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Using a data warehouse model based on CDISC SDTM for centralized data and safety reporting for clinical trials

Friday, September 16, 2016 — Poster Session IV

12:00 p.m. – 1:30 p.m.
FAES Terrace
NIAID
COMPBIO-16

Authors

  • KO Newell
  • J Xiao
  • M Duvenhage
  • J Singh
  • P Gumne
  • H Kandaswamy
  • C Whalen
  • A Rosenthal
  • M Tartakovsky

Abstract

This presentation describes the process of developing a statistical reporting framework that relies on common data elements from a hybrid CDISC SDTM data warehouse model, and it will serve as an example for other institutions that seek to create efficiencies by adopting a centralized statistical reporting infrastructure. Our team provides clinical research data management and statistical support for ~100 protocols and develops data and safety reports for clinical trials, which are reviewed by data safety monitoring boards, Food and Drug Administration, and other medical monitoring or safety review committees depending on the trial sponsor. We also use the warehouse to provide study teams with prospective research study metric reports and visualizations to facilitate tracking, planning and implementation of research, which summarize: enrollment and accrual, data quality, safety data, demographics and other research outcomes. Historically, programmers developed these data and safety reports on a protocol-specific basis. Reporting can now be performed centrally from the validated data warehouse environment, which creates many benefits and efficiencies across protocols. We will provide an overview of the critical institutional changes that have been incorporated into the development of study databases within our CDMS that facilitate centralized services. We will highlight where efficiencies across protocols have been achieved, and also where we have found the need for ongoing protocol-specific programming. We will discuss inherent risks involved with this approach, as well as considerations for further refinements and how this could expand into other downstream areas in the future, such as for use in statistical analyses.

Category: Computational Biology