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Jaskiran Singh, Clinical Data ArchitectInternational Biomedical Research Support ProgramOffice of Cyberinfrastructure and Computational Biology, NIAID, NIH
© CDISC 2017
Presentation Outline
• CDISC data standards• Clinical Data Warehouse using CDISC
• Data Model• Data transformation • Warehouse Workflow sources to SVDW• Dashboards & Data Visualization• DSMB Reporting• Data Validation in Clinical Data Warehouse
• Conclusion• Acknowledgements
Clinical Data Standards for a warehouseWhy?
Why are data standards important for our warehouse?• Patient safety – using standardized data models in clinical trials reduces confusion
during analysis.• Allows regulators to streamline the data review process and reduce transformational
effort for DSMB reporting• Ease of hiring new staff. Individuals with CDISC experience hit the ground running,
thereby reducing orientation time and effort. • Improves consistency of semantic interpretation of information.• Allows for exchange of data with increased efficiency, during data collection and
processing.• Provisions the downstream analysis and potential future use of the data in virtual
clinical trials and cross study analysis. e.g. The below example shows how using standard allow a single representation of data.
Birth Date
DOB
Date of Birth
BRTHDTC
Field name not using CDISC standards
Field name using CDISC standards
Extract, Transform, Load, and Validate
Using CDASH for CRF creation will reduce the time and effort spent in mapping fields and domains and time spent on data analysis.
Benefits of using CDASH
Increased time in mapping study variables to SDTM variable
Reduced time as most variables directly map to SDTM
Average time to map a study ranges from 5-6 months
Average time to map a study to SDTM reduced to 1-2 month
Need more training and effort in understanding variables on CRF. No extra training needed
Analysis without CDASH Analysis with CDASHvs
Study Data Tabulation Model (SDTM) provides a standard for organizing and formatting data thereby streamlining the processes in collecting, managing, analyzing and reporting data.
SDTM
Supports data aggregation and
warehousing
Fosters data mining and reuse
Facilitates collaborations,
acquisitions, mergers
Has potential to improve the
regulatory review / approval process
CDASH is for Data CaptureSDTM: Data Tabulation
© CDISC 2017
Building a data model from SDTM
Data Warehouse Data Model using SDTM
Expandable (Can include other
source data elements)
Include variables for study management
RDMS Physical Data Model
Example Demographic DomainExample Demographic Domain
Data transformation from CDMS to Clinical Data WarehouseMore complex if study uses custom data fields
Process of data transformation and loading into the warehouse based on mapping and specification document.
Data transformation from CRF to Clinical Data Warehouse Database
CDASH SDTM
Clinical Data Warehouse Workflow
Clinical Data Management
Systems
Sample Management
Systems
Other Source SystemsData Collection Systems
Data Validation, Storage and Processing using ETL toolProcessing:
Combining records, Standardization,
Archive, Export to warehouse
Clinical Data Warehouse (PostgreSQL)
Ad hoc Query Tool
Report WritersVisualization
Reports
Staging Area
CDISC/SDTM - ETLV
End User Reporting
© CDISC 2017
Software Tools Used for Clinical Data Warehouse
ETL Process Analytical Reports
Pentaho open source community edition delivers
Extraction, Transformation, and Loading (ETL) capabilities.
Tableau software is used for creating analytical reports
Operating system used for Clinical Data Warehouse implementation is Red Hat Enterprise Linux
Database
PostgreSQL
PostgreSQL is a Open source object-relational database
system.
Reporting tools can perform aggregation and transformation of data in the Clinical Data Warehouse to build regulatory and study status reports for ongoing active trials.
Clinical study analytics and reporting is important:
Provides ad-hoc study reports such as those related to demographics summaries, enrollment trends, patient safety outcomes, participant accrual and disposition.
Provides an efficient backbone for generating Data and Safety Monitoring Board reports in a standard format across multiple studies
Simplifies and standardizes the validation effort
Clinical Study Analytics and Reporting
Example Dashboard Report
Integrating Clinical Data Warehouse with Sample Management System
Sample 1 (DNA)
Racks/Shelf
Box 1 Box 2
Sample 2 (Serum)
Global SpecimenDatabase
Clinical Data Warehouse
Reports providing detail information
about freezers, samples, etc
ETLClinical Data Management
SystemETL
Example Sample Detail ReportsThis report provides aggregated date of the subjects based on various dimension and measures.
Our Validation Process
Visual InspectionThrough visual inspection, review the Study Visuals
Database Data Model Specifications against the
SDTMIG standards for each CDISC domain.
Field Data verificationVerify that the data in the fields of each database table is accurate when compared to the source.
Record Count Verification
Perform record counts to verify that all expected records are loaded into
the target tables
Data Validation Techniques ensure integrity in the
clinical data warehouse
Building Safety Reports from the Clinical Data Warehouse
Current Method:
New Method: Removes complexity and overhead without compromising data quality.
CDMS SAS Programming
Data Conversion
to CDISCDSMB
Reports
CDMS
Clinical Data Warehouse
(CDISC Standard)
DSMB Reports
Data Validation
CDASHData Warehouse Model using SDTM standards
Reporting with minimal re-mapping
Data Manager Quality Check
Data Manager Quality Check
Why build the Data Warehouse for Active Clinical Trials?
Separates research and
decision support
functions
Consolidates data
from multiple
sources, models,
and dictionaries to
allow complete and
consistent store of
study data.
Separates research and
decision support
functions from the
operational systems
such as clinical data
management systems,
sample management
systems, LIMS, etc..
Serves as a foundation for
data mining, data
visualization, advanced
reporting (including to Data
Safety and Monitoring Board
(DSMB), Safety Review
committees, etc.) and
Online Analytical Processing
(OLAP) tools
Provides common
standards, like
CDISC, that can be
used for data
exchange, data
integration or data
reporting to a
regulatory body,
such as FDA
Conclusion A clinical data warehouse provides:
• Investigators, research teams and sponsors with a central, standardized, electronic system that serves as a backbone for providing study progress monitoring and automated data and safety reporting.
• Research staff ability to interact directly with warehouse and formulate, ad-hoc, data lookups to understand and make informed decisions for ongoing health of the clinical study
• A relational database linking clinical data to other research data, such as research laboratory outcomes, specimen repository and imaging data.
Acknowledgements
NIAID OCICBMichael Tartakovsky - NIAID CIOAlex Rosenthal – NIAID CTOChristopher WhalenMichael DuvenhageMichael HoldsworthHarish KandaswamyJohn David OtooPaschaline GummeInderdeep KaurFrancis AppiahJennifer XiaoGuo WeiKanwaldeep BajwaNIAID Linux Application Hosting Team
Division of Intramural Research, NIAIDThomas Quinn, MDSteven Reynolds, MDSara Healy, MDPeter Crompton, MDRathy Mohan – Data ManagerAndrew Orcutt – Data ManagerJeff Skinner - Statistician