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Implementing Clinical Data Warehouse using CDISC for analysis and management of clinical trials

Friday, September 14, 2018 — Poster Session V

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

Authors

  • J Singh
  • H Kandaswamy
  • JD Otoo
  • P Gumne
  • I Kaur
  • S Debrincat
  • C Whalen
  • M Tartakovsky

Abstract

International Clinical Research Team provides biomedical research and clinical data management support for variety of clinical studies sponsored by National Institute of Allergy and Infectious Diseases. Global dispersion and complex protocol designs require continuous management and predictive analytics for investigators and study teams to ensure capture of accurate and consistent data. Efficient oversight of clinical research process demands capture of standardized, and high-quality data in timeliest manner possible to protect participant safety and accuracy in study monitoring, and decision-making. We implemented integrative solution for management of global clinical studies using CDISC CDASH and SDTM standards. Capturing the data with CDASH structured electronic (or paper) CRFs facilitated creation and mapping from clinical database into standardized data warehouse modeled on SDTM standards with ability to have automated extraction, transformation and loading. Warehouse provides platform for generating timely safety reports in standardized format and visualizations of critical study data elements and validation process targeted by study teams for scrutiny. CRF creation using CDASH and data collection visualizations provide efficient approach for protocol management, timely response tracking for safety data, specimen collection, among other visuals and centralized reporting framework. It reduces time for validation and provides tool that augments quality control. This framework provides an effective approach for creation of study analysis and creation of security and monitoring reports for study team in a simplified and efficient platform. Implementing CDASH, CDISC data model, and visualizations provides a model for highly efficient clinical research data management that considerably augments participant safety.

Category: Computational Biology