or the Medical Research Future Fund’s (MRFF) Clinical Trials Activity Initiative
Notice bibliographique
Résumé
Background: The Medical Research Future Fund (MRFF) is a research fund set up by the Australian Government in 2015 to support health and medical research in Australia. The Clinical Trials Activity (CTA) Initiative was established in 2016. Early funding priorities included rare cancers, rare diseases and unmet need, childhood brain cancer, reproductive cancers, and neurological disorders. The Department of Health and Aged Care (the department) contracted the Institute for Evidence-Based Healthcare (IEBH), at Bond University, to conduct an evaluation of MRFF’s CTA Initiative, to assess its progress in achieving the objectives set out in the MRFF 10-year Investment Plan in accordance with the MRFF Monitoring, evaluation and learning strategy, 2020-21 to 2023-24, and to guide future investments in clinical trials activity through the MRFF. The intention of the evaluation of the MRFF Clinical Trials Activity Initiative was to: • consider all existing investments on clinical trials made through the MRFF (e.g., progress made through MRFF funded projects) • consider approaches and the current landscape for clinical trials internationally and nationally in Australia • suggest opportunities for improving funding and granting arrangements for clinical trials through the initiative and the MRFF more broadly. Methods: To collect data for the evaluation, we used three complementary methods. 1. Desktop Review Data Set A desktop review compared MRFF-funded trials with trials funded by comparable funders, including: the National Health and Medical Research Council (NHMRC) in Australia and their subset of trials in the Clinical Trials and Cohort Studies Scheme (CTCS), the National Institute for Health and Care Research (NIHR) in the United Kingdom, Canadian Institute of Health Research (CIHR) in Canada, and the National Institutes of Health (NIH) in the United States. The data was derived from items in the clinical trial registries including the Australian New Zealand Clinical Trials Registry (ANZCTR) and the NIH’s National Library of Medicine. 2. Survey Data Set For each MRFF and NHMRC CTCS-funded grant, we sought two responses – one from the Chief Investigator A (CI-A) and one from an Early to Mid-Career Researcher (EMCR). The survey was open for completion between 4 October and 24 November 2022. 3 Stakeholder Consultation Data Set To supplement the findings of the Desktop Review (Project 1) and the Survey (Project 2), we conducted interviews with key stakeholders to better understand the key factors contributing to success of funded trials – including recruitment, follow up, and publication. The interviews included comments on data from the Desktop Review (Project 1) and Survey (Project 2), as well as questions about the MRFF Clinical Trial Activity Initiative, and barriers, facilitators, research ethics and governance, and trial funder interactions. Findings Characteristics of MRFF-funded clinical trials vs other funders The registry data analysis (Desktop Review) found that the MRFF-funded trials were broadly similar to trials funded by NHMRC, NHMRC CTCS, NIH and CIHR, and there were a few areas where MRFF-funded trials appeared better on average. The study design and quality of the MRFF funded trials was broadly equal to or better than most other funders’ trials. For example, 16% of MRFF-funded trials are in the “over 1000 participants” category, which is larger than for the other funders, including the NHMRC (full set), CIHR, and NIH (the 16% is smaller than the NHMRC CTCS’s 40%, but due to a very small size of the CTCS sample set (n=14), it is difficult to draw meaningful comparisons). The mix of study designs were comparable across funders. However, there was a notable lack of factorial trials – a very efficient design – across all funders including MRFF. Rates of use of randomised versus non-randomised trial designs, and the percentage of trials that were blinded, were also generally similar for all funders, aside from NHMRC-funded CTCS studies which generally had a higher percentage of randomised trials and blinded trials than other funders. By far, the most common design was a parallel group trial, but with a modest number of cluster, adaptive, platform, crossover, and factorial studies. Given the recent acceptance by the clinical trials community of adaptive and platform trials – which improve trial efficiency and the speed of addressing new clinical questions – the number being funded is encouraging. In contrast, the small number of factorial designs may require some explicit intervention on the part of MRFF. The design issues were commented on by some stakeholders, in particular the need for methodological expertise on the Grant Assessment Committees. A related concern was the small number of trials using a “Standardised Outcome Set”, i.e. a set of clinically-relevant measures that have been identified by experts by consensus for common reporting in the field/disease area, which is considered best practice, and consideration might be given to encouraging this in the advice to applicants. The Open Science processes elements available were protocol access and whether individual patient data would be available. Protocol availability was very low for trials of all of the 5 funders examined, but strikingly better for MRFF studies with 22% of protocols being available. arch.
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 | 0,456 | 0,489 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,006 | 0,015 |
| Bibliométrie | 0,014 | 0,015 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,031 | 0,013 |
| Science ouverte | 0,008 | 0,012 |
| Intégrité de la recherche | 0,016 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,019 | 0,008 |
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 ».