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