April 2010
Volume 51, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2010
Standardizing Ophthalmic Database Structures Streamline Electronic Submissions and Reduce Startup Time/Cost and Errors
Author Affiliations & Notes
  • J. M. Gonzalez
    Data Management, Statistics and Data Corporation, Tempe, Arizona
  • Q. D. Doan
    Data Management, Statistics and Data Corporation, Tempe, Arizona
  • E. D. Enugandula
    Data Management, Statistics and Data Corporation, Tempe, Arizona
  • M. B. Hall
    Data Management, Statistics and Data Corporation, Tempe, Arizona
  • J. Tang
    Data Management, Statistics and Data Corporation, Tempe, Arizona
  • D. Kennedy
    Data Management, Statistics and Data Corporation, Tempe, Arizona
  • Footnotes
    Commercial Relationships  J.M. Gonzalez, Statistics and Data Corporation, E; Q.D. Doan, Statistics and Data Corporation, E; E.D. Enugandula, Statistics and Data Corporation, E; M.B. Hall, Statistics and Data Corporation, E; J. Tang, Statistics and Data Corporation, E; D. Kennedy, Statistics and Data Corporation, E.
  • Footnotes
    Support  None.
Investigative Ophthalmology & Visual Science April 2010, Vol.51, 5371. doi:
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      J. M. Gonzalez, Q. D. Doan, E. D. Enugandula, M. B. Hall, J. Tang, D. Kennedy; Standardizing Ophthalmic Database Structures Streamline Electronic Submissions and Reduce Startup Time/Cost and Errors. Invest. Ophthalmol. Vis. Sci. 2010;51(13):5371.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose: : To develop standard ophthalmic data structures that are consistent with the Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM). SDTM guidelines do not completely address ophthalmic data structures. The CDISC-SDTM compatible data structures we develop support a more streamlined, higher quality electronic submission of study datasets to the FDA. This is because non-standard data structures make the evaluation of data across studies challenging and time consuming. Standardization also reduced study startup time/cost and database development error rate.

Methods: : SDTM guidelines were reviewed to evaluate data formats, variables, and controlled terminology for applicability to ophthalmic data. We identified common exams that required standardization of database structures. For example, we classified data points within ophthalmic tests to be modeled consistently with SDTM guidelines. We created standardizations for both case report forms (CRFs) and ophthalmic domains. The standards were tested using two independent clinical data management systems (CDMSs). We then examined study startup time and error rate for comparable studies pre- and post- standards development.

Results: : The use of standard ophthalmic data structures streamlines the FDA electronic submission process by eliminating the requirement to restructure this data for CDISC-SDTM compliance. It is independent of CDMS platform. The time required for database development and the related error rate were reduced by 40% and 30% respectively.

Conclusions: : Standard ophthalmic data structures need to be created to support CDISC-SDTM compliant FDA submissions. They help streamline the electronic submission of study data to the FDA. Furthermore, the use of these standards results in a significant reduction in the time and error rate associated with database development. The challenge now is to expand this approach to cover all common ophthalmic data. To do so, we propose that CDISC create a forum for the ophthalmic community to develop data structure standards to be included in the SDTM guidelines.

Keywords: clinical research methodology • clinical (human) or epidemiologic studies: systems/equipment/techniques • clinical (human) or epidemiologic studies: outcomes/complications 
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