Time for NIH to lead on data sharing

Sim, Ida; Stebbins, Michael; Bierer, Barbara E.; Butte, Atul J.; Drazen, Jeffrey M.; Dzau, Victor J.; Hernandez, Adrian F.; Krumholz, Harlan M.; Lo, Bernard; Munos, Bernard H.; Perakslis, Eric D.; Rockhold, Frank; Ross, Joseph S.; Terry, Sharon F.; Yamamoto, Keith R.; Zarin, Deborah A.; Li, Rebecca

doi:10.1126/science.aba4456

Cited by 45 publications

(45 citation statements)

References 8 publications

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“…Assembling them will require extensive data-sharing. Still, despite well-intended policies at funding organizations, there is concern that data-sharing at many organizations remains half-hearted 17, 18 . To reap the full societal benefits of ML, this needs to change.…”

Section: Summary and Discussionmentioning

confidence: 99%

Improving the Prediction of Clinical Success Using Machine Learning

Munos

Niederreiter

Riccaboni

2021

Preprint

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In pharmaceutical research, assessing drug candidates' odds of success as they move through clinical research often relies on crude methods based on historical data. However, the rapid progress of machine learning offers a new tool to identify the more promising projects. To evaluate its usefulness, we trained and validated several machine learning algorithms on a large database of projects. Using various project descriptors as input data we were able to predict the clinical success and failure rates of projects with an average balanced accuracy of 83% to 89%, which compares favorably with the 56% to 70% balanced accuracy of the method based on historical data. We also identified the variables that contributed most to trial success and used the algorithm to predict the success (or failure) of assets currently in the industry pipeline. We conclude by discussing how pharmaceutical companies can use such model to improve the quantity and quality of their new drugs, and how the broad adoption of this technology could reduce the industry's risk profile with important consequences for industry structure, R&D investment, and the cost of innovation.

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Section: Summary and Discussionmentioning

confidence: 99%

Improving the Prediction of Clinical Success Using Machine Learning

Munos

Niederreiter

Riccaboni

2021

Preprint

Self Cite

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“…Openness and the sharing of data enable researchers to collaborate, review and reproduce findings, and such activities strengthen trust. To move forward, journals and funders not only should continue to encourage data sharing, but also must further strengthen the principles of findable, accessible, interoperable and reusable (FAIR) data 3 . Open science should be the norm.…”

Section: Open Sciencementioning

confidence: 99%

Science After Covid-19: Faster, Better, Stronger?

Schwab

Held²

2020

Significance

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“…However, perceived risks of inappropriate reuse and competition have been difficult to mitigate, especially when the current reward system predominantly incentivizes high-impact publications, often based on exclusive data, at the expense of transparency, reproducibility, and data reuse. [19][20][21][22] Disincentives for data sharing are known to have a disproportionate impact on clinical studies as the process of conducting those studies is time, cost, and labor intensive. 3 Yet the role of prevalent disincentives and incentives (e.g., data authorship 23,24 ) for clinical trial data sharing have only recently entered the public realm 3,23,25,26 , in part accelerated by discussions surrounding the ICMJE's data sharing policy 11,27 when many points of agreement and disagreement among stakeholders were articulated.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Clinical Trial Data Sharing Policy in Leading Medical Journals

Danchev

Yan

Borghi

et al. 2020

Preprint

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Background. The benefits from responsible sharing of individual-participant data (IPD) from clinical studies are well recognized, but stakeholders often disagree on how to align those benefits with privacy risks, costs, and incentives for clinical trialists and sponsors. Recently, the International Committee of Medical Journal Editors (ICMJE) required a data sharing statement (DSS) from submissions reporting clinical trials effective July 1, 2018. We set out to evaluate the implementation of the policy in three leading medical journals (JAMA, Lancet, and New England Journal of Medicine (NEJM)). Methods. A MEDLINE/PubMed search of clinical trials published in the three journals between July 1, 2018 and April 4, 2020 identified 487 eligible trials (JAMA n = 112, Lancet n = 147, NEJM n = 228). Two reviewers evaluated each of the 487 articles independently. Captured outcomes were declared data availability, data type, access, conditions and reasons for data (un)availability, and funding sources. Findings. 334 (68.6%, 95% confidence interval (CI), 64.1%-72.5%) articles declared data sharing, with non-industry NIH-funded trials exhibiting the highest rates of declared data sharing (88.9%, 95% CI, 80.0%-97.8) and industry-funded trials the lowest (61.3%, 95% CI, 54.3%-68.3). However, only two IPD datasets were actually deidentified and publicly available as of April 10, 2020. The remaining were supposedly accessible via request to authors (42.8%, 143/334), repository (26.6%, 89/334), and company (23.4%, 78/334). Among the 89 articles declaring to store IPD in repositories, only 17 articles (19.1%) deposited data, mostly due to embargo and regulatory approval. Embargo was set in 47.3% (158/334) of data-sharing articles, and in half of them the period exceeded 1 year or was unspecified. Interpretation. Most trials published in JAMA, Lancet, and NEJM after the implementation of the ICMJE policy declared their intent to make data available. However, a wide gap between declared and actual data sharing exists. To improve transparency and data reuse, journals should promote the use of unique pointers to dataset location and standardized choices for embargo periods and access requirements. All data, code, and materials used in this analysis are available on OSF at https://osf.io/s5vbg/.

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Time for NIH to lead on data sharing

Abstract: A draft policy is generally supportive but should start mandating data sharing

Cited by 45 publications

References 8 publications

Improving the Prediction of Clinical Success Using Machine Learning

Improving the Prediction of Clinical Success Using Machine Learning

Science After Covid-19: Faster, Better, Stronger?

Evaluation of Clinical Trial Data Sharing Policy in Leading Medical Journals

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