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:<br/>The Medical Research Future Fund (MRFF) is a research fund set up by the Australian <br/>Government in 2015 to support health and medical research in Australia. The Clinical Trials<br/>Activity (CTA) Initiative was established in 2016. Early funding priorities included rare <br/>cancers, rare diseases and unmet need, childhood brain cancer, reproductive cancers, and <br/>neurological disorders.<br/><br/>The Department of Health and Aged Care (the department) contracted the Institute for <br/>Evidence-Based Healthcare (IEBH), at Bond University, to conduct an evaluation of MRFF’s <br/>CTA Initiative, to assess its progress in achieving the objectives set out in the MRFF 10-year <br/>Investment Plan in accordance with the MRFF Monitoring, evaluation and learning strategy, <br/>2020-21 to 2023-24, and to guide future investments in clinical trials activity through the <br/>MRFF.<br/><br/>The intention of the evaluation of the MRFF Clinical Trials Activity Initiative was to: <br/>• consider all existing investments on clinical trials made through the MRFF (e.g.,<br/>progress made through MRFF funded projects)<br/>• consider approaches and the current landscape for clinical trials internationally <br/>and nationally in Australia<br/>• suggest opportunities for improving funding and granting arrangements for <br/>clinical trials through the initiative and the MRFF more broadly.<br/><br/>Methods:<br/>To collect data for the evaluation, we used three complementary methods. <br/>1. Desktop Review Data Set<br/>A desktop review compared MRFF-funded trials with trials funded by comparable funders, <br/>including: the National Health and Medical Research Council (NHMRC) in Australia and their <br/>subset of trials in the Clinical Trials and Cohort Studies Scheme (CTCS), the National Institute <br/>for Health and Care Research (NIHR) in the United Kingdom, Canadian Institute of Health<br/>Research (CIHR) in Canada, and the National Institutes of Health (NIH) in the United States. <br/>The data was derived from items in the clinical trial registries including the Australian New <br/>Zealand Clinical Trials Registry (ANZCTR) and the NIH’s National Library of Medicine.<br/><br/>2. Survey Data Set <br/>For each MRFF and NHMRC CTCS-funded grant, we sought two responses – one from the <br/>Chief Investigator A (CI-A) and one from an Early to Mid-Career Researcher (EMCR). The <br/>survey was open for completion between 4 October and 24 November 2022. <br/><br/>3 Stakeholder Consultation Data Set<br/>To supplement the findings of the Desktop Review (Project 1) and the Survey (Project 2), we <br/>conducted interviews with key stakeholders to better understand the key factors <br/>contributing to success of funded trials – including recruitment, follow up, and publication. <br/>The interviews included comments on data from the Desktop Review (Project 1) and Survey <br/>(Project 2), as well as questions about the MRFF Clinical Trial Activity Initiative, and barriers, <br/>facilitators, research ethics and governance, and trial funder interactions. <br/><br/>Findings<br/>Characteristics of MRFF-funded clinical trials vs other funders<br/>The registry data analysis (Desktop Review) found that the MRFF-funded trials were broadly <br/>similar to trials funded by NHMRC, NHMRC CTCS, NIH and CIHR, and there were a few areas <br/>where MRFF-funded trials appeared better on average.<br/><br/>The study design and quality of the MRFF funded trials was broadly equal to or better than <br/>most other funders’ trials. For example, 16% of MRFF-funded trials are in the “over 1000 <br/>participants” category, which is larger than for the other funders, including the NHMRC (full <br/>set), CIHR, and NIH (the 16% is smaller than the NHMRC CTCS’s 40%, but due to a very small <br/>size of the CTCS sample set (n=14), it is difficult to draw meaningful comparisons). <br/><br/>The mix of study designs were comparable across funders. However, there was a notable <br/>lack of factorial trials – a very efficient design – across all funders including MRFF. Rates of <br/>use of randomised versus non-randomised trial designs, and the percentage of trials that <br/>were blinded, were also generally similar for all funders, aside from NHMRC-funded CTCS <br/>studies which generally had a higher percentage of randomised trials and blinded trials than <br/>other funders. <br/><br/>By far, the most common design was a parallel group trial, but with a modest number of <br/>cluster, adaptive, platform, crossover, and factorial studies. Given the recent acceptance by <br/>the clinical trials community of adaptive and platform trials – which improve trial efficiency <br/>and the speed of addressing new clinical questions – the number being funded is <br/>encouraging. In contrast, the small number of factorial designs may require some explicit <br/>intervention on the part of MRFF. <br/><br/>The design issues were commented on by some stakeholders, in particular the need for <br/>methodological expertise on the Grant Assessment Committees. A related concern was the <br/>small number of trials using a “Standardised Outcome Set”, i.e. a set of clinically-relevant <br/>measures that have been identified by experts by consensus for common reporting in the <br/>field/disease area, which is considered best practice, and consideration might be given to <br/>encouraging this in the advice to applicants. <br/><br/>The Open Science processes elements available were protocol access and whether individual <br/>patient data would be available. Protocol availability was very low for trials of all of the 5 <br/>funders examined, but strikingly better for MRFF studies with 22% of protocols being <br/>available.<br/>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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,951 | 0,795 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,014 | 0,012 |
| Bibliométrie | 0,004 | 0,018 |
| Études des sciences et des technologies | 0,005 | 0,008 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,019 | 0,008 |
| Intégrité de la recherche | 0,004 | 0,022 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,036 | 0,002 |
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; les deux têtes enseignantes 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 ».