presents
Implementing a clinical data management randomization system aimed to satisfy research regulationsMichael Duvenhage, Operations Manager
National Institute of Allergy and Infectious Diseases, NIH
Clinical Data Management Support Operational Support Overview
Cambodia = 5
Thailand = 1
Cameroon = 2
China = 2India = 3
Mali = 31
Tanzania = 2
Uganda = 26South Africa = 3
Ghana = 1
Korea = 2Burkina Faso = 1
USA = 3
Senegal = 1
Overview• Countries: 15• Active DataFax Studies: 81• DataFax Studies in Setup: 5• Archived Studies: 11 (previous server)• Studies Terminated without Stating: 2• Total CRF pages: 1,856,625• Active Users: 204
Bangladesh = 1
256473
122700
148262128458
0
50000
100000
150000
200000
250000
300000
2015 - Q4 2016 - Q1 2016 - Q2 2016 - Q3
CRF Page submissions into DataFax(2015 Q4 – 2016 Q3)
ALASKA (USA)
CANADA
UNITED STATES AMERICA
MEXICO
GREENLAND(DENMARK)
ICELAND
FARCA ISLANDS UNITEDKINGDOM
FRANCE
VENEZUELA CENTRAL
AFRICAN
REPUBLIC
DEMOCRATIC
REPUBLIC
OF CONGO
ALGERIA
NIGERMALICHAD
SUDANERITREA
TUNISIA
MAURITANIA
WESTERN
SAHARA
SENEGAL
GUINEA
LIBERIA
NAMIBIA
ZIMBABWE
ANGOLAZAMBIA
BOTSWANA
SOUTHAFRICA
KENYA
TANZANIA
CONOGO
CABON
CAMEROON
NIGERIA
BURKINAFASO
COLOMBOA
GUYANA
SURINAMEFRENCHGUIANA
ECUADOR
PERU
BOLIVIA
BRAZIL
PARAGUAY
ARGENTINACHILEURUGUAY
BELIZEA
HONDURAS
NICARAGUA
GUATEMALA
SALVADORCOSTA RLCA
PANAMA
HAITI DOM.CUBU
BAHAMAS
G.B.
SIERRA L. LVORY
COAST
TOGO
BENION
LES.
SWA.
MADAGASCAR
MALAWI
BU.
SWI.
GARMANYBEL.
NORWAY
DENMARK
ETHIOPIA
JAM.
MOZAMBIQUE
SOMALIA
LIBYA EGYPTSAUDI
ARABIAOMAN
UAE
FRANCE
IRAQ IRAN
SYRIA
TURKEY
PAKISTAN
AFGHANISTAN
INDIA
TURKMENISTAN
UZBEKISTAN
KAZAKHSTAN
SRI LANKA
JORDAN
CYP.
GREECE
LTALY
BUL.BOS.
HUN.
AUS.SWI. M.
UKRAINE
POLANDGARMANY
KUWAIT
QATER
M.
ROMANIA
HOL.
MOROCCO
PORTUGAL SPAIN
RUSSIA
MONGOLIA
INDIA
C H I N A
I N D O N E S I A
A U S T R A L I A
BHUTAN
BANGLADESH
MYANMAR
SRI LANKA
N.KOREA
S.KOREA
JAPAN
TAIWANLOAS
THAILAND
CAMB. VIETNAM
MALAYSIABR.
PAPUA
NEW-GUINEA
NEW-ZEALAND
SWEDEN
EST.
LIT.
LAT.
FINLAND
S.
A.
BELARUS
SOL.
Mali Summary• Active DataFax Studies: 31• DataFax Studies in Setup: 1• Labs: LMIV (Laboratory of Malaria Immunology and
Vaccinology), LMVR (Laboratory of Malaria and Vector Research )
• Study Indications: Malaria, Falciparum, Lassa Fever, M. Africanum TB
• Total CRF pages: 1,290,241
MALI
ICER - Mali
ALASKA (USA)
CANADA
UNITED STATES AMERICA
MEXICO
GREENLAND(DENMARK)
ICELAND
FARCA ISLANDS UNITEDKINGDOM
FRANCE
VENEZUELA CENTRAL
AFRICAN
REPUBLIC
DEMOCRATIC
REPUBLIC
OF CONGO
ALGERIA
NIGERMALI
CHADSUDAN
ERITREA
TUNISIA
MAURITANIA
WESTERN
SAHARA
SENEGAL
GUINEA
LIBERIA
NAMIBIA
ZIMBABWE
ANGOLA
ZAMBIA
BOTSWANA
SOUTHAFRICA
KENYA
TANZANIA
CONOGO
CABON
CAMEROON
NIGERIA
BURKINAFASO
COLOMBOA
GUYANA
SURINAME FRENCHGUIANA
ECUADOR
PERU
BOLIVIA
BRAZIL
PARAGUAY
ARGENTINACHILE
URUGUAY
BELIZEA
HONDURAS
NICARAGUA
GUATEMALA
SALVADORCOSTA RLCA
PANAMA
HAITI DOM.CUBU
BAHAMAS
G.B.
SIERRA L. LVORY
COAST
TOGO
BENION
LES.
SWA.
MADAGASCAR
MALAWI
BU.
SWI.
GARMANYBEL.
NORWAY
DENMARK
ETHIOPIA
JAM.
MOZAMBIQUE
SOMALIA
LIBYA EGYPTSAUDI
ARABIAOMAN
UAE
FRANCE
IRAQ IRAN
SYRIA
TURKEY
PAKISTAN
AFGHANISTAN
INDIA
TURKMENISTAN
UZBEKISTAN
KAZAKHSTAN
SRI LANKA
JORDAN
CYP.
GREECE
LTALY
BUL.BOS.
HUN.
AUS.SWI. M.
UKRAINE
POLANDGARMANY
KUWAIT
QATER
M.
ROMANIA
HOL.
MOROCCO
PORTUGAL SPAIN
RUSSIA
MONGOLIA
INDIA
C H I N A
I N D O N E S I A
A U S T R A L I A
BHUTAN
BANGLADESH
MYANMAR
SRI LANKA
N.KOREA
S.KOREA
JAPAN
TAIWANLOAS
THAILAND
CAMB. VIETNAM
MALAYSIABR.
PAPUA
NEW-GUINEA
NEW-ZEALAND
SWEDEN
EST.
LIT.
LAT.
FINLAND
S.
A.
BELARUS
SOL.
Uganda Summary• Active DataFax Studies: 26• DataFax Studies in Setup: 0• Labs: LMIV (Laboratory of Malaria Immunology
and Vaccinology), LIR (Laboratory of Immunoregulation)
• Study Indications: Malaria, Tuberculosis, HIV, Cryptococcal Meningitis, Syphilis, Liver Cancer, Fibrosis, Lymphoma Non-Hodgkin
• Total CRF pages: 431,060
UGANDA
ICER - Uganda
ALASKA (USA)
CANADA
UNITED STATES AMERICA
MEXICO
GREENLAND(DENMARK)
ICELAND
FARCA ISLANDS UNITEDKINGDOM
FRANCE
VENEZUELA CENTRAL
AFRICAN
REPUBLIC
DEMOCRATIC
REPUBLIC
OF CONGO
ALGERIA
NIGERMALICHAD
SUDANERITREA
TUNISIA
MAURITANIA
WESTERN
SAHARA
SENEGAL
GUINEA
LIBERIA
NAMIBIA
ZIMBABWE
ANGOLAZAMBIA
BOTSWANA
SOUTHAFRICA
KENYA
TANZANIA
CONOGO
CABON
CAMEROON
NIGERIA
BURKINAFASO
COLOMBOA
GUYANA
SURINAMEFRENCHGUIANA
ECUADOR
PERU
BOLIVIA
BRAZIL
PARAGUAY
ARGENTINACHILEURUGUAY
BELIZEA
HONDURAS
NICARAGUA
GUATEMALA
SALVADORCOSTA RLCA
PANAMA
HAITI DOM.CUBU
BAHAMAS
G.B.
SIERRA L. LVORY
COAST
TOGO
BENION
LES.
SWA.
MADAGASCAR
MALAWI
BU.
SWI.
GARMANYBEL.
NORWAY
DENMARK
ETHIOPIA
JAM.
MOZAMBIQUE
SOMALIA
LIBYA EGYPTSAUDI
ARABIAOMAN
UAE
FRANCE
IRAQ IRAN
SYRIA
TURKEY
PAKISTAN
AFGHANISTAN
INDIA
TURKMENISTAN
UZBEKISTAN
KAZAKHSTAN
SRI LANKA
JORDAN
CYP.
GREECE
LTALY
BUL.BOS.
HUN.
AUS.SWI. M.
UKRAINE
POLANDGARMANY
KUWAIT
QATER
M.
ROMANIA
HOL.
MOROCCO
PORTUGAL SPAIN
RUSSIA
MONGOLIA
INDIA
C H I N A
I N D O N E S I A
A U S T R A L I A
BHUTAN
BANGLADESH
MYANMAR
SRI LANKA
N.KOREA
S.KOREA
JAPAN
TAIWANLOAS
THAILAND
CAMB. VIETNAM
MALAYSIABR.
PAPUA
NEW-GUINEA
NEW-ZEALAND
SWEDEN
EST.
LIT.
LAT.
FINLAND
S.
A.
BELARUS
SOL.
India Summary• Active DataFax Studies: 3• Labs: LIR (Laboratory of Immunoregulation), LPD
(Laboratory of Parasitic Disease)• Study Indications: Pulmonary Tuberculosis,
Lymphatic Filarial Disease, Helminth in Latent TB• Total CRF pages: 62,753
INDIA
ICER - India
Required a system that allowed for sequential randomization allocation (allowing stratification) across 9 sites in South Africa and China
01Fixed Randomization
System needed to leverage benefits current provided by IVRS/IWRS.
02 IVRS/IWRS Benefits
The Randomization workflow and data storage had to be seamlessly integrated into the CDMS (DataFax)
03 Integration
Randomization Tool have to comply to various regulations including GCP and 21 CFR Part 11.
04 Compliance
Communication had to be enhanced to become more automated and system driven
05 Communication automation
Reduction in complex user interaction with Randomization Tool as well as reduction in setup requirements (i.e. data mappings)
06 Complexity Reduction
Randomization Tool Requirements
Traditional IVRS/IWRS Challenges
Traditional IVRS/IWRS Challenges
Access to phones, toll-free numbers or ability to phone globally
Language and voice translation requirements - especially for global clinical trials
Technology challenges - especially if complex randomization criteria (e.g. inputtinglaboratory data into IVRS/IWRS or complex study designs influencing randomization)
Time zone challenges and helpdesk availability
Regional settings (e.g. date formatting differences like dd/mm/yyyy vs. mm/dd/yyyy)
Data mapping challenges from the IVRS/IWRS system to the CDMS system
Updates to IVRS/IWRS system – data mapping challenges to the CDMS system
IVRS/IWRS System costs
Additional external data mapping costs (mappings programming and validation into the CDMS)
Traditional Workflow vs. “CDMS” Workflow and quality risks
CDMS (Clinical Data
Management System)
Data Mappings
Sponsor
Monitors
Clinical Drug Suppliers Database
IVRS/IWRS (Interactive Voice/Web Response Systems)
PatientRandomizationby IVRS/IWRS
Data Mappings
Sponsor
Monitors
Clinical Drug Suppliers Database
CDMS (Clinical Data
Management System)
Patient Randomizationby CDMS
PI / Clinician responsible for randomization
QR2
QR1
QR3
QR3
QR1
Quality Risks
QR1 – Using IVRS/IWRS or CDMS systemcorrectly as well as user authentication
QR2 – Data mappings to CDMS (traditionalmodel only)
QR3 – Data reporting / mappings toClinical drug suppliers
Randomization Workflow
Baseline (eDC) Week 4 (eDC) Week 16 (eDC) Randomization Tool (eDC)
Image reviewer Image reviewer Site Study Coordinator Site Randomization Coordinator
Randomization Notifications
Data Manager (Backup)
DataFax(CDMS)
Responsible Person
Data Mapped into Randomization Tool
DataFax Randomization Tool
Baseline and Week 4 Radiology criteria entered via
EDC into CDMS
Week 16 data entered via EDC into CDMS
Randomization to be performed by specific site
users. CDMS will not allow randomization if all criteria
are not met.
Open label randomization arm provided.
Can also be a blinded
treatment number
CDMS Randomization development considerations
Randomization Table development
Randomization table needs to be received, uploaded (as lookup-table) and appropriately protected in the CDMS prior to the database go-live event. User access should be completely restricted and changes should not be allowed. Important to built in controls that disallow any view-access to the Randomization schema
Lookup Table in DFSetup
CDMS Randomization development considerations
Integration of Eligibility criteria into Randomization
CDMS should immediately reject the randomization of patients not meeting the eligibility criteria as entered. This can be done with the use of interactive prompt messages when the user attempts to randomize a patient
CDMS Randomization development considerations
User access management
Users responsible for randomization do not necessary need access to all CRF pages. Access may be restricted to only the Randomization form depending on study needs. System initiated usernames and passwords should be provided for additional level of security during the randomization process.
CDMS Randomization development considerations
Randomization protection
The randomization should be protected after completion (no changes should be allowed onthe form post-randomization).
CDMS Randomization Demonstration
Randomization Tool - Edit Checks
Plate Enter• Map Criteria data from previous
pages• Check if patient is already
randomized (If Patient is already Randomized then make page view-only)
Field Exit• Ensure that all criteria = “Yes”, if
not then do not allow to randomize
Plate Exit• If patient is to be randomized
then invoke dfpassword• Autofill Date (dftoday)• Autofill Time (dftime)• Arm (dflookup)
Randomization Tool Risks
Technology is sufficiently tested before implementation
Randomization workflow/functioning should be tested as part of the CDMS validation
Change control testing
Database changes need to be carefully considered and tested before implementationespecially since randomization functionality may be influenced as a result of other CDMSchanges.
CDMS Downtime
CDMS (DataFax downtime (due to upgrades or other planned outages) should be carefullyconsidered as it may impact randomization activities (which are extremely timely)
Advantages using CDMS (DataFax) for Randomization
Advantages using Data
Reducing the number of system(s) reduces project overhead, validation requirements and project complexities
Study cost reduction
Centralized helpdesk (for CDMS as well as randomization system)
Elimination of separate account details required for IVRS/IWRS systems
Ability to interact/review CRF data (e.g. Screening data) to ensure randomization performed correctly
User authentication per CDMS
Inherent audit trials (including date/time stamps) from the CDMS
Randomization data contained with clinical research data
Acknowledgements
Dr. Clifton E. Barry , Dr. Laura Via, Dr. Ray Chen, Chrissie Cai, Lisa Goldfeder -Tuberculosis Research Section, Laboratory of Clinical Infections Diseases (LCID), NIAID, NIH
Dr. Lori Dodd – Division of Clinical Research (DCR), NIAID, NIH
Lisa Hoopengardner - Clinical Research Directorate/Clinical Monitoring Research Program, Leidos Biomedical Research, Inc
Jennifer Xiao, Cindy Lassnoff - NET ESOLUTIONS CORPORATION
Kevin Newell, Christopher Whalen - Research Data and Communication Technologies, Inc.
Alexander Rosenthal, Michael Tartakovsky - 3Office of Cyber Infrastructure and Computational Biology