ArticlePharmacoEconomics - open2024
An Analysis of Uncertainties and Data Collection Agreements in the Cancer Drugs Fund.
Article in PharmacoEconomics - open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mapping the landscape of managed entry agreements: a systematic review of global frameworks, system-level components, and implementation challenges.Frontiers in pharmacology · 2026Pooled it
- NICE's early value assessment: an external assessment group's commentary on the challenges and opportunities of NICE's new life cycle approach to HealthTech.International journal of technology assessment in health care · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundManaged Access Agreements (MAAs) are a commercial arrangement that provide patients earlier access to innovative health technologies while uncertainties in the evidence base are resolved through data collection. In the UK, data collection agreements (DCAs) outline the evidence that will be collected during the MAA period and are intended to resolve uncertainties in the clinical- and cost-effectiveness of a technology sufficient for the National Institute of Health and Care Excellence (NICE) committee to make a final decision on reimbursement.
objectiveThe aim of this study was to identify the primary uncertainties leading to a recommendation for entry to the Cancer Drugs Fund (CDF) and evaluate how the corresponding DCAs attempt to address these.
methodsA database of MAAs agreed within the CDF was compiled with coverage between July 2016 and December 2020 (the time during which evidence generation was routinely collected within the CDF up until the time of analysis). Uncertainties in the evidence base for technologies entering the CDF were analysed alongside the outcomes planned for data collection during the MAA. These data provide an overview of the key uncertainties surrounding health technologies in the CDF on entry and the types of evidence targeted by DCAs.
resultsIn the assessment of 39 Cancer Drugs Fund (CDF) cases, NICE committees identified a total of 108 key uncertainties in cost-effectiveness estimates. Overall survival was the most commonly identified uncertainty, followed by generalisability of the evidence to the target population. DCAs specified a range of outcomes relevant to understanding the clinical effectiveness of the technology, though fewer than half (43.6%) of the DCAs addressed all the key uncertainties identified by the NICE committee.
conclusionThe analysis indicated that data collection within the CDF is not sufficient to resolve all the uncertainties identified by the NICE committee, meaning that other approaches will be needed at re-appraisal to ensure that the NICE committee can reach a final decision on reimbursement.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.