National Academies Press: OpenBook
« Previous: Part 2: Different Types of Neuroscience Data: Challenges and Potential Opportunities
Suggested Citation:"7 Clinical Trial and Research Data." National Academies of Sciences, Engineering, and Medicine. 2020. Neuroscience Data in the Cloud: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25653.
×
Page 45
Suggested Citation:"7 Clinical Trial and Research Data." National Academies of Sciences, Engineering, and Medicine. 2020. Neuroscience Data in the Cloud: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25653.
×
Page 46
Suggested Citation:"7 Clinical Trial and Research Data." National Academies of Sciences, Engineering, and Medicine. 2020. Neuroscience Data in the Cloud: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25653.
×
Page 47
Suggested Citation:"7 Clinical Trial and Research Data." National Academies of Sciences, Engineering, and Medicine. 2020. Neuroscience Data in the Cloud: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25653.
×
Page 48
Suggested Citation:"7 Clinical Trial and Research Data." National Academies of Sciences, Engineering, and Medicine. 2020. Neuroscience Data in the Cloud: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25653.
×
Page 49
Suggested Citation:"7 Clinical Trial and Research Data." National Academies of Sciences, Engineering, and Medicine. 2020. Neuroscience Data in the Cloud: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25653.
×
Page 50

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

7 Clinical Trial and Research Data Highlightsa • New models for aligning datasets to accommodate various measures, data formats, analysis workflows and pipelines, and research goals are needed to share clinical trial and research data via the cloud (Lancashire, Li, Mangravite). • Although industry has become more supportive of data shar- ing, academic participation has been slow (Egan, Li). • To share clinical trial data for future analyses, data need to be curated and formatted in a standardized manner, such as using Clinical Data Interchange Standards Consortium (CDISC) stan- dards (Li, L. Merck). • An inventory of data and platforms that could be built through cross-community collaboration could enable more secondary analyses of these data (Hicks, Mangravite, L. Merck). • Clinical trials should plan for data sharing during their plan- ning stages, and consider where and whether existing data from other trials could be used (Li). • The amount and type of information collected for future uses must be balanced with the benefits of running more stream- lined trials (Hicks, Li, Mangravite). • Preclinical data harmonization and sharing is distinct from, and may be more difficult than, for clinical data (Hicks). a These points were made by the individual workshop participants identified above. They are not intended to reflect a consensus among workshop participants. 45 PREPUBLICATION COPY—Uncorrected Proofs

46 NEUROSCIENCE DATA IN THE CLOUD Cloud computing challenges relating to clinical trial and research data largely center on data sharing and related topics such as data curation and harmonization, said Lee Lancashire. This requires aligning datasets to accommodate various outcome measures, standardizing data formats, and harmonizing analysis workflows and pipelines, he said. Data sharing requires investment both in dollars and time, but may provide benefits in terms of transparency and enabling meta-analysis, triangulation, and distribution, said Lara Mangravite, president of Sage B ­ ionetworks. Each research group may value these benefits differently, she said. Therefore, Sage has been exploring a series of models that pair governance and policy with infrastructure to support their individual goals. These models may range from the most open of open resource sharing to models that restrict who can see and use the data, she said. Rebecca Li added that to incentivize data sharing, the individual goals of each researcher or research group need to be addressed, whether those goals include publications, increased funding, fulfilling an ethical commitment to participants, or driving new science and discoveries. Michael Egan, vice president of clinical research for neuroscience at Merck, said there has been a movement in industry to share more data. Some companies will give academic researchers access to datasets for analy- sis, while other companies use a web portal where researchers can submit data requests or specific analysis plans. However, Li noted that usage of data sharing platforms has often lagged behind contribution of data on many platforms. Academic participation has been particularly slow, she said, although the culture in academia is changing with regard to data sharing. Educating researchers about the availability of data and different platforms may help boost data sharing efforts, said Li. According to Ramona Hicks, director of science and technology at One Mind, NIH’s approach to data collection in clinical trials has flipped from a sense that clinical trials were collecting too much unnecessary data and causing unnecessary expense, to the attitude that because one does not necessarily know what data will prove to be important for subtyping and other types of analysis, that may become necessary. Mangravite agreed that deciding how much data to collect, manage, and share is a challenge for data sharing platforms and depends on the goals of the data collectors. Funders certainly want to see data sharing to improve transparency and reproducibility of the primary analyses, she said, while other stakeholders may be more interested in building a common resource for many people to use. Harmonizing research data from the AMP and other non-clinical research projects present additional challenges, said Mangravite. She noted that the AMP-Alzheimer’s disease (AD) is actually a cluster of five different projects with different sample sets, collection methodologies, and PREPUBLICATION COPY—Uncorrected Proofs

CLINICAL TRIAL AND RESEARCH DATA 47 data types. While recognizing that harmonizing these data would increase their value, she said this benefit has to be balanced against the amount of work that would be required to do so. She said the AMP investigators have taken a middle ground, harmonizing the major human datasets and leaving some of the other data less harmonized, but making them avail- able for reuse. Li added that big, bloated clinical trials often result when researchers try to piggyback adjunct studies onto the trial, for example, by requesting collection of additional samples for a pharmacogenomics study. More sim- plified, streamlined trials are currently more common, she said. However, Hicks argued that one reason so many clinical trials have failed is because of insufficient understanding of the disorder, which can only be gained by conducting large observational, natural history studies that incorporate biomarkers, subtyping, and comparative effectiveness analyses. This could be achieved, she said, by piggybacking population-based data studies onto clinical trials. Li agreed, noting that there have been efforts to compare con- trol arms from many Alzheimer’s trials. She added that clinical trials should plan for data sharing up front and for the reuse of data by understanding better what similar trials are being conducted and leveraging data that have already been collected. CURRENT PROMISING PRACTICES IN CLINICAL TRIAL AND RESEARCH DATA SHARING Li described Vivli, a nonprofit, global, data-sharing platform built on the Microsoft Azure Cloud. Vivli hosts a diverse group of pharmaceutical and biotech companies as well as academic centers conducting clinical trials in AD and Parkinson’s disease (PD) as well as other neurological disorders, she said. Each member sets its own boundaries in terms of what data they will share and when those data will be shared, said Li. For example, most stipulate that only anonymized data will be shared, and some will share completed Phase 1 through Phase 4 clinical trials data only after a regu- latory decision has been made. She noted, however, that the diversity of stakeholders complicates efforts to come up with a single harmonized data use agreement. Users coming to the Vivli site can search the platform for studies of inter- est and then request individual participant-level data (IPD). Vivli reviews the request against the data contributors’ specifications. If approved, the user can access and analyze the data in a secure research environment in the cloud using specific analytical tools provided by Vivli. In some cases users may be given permission to download data, said Li. Completed research results are assigned a DOI. Users may use the Vivli platform to meet pub- lication and funder requirements, she said. PREPUBLICATION COPY—Uncorrected Proofs

48 NEUROSCIENCE DATA IN THE CLOUD With regard to standardization of data, Li noted that the FDA requires submitted data to adhere to CDISC standards.1 She said Vivli recommends this as well, but recognizes that many valuable datasets collected by aca- demic researchers do not conform to CDISC standards. Vivli made the strategic decision to allow such data to be accepted. She suggested that in the future there may be machine-learning approaches that will enable standardization of such data. ISSUES TO BE RESOLVED FOR SHARING CLINICAL TRIAL AND RESEARCH DATA When executing good, large, prospective trials, investigators have a duty to curate those data for future analyses, according to Lisa Merck. She identified two challenges: First, these data need to be curated and formatted in a way that enables other investigators to use them; and second, resources are needed to identify biomarkers and other covariates that may be buried as by-products of the first analysis. NIH and possibly other funders have designated funding for this use, said Hicks, although these funds may be underused. Providing investigators with some good examples of learnings derived from secondary analyses might encourage them to take advantage of these resources, she said. Hicks and Mangravite both advocated building an inventory of data and platforms, adding that cross-community collaboration will be essential to making this a reality. Lancashire said Cohen Veterans Bioscience evalu- ated more than 100 different platforms prior to identifying the BRAIN Commons, and is currently writing a white paper to make this information widely available. Hicks noted that The Kavli Foundation has also addressed this as part of the International BRAIN Initiative. With many data aggregation and sharing platforms existing or in devel- opment, Lancashire noted the potential value of sharing data across plat- forms. He suggested that identifying and aligning a core set of metadata would allow integration of cohort data across platforms. Recognizing that there would be barriers to doing this, Sean Horgan suggested picking a few different projects and convening investigators to start working on this. Mangravite agreed that a scientific use case approach is probably the best way to approach this problem. With AMP-AD and AMP-PD, for example, one was initially a target discovery project while the other was a biomarker discovery project. They have since morphed into each having a little bit of both. The opportunity to share data could prove highly productive, she said. 1  CDISC develops and advances data standards for clinical research, with a goal of making the data more interoperable and reusable. For more information, see https://www.cdisc.org (accessed December 12, 2019). PREPUBLICATION COPY—Uncorrected Proofs

CLINICAL TRIAL AND RESEARCH DATA 49 Egan added that FDA has a huge repository of raw data from clinical trials. He envisioned a future where academics could submit queries; then, if an FDA statistician approves and funding is obtained, analysis could be run internally, with the results given back to the researchers. Silvana Borges noted, however, that while FDA has access to a wealth of clinical trial data, most of it is proprietary data that will require consent from the companies who acquired the data. More than FDA willingness will be required, she said, to engage in that conversation with sponsors and others in the scien- tific community. Harmonizing and sharing preclinical data represents another and possi- bly more difficult challenge because preclinical research is even more siloed than clinical research, said Hicks. However, she said that One Mind tried to do this in the traumatic brain injury field by identifying some common data elements. One factor that limits the interoperability of platforms focused on discovery research is a lack of incentives for funders to initiate projects that go beyond what their organization or their country is doing. Horgan noted that technology companies such as Google, Apple, and Microsoft have begun to invest heavily in these areas because they see a business case for it. Moreover, he said, they have the best incentive and the best personnel to think through some of the data sharing, data processing, discoverability, and interoperability challenges. Horgan added, however, that an additional challenge is the “first mover disadvantage” whereby the developers of the earliest tools may have trou- ble sustaining their leading edge, and users may be reluctant to choose a new tool or platform because of the likelihood that something better will shortly become available. Consequently, said Horgan, although technology has advanced quickly, investigators have found it difficult to navigate the tool ecosystem and this has put a damper on scientific discovery. “We’re left with hard choices about spending today’s money on something that may not be sustainable, and we’re not budgeting for the transition from whatever we write into our budgets today and something better down the road,” he said. PREPUBLICATION COPY—Uncorrected Proofs

PREPUBLICATION COPY—Uncorrected Proofs

Next: 8 Genetic Data »
Neuroscience Data in the Cloud: Opportunities and Challenges: Proceedings of a Workshop Get This Book
×
Buy Paperback | $45.00
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

The cloud model of data sharing has led to a vast increase in the quantity and complexity of data and expanded access to these data, which has attracted many more researchers, enabled multi-national neuroscience collaborations, and facilitated the development of many new tools. Yet, the cloud model has also produced new challenges related to data storage, organization, and protection. Merely switching the technical infrastructure from local repositories to cloud repositories is not enough to optimize data use.

To explore the burgeoning use of cloud computing in neuroscience, the National Academies Forum on Neuroscience and Nervous System Disorders hosted a workshop on September 24, 2019. A broad range of stakeholders involved in cloud-based neuroscience initiatives and research explored the use of cloud technology to advance neuroscience research and shared approaches to address current barriers. This publication summarizes the presentation and discussion of the workshop.

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  6. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  7. ×

    View our suggested citation for this chapter.

    « Back Next »
  8. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!