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Sharing Clinical Research Data Workshop Summary (2013) / Chapter Skim
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5 Standardization to Enhance Data Sharing
Pages 43-56

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From page 43...
...  The development of standards requires collaborative expert input, analysis, and consensus.  Clinical and scientific expertise is also needed to deter mine how to fit data retroactively to standards and harmonize terminology.
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From page 44...
... Data reuse requires both standards and a level of rigor and semantic specificity sufficient not just for human, but also for computational analysis. For example, she briefly described an effort by the Clinical Trials Network at the National Institute on Drug Abuse to de-identify data, align the data to Clinical Data Interchange Standards Consortium (CDISC)
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From page 45...
... Information is lost when data are gathered in different ways and later mapped to common standards. Standardization also provides opportunities for additional impact on clinical research through increased data quality, better data integration and reusability, facilitation of data exchange and communication with partners, interoperability of software tools, and facilitation of regulatory reviews and audits.
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From page 46...
... Cautions on Standardization While acknowledging the value of data standards, Vicki SeyfertMargolis, senior advisor for science innovation and policy at the Food and Drug Administration's (FDA's) Office of the Chief Scientist, brought up some points researchers should remember when thinking about standardizing data.
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From page 47...
... The development of CDISC consensus standards requires the expert input from thousands of volunteers around the world, said Kush. In some cases, CDISC also works with other SDOs, like HL7 International, which generates standards for the exchange, integration, sharing, and retrieval of electronic health information to support clinical practice and health services areas.
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From page 48...
... While complying with 21 CFR 11 -- regulations that require clinical researchers to implement controls such as audit trails and system validations to ensure that electronic health records are trustworthy and reliable -- it can produce a standard core clinical research dataset, such as that defined by the CDISC Clinical Data Acquisition Standards Harmonization (CDASH) standard.
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From page 49...
... In the first, Fitzmartin described a clinical data integration tool developed by SAS that could be used to map clinical trial submission data in the CDISCdeveloped standard SDTM format to the format required by a liver toxicity assessment product called Electronic Drug-Induced Serious Hepatoxicity, or eDISH. Using this tool, reviewers do not have to spend time piecing these data together and can quickly drill down to the patient-level data to look at outliers and elevated values.
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From page 50...
... were converted to a standard data format with no predetermined scientific questions, then the standardized dataset was used for research. In the second approach, the converted data and the unconverted data were both used to answer a specific scientific question using a program called Amalga, Microsoft software designed to integrate patient data from disparate sources and in different formats.
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From page 51...
... CAMD is working to qualify biomarkers as drug development tools and has also been developing standards to create integrated databases drawn from clinical trials. These databases have been used to model clinical trials to optimize trial design.
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From page 52...
... The modeling tool allowed for accurate quantitative predictions of defined patient populations, Compton said. By merging data from diverse sources, 65-year-old males who looked alike in the databases could be divided into three classes with different trajectories of disease.
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From page 53...
... Clinical trials should not waste patients' time on drugs that are not going to work. "Get me 60 or 70 hypotheses that I can rule out, and then I can be really interested in the one that I cannot." Perakslis concluded that he prefers light and agile data "marts," or databases generated to answer specific questions or test hypotheses, over large data warehouses.
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From page 54...
... First, rules for developing the data standards require collaborative expert input and consensus. Disease definitions need to come from the bottom up, said Compton, from the clinicians who are dealing with patients and diseases.
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From page 55...
... As detailed in the next chapter, the social and cultural aspects of sharing clinical data are much more challenging than the technical issues.
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Key Terms

  • data standards
  • data sharing
  • clinical data
  • sharing clinical
  • data quality

  • electronic health
  • legacy data
  • standards development
  • drug development
  • health records

  • adverse event
  • standardized data
  • data integration
  • standard format
  • development tools

  • critical path
  • data standardization
  • submission data
  • data terminology
  • developing standards

  • modeling tool
  • collaborative expert
  • event reporting
  • sdtm format
  • patient care

  • development organizations
  • translational medicine
  • adverse events
  • fitzmartin described
  • path institute


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