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Tag: clinical trial data

Vivli Senior Advisor speaks at CDISC 2023 Japan Interchange Program

Vivli Data Request Process

Vivli Senior Advisor Azusa Tsukida spoke at Clinical Data Interchange Standards Consortium (CDISC) 2023 Japan Interchange Program on July 10.

Tsukida presented during the session on ‘Real World Data & Regulatory Presentations/Perspectives’. Her talk focused on the benefits of data sharing, using case studies from data contributors who are sharing high-quality data via the Vivli platform to enable access to researchers worldwide and contribute to scientific discovery.

CDISC works to develop and advance data standards to support transforming incompatible formats, inconsistent methodologies, and diverse perspectives into a coherent framework for generating clinical research data that is accessible, interoperable, and reusable. More than 80% of the data available in Vivli is formatted in the CDISC-SDTM standard.

Find out more about how you can request data from Vivli’s repository and help accelerate the progress of health research.

Vivli Researcher Spotlight: Dr. Fasihul Khan on the potential for biomarkers to predict outcomes for people with pulmonary fibrosis

Fasihul Khan, M.D., Ph.D., is a consultant at Glenfield Hospital, University Hospitals of Leicester NHS UK. Dr. Khan’s team submitted a research proposal to access Vivli to conduct analysis relevant to their topic, “A systematic review and individual patient data meta-analysis of physiological biomarkers in idiopathic pulmonary fibrosis”. The team’s completed research has been presented to the research community at conferences and in publications including American Journal of Respiratory and Critical Care Medicine. He sat down with Vivli to tell us more about accessing individual participant data to advance his research, and the potential for biomarkers to predict outcomes for people with pulmonary fibrosis.

Please tell us more about your research – what led you to want to research this particular topic?

So my area of interest is pulmonary fibrosis, which is a condition causing scarring of the lungs. Pulmonary fibrosis is a relatively rare condition, and therefore the number of studies in this area are limited, although expanding rapidly.  I was keen to synthesize some of the existing information that was already available. I wanted to perform a systematic review, specifically looking to see whether there are blood biomarkers that can predict outcomes in patients with diagnosed pulmonary fibrosis. When I started searching the literature, it very quickly became apparent that there were several published studies, but actually the data and the way the studies were reported were very heterogeneous.  Individually the studies yielded inconsistent results, utilized data-dependent thresholds, and frequently did not adjust for confounders. Therefore, I sought individual participant data which helped overcome these limitations and enabled robust data analyses to be performed leading to reliable conclusions. 

Could you talk about what it was like to work across multiple data-sharing platforms; how did you handle that?

This was not straightforward! I created summary estimates from each study separately on the different platforms in Vivli and in CSDR, then imported them manually onto my own database. I then used additional software to pool the summary estimates. Having the data all  in one place would have saved me a lot of time and stress!

Not a lot of researchers have the perseverance to do what you did. What advice would you give to researchers before they start off? Things you wish you’d known before you started?

I think it’s important to consider the project as a whole. It is highly likely the process will take much longer than you think, and that’s not necessarily any individual or organization’s fault. You need to have a clear understanding with contingency plans for each stage, and give yourself plenty of time! Be clear about your research question, and whether individual participant data are likely to improve your research, before committing to the additional effort. Speak to others who have been through the process of acquiring individual participant data, and your institution to understand timescales for data sharing agreements as these are likely to be time consuming and potential limiting factors. 

Once you were able to access the individual patient data, were you able to get past the reporting limitations and find what you needed? 

Absolutely; once we had the raw data, we were able to perform our analysis and produce some very meaningful results, which we have  subsequently published in two journals. The first was a blood biomarker paper in the European Respiratory Journal which was the first blood biomarker study in pulmonary fibrosis to utilize this approach, and provides robust estimates of the association between matrix-metalloproteinase 7 and disease progression.

The second paper was published in the American Journal of Respiratory Critical Care Medicine. In this paper, we looked at change in FVC which is a lung function measurement used to assess progression in pulmonary fibrosis. All interventional clinical trials measure FVC as an endpoint – typically at 12 months, but patients have additional FVC measurements at baseline, 3, and 6 months. The purpose of our research was to evaluate whether short term changes in FVC i.e. over three-months, are associated with overall mortality. In other words, can we shorten clinical trials by finding an earlier signal than the 12 months FVC change that is currently accepted by regulators. Since the association between short term FVC change and mortality was not reported in any clinical study, we needed the individual participant data to model this association. Indeed, we were able to find that three-month FVC change is associated with mortality, and perhaps more importantly a treatment effect could be observed between treatment and placebo arms at three-months. The findings of this study have been well received by the research community, and have already been adopted into the design of an adaptive trial in IPF. Lots of hard work, but worth it as the results are likely to generate further research which ultimately will hopefully impact patients in a positive manner!  



Vivli CEO Rebecca Li to Speak at FAIR Data Symposium at BIO-IT World Conference and Expo

Vivli’s CEO, Rebecca Li, will speak at the FAIR Data Symposium as part of the Bio-IT World Conference and Expo on May 16.

Li will discuss how to apply FAIR principles to the access of data from completed clinical trials, including using the appropriate technical and governance infrastructure. Platform architecture must include clear, computable metadata to facilitate findability and interoperability. Her talk will focus on Vivli as a use case and cover:

1) Principles that guided Vivli’s unique design choices as a FAIR trial data sharing platform

2) How Vivli balances the rights and interests of study participants and investigators with the needs of data requesters and the societal benefit of greater data sharing.

3) How Vivli has evolved with the needs of the ecosystem over time.

Learn more here.

Vivli is hiring a Clinical Research Manager

VIVLI, the Center for Global Clinical Research is seeking a Clinical Research Manager.

Vivli is a mission-focused non-profit and to help manage our continued growth, we are looking for a seasoned clinical research manager to join the operations team. The operations team supports researchers seeking to access datasets from contributors and provides support for data contributors.

Location: Remote, with a preference for East Coast or European working hours.

Responsibilities will include but not limited to:
• Manage day-to-day data request review process for specific requests.
• Work closely with Vivli end users to ensure success of the use of the platform
• Vivli platform QA
• Support the Vivli resource library.
• Lead in on-boarding new members
• Perform other duties when requested

Qualifications:
• Minimum 5 years prior experience managing data transparency processes, either at an existing repository, platform or as a data contributor
• 2-3 years experience with vendor management
• Experience in supporting researcher and/or data contributors in fulfilling data requests
• Understanding of clinical trials processes and/or clinical data management preferred
• Excellent written and oral communication skills and interpersonal skills
• Prior experience with process improvement a plus
• Computer proficiency in MS Office, Excel, PowerPoint, Sharepoint, Dropbox etc.
• Excellent organizational skills and attention to detail
• Ability to manage projects and resources
• Bachelor’s degree in the health profession, science, IT or business field

To apply, send a cover letter and CV to hr@vivli.org.

Vivli Board names Rebecca Li CEO

Vivli announced that its Board of Directors has promoted Rebecca Li to the position of CEO. Li previously held the position of Executive Director and has been with the Vivli since its founding when it launched as a project from the MRCT Center of the Brigham and Women’s Hospital and Harvard.

“Rebecca has overseen the extraordinary growth of Vivli from the start and we are excited that she will continue to lead Vivli through the next phase of growth as we aim to go even further in our mission to make clinical trial available for research. She has demonstrated herself to be an exceptional team leader and strategic thinker with a clear vision for how we will develop globally in the coming years,” said Dr. Michael Stebbins, Vivli’s Board Chair.

“I am privileged to lead Vivli and envision further expanding our successful platform technology into Europe and Asia as we enter our next phase of innovation and growth” said Dr. Rebecca Li.

Vivli was founded in 2018 as a non-profit organization that is currently the largest individual participant-level (IPD) data sharing platform focused on sharing clinical trial data serving the international research community.

 

Vivli Researcher Spotlight: Dr. Elena Myasoedova on accessing clinical trial data to advance research and the possibilities for using machine learning as a tool in clinical support for people with rheumatoid arthritis

Elena Myasoedova, M.D., Ph.D., is a clinical rheumatologist with specialty interest in inflammatory arthritis. She is an associate professor of medicine and epidemiology and clinical practice, and leads research in rheumatology and specifically inflammatory arthritis at the Mayo Clinic. Dr. Myasoedova’s team submitted a research proposal to access Vivli to conduct analysis relevant to their topic, “Individualized Prediction of Treatment Response to Methotrexate in Patients with Rheumatoid Arthritis: A Machine Learning Approach”. Their completed research has been presented to the rheumatology research community at conferences and in publications including Annals of the Rheumatic Diseases. She sat down with Vivli to tell us more about accessing clinical trial data to advance her research, and the possibilities for using machine learning as a tool in clinical support for people with rheumatoid arthritis.

Please tell us a little bit more about your research – what led you to want to investigate this particular topic?
In patients with rheumatoid arthritis, methotrexate is a medicine that is used very commonly, and frequently is the first medicine and the most common medicine used in combination later on during the disease course. The challenge that we are facing in rheumatology in general is that we do not have individualized prediction of response to treatments. This means that we use a trial and error approach to treatments; we start patients on medications that are generally effective and safe, and then if a patient does not respond, we upgrade to a different medicine. Because most of the medicines that we use take weeks and months to show their effects, it’s important to understand early on if a person is likely to be a responder. That would help to save time and potentially save some unwanted side effects for the patient, and also help us to be more proactive and helpful.

For this specific study, we looked at clinical markers or clinical predictors of response to methotrexate. We found more than 1400 patients from 13 randomized trials. A total of 775 patients from 4 RCTs were included in the study, and we monitored their response across a six-month timeframe. We further evaluated whether people who did not respond to methotrexate and had some moderate to high disease activity at 12 weeks – who out of this population would respond by follow-up at 24 weeks. We also found a couple of markers for that: specifically if there was a drop in DAS28 sat rate from baseline to 12 weeks of at least 1 point, then it was predictive of then achieving remission or low disease activity at 24 weeks. Otherwise the chances were slim.

We developed this algorithm, and externally validated it using two independent trials with good results. I think that these findings advance the understanding of who are the most likely responders, and how we should discuss with patients their likelihood of response at the very start of their treatment.

Are you hoping that this is going to change the clinical approach? Has it already had an impact in clinical approaches to working with people who have arthritis?
This particular study, and similar studies would probably change the way we discuss this treatment with a patient; changing the treatment approach is a very complex task that probably has to come through the association guidelines.

Is there anything specific that you’d like to say about what working with the dataset in Vivli enabled you to do that you might not have been able to do otherwise?
It was actually a very good experience for us to work with Vivli datasets. It provided longitudinal data on patients who were users of methotrexate but not other medicines, and there were hundreds of these patients available from the trials. So it’s a fairly good dataset to work with, and it had multiple data points longitudinally. Also, at each point, there were multiple clinical points collected, so it was fairly comprehensive.

Do you have any advice that you would give to other researchers who might be interested in accessing this type of data using a platform like Vivli?
The most important advice is to put together a comprehensive proposal with a plan right off the bat, to make sure that the timeline is feasible and that the question that they want to address is feasible with the available data – just to make sure that they are not over-expecting or under-planning. I think it’s most important to ensure that the study question matches the data set.

Have you had feedback about your research and findings?
This research has been presented at several conferences, and the comments have mostly been positive, acknowledging the need for developing such algorithms.

T1D exercise data RFP from Helmsley Charitable Trust due the end of this month

The Leona M. and Harry B. Helmsley Charitable Trust’s Type 1 Diabetes Program has launched an initiative to support innovative and practicable solutions to help people with type 1 diabetes (T1D) exercise safely and to improve their quality of life. To help address knowledge gaps about the effect of exercise on T1D, Helmsley supported a research collaboration centered on two large observational exercise studies in people with T1D: one in adults (T1-DEXI) and one in children (T1-DEXIP). More than 500 people took part in the adult study, and another 250 in the pediatric study. Both studies were recruited and conducted fully remotely by design, enabling full monitoring and data collection which was unimpeded by COVID-19 pandemic restrictions.

The data from these studies include information about types of physical activity, heart rate, insulin use, CGM, diet, and genetics. The research collaboration collected and organized the observational data to maximize interoperability, and Helmsley has partnered with Vivli to make the data from T1-DEXI and T1-DEXIP publicly available. Sharing study data publicly will enable a range of researchers to access and explore the data from a variety of perspectives and research specialties.

In addition to facilitating public access through the Vivli platform, Helmsley has also opened a request for proposals (RFP) to support researchers, clinicians, and data scientists interested in analyzing the data and testing novel solutions in people with T1D. The RFP aims to fund projects that will improve the understanding of how exercise impacts T1D and that will provide people with T1D and their healthcare providers practical management solutions and clinical guidelines. The overarching purpose of this initiative is to move real-world data towards real-world solutions.

Concept notes for the RFP are due by April 30, 2023, and full proposals will be invited for submission later in 2023. Researchers interested in exploring the T1D dataset on Vivli in order to prepare concept notes for submission can search data publicly available through Vivli’s search portal, and submit a data request using the standard process. Complete information about the RFP, including key dates and submission criteria, is available on The Helmsley Charitable Trust site.

Deniz Dalton, Program Officer in T1D at Helmsley, said, “People with T1D face many burdensome challenges in their daily lives, including complex decisions around exercise, diet, monitoring glucose levels, and administering insulin. We are excited to make this data publicly available and to launch this RFP, to advance the understanding of how exercise affects glucose management, and ultimately to help create real-world solutions to challenges faced by people with T1D around exercise.”

For more information, please watch Dalton’s presentation on this initiative at the Vivli 2022 Annual Meeting, or contact Vivli User Support directly at support@vivli.org with any questions

Vivli webinar: Recent Developments in Data Privacy Laws and their Impact on Data Sharing

Vivli held a webinar to discuss recent developments in data privacy laws and their impact on data sharing.

The webinar was held on Monday, April 24th at 3pm CEST / 2pm BST / 9am EDT.

VIEW THE RECORDING

The People’s Republic of China (PRC) enacted a comprehensive data privacy law called the Personal Information Protection Law (PIPL) in November 2021. This law adds to an already complex data privacy framework in the PRC, which also includes the Regulation of Human Genetic Resources, the Biosecurity Law of the PRC and the Data Security Law of the PRC. This webinar will focus on key implications of the PIPL and these other PRC laws for the sharing of clinical trials data generated in the PRC for further research purposes. The speakers will also make comparisons between PIPL and the European Union’s General Data Protection Regulation (“GDPR”), another global privacy law that has had a significant impact on the sharing of clinical trial data.

Learning objectives for this session include:
• Understand the basics of PIPL
• Understand how PIPL affects sharing of clinical trials data across international borders
• Understand key differences between PIPL and GDPR

Speakers:
• Rebecca Li, Vivli
• David Peloquin, Ropes & Gray
• Katherine Wang, Ropes & Gray

This webinar is intended for organizations and individuals that share data and will be particularly useful for those who focus on data privacy.

VIEW THE RECORDING

Vivli Executive Director Dr. Rebecca Li Speaking at NIA-DUKE-Alzheimer’s Association Workshop

Rebecca Li will be speaking at the NIA-DUKE-Alzheimer’s Association Workshop held on March 15-16th sponsored by the National Institute on Aging.

The focus of the talk by Rebecca Li and Katherine Welsh-Bohmer (Duke University School of Medicine) is on how the Vivli platform can be used to access trials in Alzheimer’s Disease. Dr. Welsh-Bohmer will discuss the TOMMORROW trial – a large prevention study in Alzheimer’s Disease – as a specific use case.

 NIH’s New Data Sharing Policy: Maximizing the Value of Research through Data Re-use and Data Access on the Vivli Repository

Learn how the Vivli repository is making it easier for scientists to share and access data, and how you can comply with the NIH’s data management and sharing policy (DMSP) to maximize the value of your research

The National Institutes of Health (NIH) has a policy in place to ensure that data generated by NIH-funded research is accessible to the scientific community starting on January 25, 2023.

Data should be made available as soon as possible or the acceptance for publication of the main findings from the final dataset but the latest date is the end of the award. Data sharing can be done through a variety of mechanisms, including NIH domain repositories or NIH generalist repositories. These can be open-access or controlled-access systems. One such controlled-access repository recognized by the NIH is Vivli, a generalist repository for sharing of clinical data for human research studies.

As a condition of their grant application, investigators are now required to prospectively plan for management of their data and preparing it for re-use, submit a data management and sharing plan (DMSP), and comply with the drafted plan. The NIH Data Sharing Policy (DSMP) encourages investigators to share their data in order to maximize the value of NIH research funds.  But what exactly is a DMSP and how do you draft one for submission to the Vivli Repository? The DMSP is a set of principles and guidelines that outline the requirements for sharing data generated by NIH-funded research. It includes 6 major elements that were selected to ensure that the data is shared as widely and promptly as possible, to maximize the scientific and public health value of the research, while protecting participant privacy and confidentiality. To fill out the DMSP, decisions should be made about the choice of repository, how long the repository will hold/archive the data, whether special tools/software will be provided to access the data, whether consensus data standards apply or exist, whether controlled access will be required and the oversight management details.

To help investigators on their journey to fulfilling the NIH data sharing policy, we have created a list of all the other resources available on our website, including the DMSP template guidance and budget guidance specific to using Vivli to help you navigate the process. Vivli has a step-by-step guide to understanding each of these elements and items to consider when developing a DMSP. We also have a customizable DMSP exemplary language available for download and adaptation, which includes sample text as well as guidance on preparing and submitting a budget as part of the DMSP.

Fill out the form below to access all the DMPS Guidance provided by Vivli.

Vivli has recently released new features timed to the NIH policy launch including: branded portals for research programs / institutions; academic credit; streamlined process for data sharing and reporting for institutions.

In summary, the NIH encourages data sharing as part of its mission to advance biomedical research and to promote collaboration among scientists. Vivli is a non-profit organization that provides a platform recognized by the NIH for funded researchers to share and access anonymized clinical trial data in a secure and compliant way.

    What best describes your current role?

    Do you plan to include Vivli in any future data management plans?