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Record W7112496924

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

2023· book· en· W7112496924 on OpenAlexaboutno aff

Bibliographic record

VenueBond University Research Portal (Bond University) · 2023
Typebook
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsnot available
FundersBond University
KeywordsClinical trialMedical researchClinical researchHealth careAlternative medicineMedical careResearch designHealth services research
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.456
metaresearch head score (Gemma)0.489
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.544
Threshold uncertainty score0.671

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4560.489
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0060.015
Bibliometrics0.0140.015
Science and technology studies0.0020.003
Scholarly communication0.0310.013
Open science0.0080.012
Research integrity0.0160.012
Insufficient payload (model declined to judge)0.0190.008

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.971
GPT teacher head0.712
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designSystematic review
DomainEvaluation
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractyes

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