A DELPHI study priority setting the remaining challenges for the use of routinely collected data in trials: COMORANT-UK

Background Researchers are increasingly seeking to use routinely collected data to support clinical trials. This approach has the potential to transform the way clinical trials are conducted in the future. The availability of routinely collected data for research, whether healthcare or administrative, has increased, and infrastructure funding has enabled much of this. However, challenges remain at all stages of a trial life cycle. This study, COMORANT-UK, aimed to systematically identify, with key stakeholders across the UK, the ongoing challenges related to trials that seek to use routinely collected data. Methods This three-step Delphi method consisted of two rounds of anonymous web-based surveys and a virtual consensus meeting. Stakeholders included trialists, data infrastructures, funders of trials, regulators, data providers and the public. Stakeholders identified research questions or challenges that they considered were of particular importance and then selected their top 10 in the second survey. The ranked questions were taken forward to the consensus meeting for discussion with representatives invited from the stakeholder groups. Results In the first survey, 66 respondents yielded over 260 questions or challenges. These were thematically grouped and merged into a list of 40 unique questions. Eighty-eight stakeholders then ranked their top ten from the 40 questions in the second survey. The most common 14 questions were brought to the virtual consensus meeting in which stakeholders agreed a top list of seven questions. We report these seven questions which are within the following domains: trial design, Patient and Public Involvement, trial set-up, trial open and trial data. These questions address both evidence gaps (requiring further methodological research) and implementation gaps (requiring training and/or service re-organisation). Conclusion This prioritised list of seven questions should inform the direction of future research in this area and should direct efforts to ensure that the benefits in major infrastructure for routinely collected data are achieved and translated. Without this and future work to address these questions, the potential societal benefits of using routinely collected data to help answer important clinical questions will not be realised

A DELPHI study priority setting the remaining challenges for the use of routinely collected data in trials: COMORANT-UK

Background: Researchers are increasingly seeking to use routinely collected data to support clinical trials. This approach has the potential to transform the way clinical trials are conducted in the future. The availability of routinely collected data for research, whether healthcare or administrative, has increased, and infrastructure funding has enabled much of this. However, challenges remain at all stages of a trial life cycle. This study, COMORANT-UK, aimed to systematically identify, with key stakeholders across the UK, the ongoing challenges related to trials that seek to use routinely collected data. Methods: This three-step Delphi method consisted of two rounds of anonymous web-based surveys and a virtual consensus meeting. Stakeholders included trialists, data infrastructures, funders of trials, regulators, data providers and the public. Stakeholders identified research questions or challenges that they considered were of particular importance and then selected their top 10 in the second survey. The ranked questions were taken forward to the consensus meeting for discussion with representatives invited from the stakeholder groups. Results: In the first survey, 66 respondents yielded over 260 questions or challenges. These were thematically grouped and merged into a list of 40 unique questions. Eighty-eight stakeholders then ranked their top ten from the 40 questions in the second survey. The most common 14 questions were brought to the virtual consensus meeting in which stakeholders agreed a top list of seven questions. We report these seven questions which are within the following domains: trial design, Patient and Public Involvement, trial set-up, trial open and trial data. These questions address both evidence gaps (requiring further methodological research) and implementation gaps (requiring training and/or service re-organisation). Conclusion: This prioritised list of seven questions should inform the direction of future research in this area and should direct efforts to ensure that the benefits in major infrastructure for routinely collected data are achieved and translated. Without this and future work to address these questions, the potential societal benefits of using routinely collected data to help answer important clinical questions will not be realised

Correction: Medicines and Healthcare products Regulatory Agency’s “Consultation on proposals for legislative changes for clinical trials”: a response from the Trials Methodology Research Partnership Adaptive Designs Working Group, with a focus on data sharing

Following publication of the original article [1], we have been informed that affiliation for Graham Wheeler was incorrectly presented. The correct affiliation at the time of manuscript submission is Imperial Clinical Trials Unit, Imperial College London. Originally published affiliation: “Present address: Statistics & Data Science Innovation Hub, GSK, Brentford, UK”. The original article has been corrected

Medicines and Healthcare products Regulatory Agency’s “Consultation on proposals for legislative changes for clinical trials”: a response from the Trials Methodology Research Partnership Adaptive Designs Working Group, with a focus on data sharing

In the UK, the Medicines and Healthcare products Regulatory Agency consulted on proposals “to improve and strengthen the UK clinical trials legislation to help us make the UK the best place to research and develop safe and innovative medicines”. The purpose of the consultation was to help finalise the proposals and contribute to the drafting of secondary legislation. We discussed these proposals as members of the Trials Methodology Research Partnership Adaptive Designs Working Group, which is jointly funded by the Medical Research Council and the National Institute for Health and Care Research. Two topics arose frequently in the discussion: the emphasis on legislation, and the absence of questions on data sharing. It is our opinion that the proposals rely heavily on legislation to change practice. However, clinical trials are heterogeneous, and as a result some trials will struggle to comply with all of the proposed legislation. Furthermore, adaptive design clinical trials are even more heterogeneous than their non-adaptive counterparts, and face more challenges. Consequently, it is possible that increased legislation could have a greater negative impact on adaptive designs than non-adaptive designs. Overall, we are sceptical that the introduction of legislation will achieve the desired outcomes, with some exceptions. Meanwhile the topic of data sharing — making anonymised individual-level clinical trial data available to other investigators for further use — is entirely absent from the proposals and the consultation in general. However, as an aspect of the wider concept of open science and reproducible research, data sharing is an increasingly important aspect of clinical trials. The benefits of data sharing include faster innovation, improved surveillance of drug safety and effectiveness and decreasing participant exposure to unnecessary risk. There are already a number of UK-focused documents that discuss and encourage data sharing, for example, the Concordat on Open Research Data and the Medical Research Council’s Data Sharing Policy. We strongly suggest that data sharing should be the norm rather than the exception, and hope that the forthcoming proposals on clinical trials invite discussion on this important topic

Inhibition of CaMKK2 Enhances Fracture Healing by Stimulating Indian Hedgehog Signaling and Accelerating Endochondral Ossification

Approximately 10% of all bone fractures do not heal, resulting in patient morbidity and healthcare costs. However, no pharmacological treatments are currently available to promote efficient bone healing. Inhibition of Ca2+/calmodulin (CaM)-dependent protein kinase kinase 2 (CaMKK2) reverses age-associated loss of trabecular and cortical bone volume and strength in mice. In the current study, we investigated the role of CaMKK2 in bone fracture healing and show that its pharmacological inhibition using STO-609 accelerates early cellular and molecular events associated with endochondral ossification, resulting in a more rapid and efficient healing of the fracture. Within 7 days postfracture, treatment with STO-609 resulted in enhanced Indian hedgehog signaling, paired-related homeobox (PRX1)-positive mesenchymal stem cell (MSC) recruitment, and chondrocyte differentiation and hypertrophy, along with elevated expression of osterix, vascular endothelial growth factor, and type 1 collagen at the fracture callus. Early deposition of primary bone by osteoblasts resulted in STO-609–treated mice possessing significantly higher callus bone volume by 14 days following fracture. Subsequent rapid maturation of the bone matrix bestowed fractured bones in STO-609–treated animals with significantly higher torsional strength and stiffness by 28 days postinjury, indicating accelerated healing of the fracture. Previous studies indicate that fixed and closed femoral fractures in the mice take 35 days to fully heal without treatment. Therefore, our data suggest that STO-609 potentiates a 20% acceleration of the bone healing process. Moreover, inhibiting CaMKK2 also imparted higher mechanical strength and stiffness at the contralateral cortical bone within 4 weeks of treatment. Taken together, the data presented here underscore the therapeutic potential of targeting CaMKK2 to promote efficacious and rapid healing of bone fractures and as a mechanism to strengthen normal bones

Getting our ducks in a row:The need for data utility comparisons of healthcare systems data for clinical trials

BACKGROUND: Better use of healthcare systems data, collected as part of interactions between patients and the healthcare system, could transform planning and conduct of randomised controlled trials. Multiple challenges to widespread use include whether healthcare systems data captures sufficiently well the data traditionally captured on case report forms. "Data Utility Comparison Studies" (DUCkS) assess the utility of healthcare systems data for RCTs by comparison to data collected by the trial. Despite their importance, there are few published UK examples of DUCkS.METHODS-AND-RESULTS: Building from ongoing and selected recent examples of UK-led DUCkS in the literature, we set out experience-based considerations for the conduct of future DUCkS. Developed through informal iterative discussions in many forums, considerations are offered for planning, protocol development, data, analysis and reporting, with comparisons at "patient-level" or "trial-level", depending on the item of interest and trial status.DISCUSSION: DUCkS could be a valuable tool in assessing where healthcare systems data can be used for trials and in which trial teams can play a leading role. There is a pressing need for trials to be more efficient in their delivery and research waste must be reduced. Trials have been making inconsistent use of healthcare systems data, not least because of an absence of evidence of utility. DUCkS can also help to identify challenges in using healthcare systems data, such as linkage (access and timing) and data quality. We encourage trial teams to incorporate and report DUCkS in trials and funders and data providers to support them.</p

Getting our ducks in a row:The need for data utility comparisons of healthcare systems data for clinical trials

BACKGROUND: Better use of healthcare systems data, collected as part of interactions between patients and the healthcare system, could transform planning and conduct of randomised controlled trials. Multiple challenges to widespread use include whether healthcare systems data captures sufficiently well the data traditionally captured on case report forms. "Data Utility Comparison Studies" (DUCkS) assess the utility of healthcare systems data for RCTs by comparison to data collected by the trial. Despite their importance, there are few published UK examples of DUCkS.METHODS-AND-RESULTS: Building from ongoing and selected recent examples of UK-led DUCkS in the literature, we set out experience-based considerations for the conduct of future DUCkS. Developed through informal iterative discussions in many forums, considerations are offered for planning, protocol development, data, analysis and reporting, with comparisons at "patient-level" or "trial-level", depending on the item of interest and trial status.DISCUSSION: DUCkS could be a valuable tool in assessing where healthcare systems data can be used for trials and in which trial teams can play a leading role. There is a pressing need for trials to be more efficient in their delivery and research waste must be reduced. Trials have been making inconsistent use of healthcare systems data, not least because of an absence of evidence of utility. DUCkS can also help to identify challenges in using healthcare systems data, such as linkage (access and timing) and data quality. We encourage trial teams to incorporate and report DUCkS in trials and funders and data providers to support them.</p