Harmonization-Information Trade-Offs for Sharing Individual Participant Data in Biomedicine.
Harmonization-Information Trade-Offs for Sharing Individual Participant Data in Biomedicine.
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DOI：
10.1162/99608f92.a9717b34
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发表时间：
2022
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Biomedical practice is evidence-based. Peer-reviewed papers are the primary medium to present evidence and data-supported results to drive clinical practice. However, it could be argued that scientific literature does not contain data, but rather narratives about and summaries of data. Meta-analyses of published literature may produce biased conclusions due to the lack of transparency in data collection, publication bias, and inaccessibility to the data underlying a publication (‘dark data’). Co-analysis of pooled data at the level of individual research participants can offer higher levels of evidence, but this requires that researchers share raw individual participant data (IPD). FAIR (findable, accessible, interoperable, and reusable) data governance principles aim to guide data lifecycle management by providing a framework for actionable data sharing. Here we discuss the implications of FAIR for data harmonization, an essential step for pooling data for IPD analysis. We describe the harmonization-information trade-off, which states that the level of granularity in harmonizing data determines the amount of information lost. Finally, we discuss a framework for managing the trade-off and the levels of harmonization. In the coming era of funder mandates for data sharing, research communities that effectively manage data harmonization will be empowered to harness big data and advanced analytics such as machine learning and artificial intelligence tools, leading to stunning new discoveries that augment our understanding of diseases and their treatments. By elevating scientific data to the status of a first-class citizen of the scientific enterprise, there is strong potential for biomedicine to transition from a narrative publication product orientation to a modern data-driven enterprise where data itself is viewed as a primary work product of biomedical research. The goal of biomedical research is to produce evidence to understand, prevent, and treat diseases. Doing so requires that scientific data are accurate, available, and generalizable enough to support reliable decision-making in medical practice. Biomedical data are judged by the imperfect process of scientific peer review and published literature rather than raw data sets. Typically, studies are evaluated based on summaries and conclusions, and the raw data from individual research participants remains inaccessible. Literature-based summaries are subject to biases and author interpretations and can mask information hidden in the raw data. Thus, to maximize the return from funding the biomedical research enterprise (estimated at U.S. $240 billion in 2009, worldwide) data must be shared. To promote this, the U.S. National Institutes of Health (NIH) has recently announced their 2023 data-sharing mandate that adheres to the FAIR (findable, accessible, interoperable, reusable) data stewardship principles, guiding researchers, institutions, and agencies to elevate scientific data to 'first-class citizen' status as a product of research. The authors discuss how making data FAIR will strengthen the evidence for medical practice by facilitating the reuse of data from different data sets, an important step in analyzing independent studies together. FAIR requires harmonization to ensure fused data elements convey the same information, producing interoperability. This article articulates the trade-offs that researchers must make during the harmonization process, balancing the level of harmonization of data sets against the level of information lost in doing so. Finally, the authors discuss a framework to help manage the information loss and to increase the potential for harmonization across shared data, readying them for emerging applications of machine learning and artificial intelligence in support of higher levels of evidence in biomedicine.
