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Developing an end to end Standard & Automated Risk Based Monitoring
Process.
AR01
Giuseppe Di Monaco,
UCB BioSciences GmbH, Monheim, Germany
Tim Williams,
UCB BioSciences, Raleigh, USA
2
ן Risk Based Monitoring (RBM) • Ensure quality of clinical trials by
- Identifying, assessing, monitoring, mitigating risks which could affect quality or safety of a study.
INTRODUCTION2
ן Key Risk Indicator (KRI)• A metric used to assess risk associated with an activity
ן Three major areas of Data Surveillance at UCB• Data Quality & Validity• Patient Safety• Site Compliance and Performance
ן Central Data Surveillance Oversight activities• Identify issues & risks• Relevant signals are monitored
ן RBM Programming Team• Creates KRI analysis datasets from SDTM/ADAM datasets
BACKGROUNDן Design Problems
• 3 areas of data surveillance are broad and complex• Lifespan of the RBM Programs
• Total Number of RBM Programmers
• Lifecycle of KRI programs
• Monolithic programs focused on KRIs Functional requirements (FR)
- FR describes what the program generates
- NFR describes how the program is built
ן Impacts• Maintainability
- Is it easy to understand the program code?• Usability
- Is it easy to use?
• Scalability - Can it handle a growing amount of work?
• Communicability - Is it easy to share information?
3
ASPECTS COVERED IN THIS PRESENTATION4
4
Usability&
Maintainability
Improve QualityImpact in ROI
Increases AccuracySimplicity
Round The Clock Operations
Automated Notification
Compliance (Audit Trails)
Externalization
Operation Efficiency
Modularization
Cost Effective
Quick To Deploy
Full Automation
Increases Productivity
Enable Collaboration
Adherence to Company Standards
Comprehensive &
Intuitive
1 2
3
4
5
67
8
9
10
4
End to End S&A RBM
Process
OBJECTIVES & STRATEGY – Zoom-Out5
ן End to End S&A
• RBM Data Surveillance process and adjacent areas re-engineered/improved/automated
STEP 1: AutoconfigurationSTEP 2: Check dataSTEP 3: Copy data STEP 4: Email notificationSTEP 5: Close previous runs
STEP 6: Adapt to standardsSTEP 7: Load study settingsSTEP 8: Run, compare, validate
STEP 9: Archive STEP 10: Quality, sanity checks
1 2 3 4 5 6 7 8 9 10
Input DataSDTM/ADAM
KRI Analysis Datasets
Programs
End to End Standard & Automated Risk Based
ZOOM-OUT
ZOOM-IN
10 major steps. We focus on step number 8
Full Automation
Automation of Notifications SMTP (Simple Mail Transfer Protocol)
1 2 3 4 5 6 7 9 10START
OBJECTIVES & STRATEGY – Zoom-Out
Study A
SDTM/ADAM Unzip by IT
DAMsAdvance Analytic
Programmers CSRMs
KRI Study X
KRI Study BStudy B
Study X
KRI Study A
Automated Notification
10 STEPS End-to-End S&A Full Automation
Automated Notification
Automated Notification
6
ן Types of modules:
• Process support (utilities), functional modules (KRI analysis creation)
ן Modular Programming
• MODULES: collection of data & function abstractions
• ZOOM-IN in to the NFR (Non-Functional Requirements)
ן Benefits
• Maintainability: minimal effort to repair• Usability: Debugging automation
• Scalability: Able to handle a growing amount of work
• Communicability: Automated email notifications
• Traceability: Same filenames for è .sas, .txt, .log, .sas7bdat, .xls
ן Example• ut_tstcd_mahap_dat_u.sas è Utility• v_dva_mahap_dat_u.sas è KRI “Low Variability”
OBJECTIVES & STRATEGY – Zoom-In Usability &Maintainability
7
Simplicity
Modularization
Externalization
Usability &Maintainability
KR
I M
acro
sU
tility
Mac
ros
Run all New Macros
Iut_kricreatval.sas
10987654321
New Process
DESIGN & IMPLEMENTATIONSTAR SCHEMA
Run all Old Programs
Old Process
III
Generate KRI output analysis datasets
“Low Variability” KRIv_dva_mahap_dat_u.sas
IV
II
ut_tstcd_mahap_dat_u.sas(Select study specific –TESTCD)
Generate values for macro variables and temporary datasets
Modularization ExternalizationFull Automation
8
10987654321
9
Modularization Externalization
Utility Macros
ut_kricreatval.sas
“Low Variability” KRIv_dva_mahap_dat_u.sas
IIIII
IV
ut_tstcd_mahap_dat_u.sas(study specific –TESTCD)
Study B –TESTCD = DIABP, SYSBP, PULSE, TEMP, WEIGHT
Study A–TESTCD = DIABP, SYSBP, PULSE
Input dataset Study A VS
Input dataset Study B VS Output dataset Study A VS
V
Output dataset Study B VS
Cal
l Mac
ro
Functional Macros
DESIGN & IMPLEMENTATION - STAR SCHEMA
DESIGN & IMPLEMENTATION PROCESS DEMO
10ModularizationSimplicity Externalization
Automated Notification
Usability &MaintainabilityFull Automation
Conversion to Knowledge Graph
CODE MANAGEMENT WITH A KNOWLEDGE GRAPH
xxxxxx.SASParse
Convert
Map
Query,Visualize
Ontology
RDF Knowledge
Graph
11
CODE MANAGEMENT WITH A KNOWLEDGE GRAPH
Modularization
Simplicity
Externalization
Automated Notification
Usability &Maintainability
Full Automation
12
CODE MANAGEMENT WITH A KNOWLEDGE GRAPHEnforcing rules
Macro calls in program ut_kricreatval.sas
ExternalizationUsability
&Maintainability
SimplicityModularization
13
CODE MANAGEMENT WITH A KNOWLEDGE GRAPHImpact Analysis Demo ModularizationSimplicity Externalization
Automated Notification
Usability &Maintainability
Full Automation
14
CONCLUSIONSן SUMMARY
• Design Problems
ØData Surveillance areas are broad and complex
ØKRI Monolithic programs are difficult and expensive to maintain
ØHigh lifespan and Large scale system
ØHigh Number of RBM Programmers
• Solution
ØS&A Process covering RBM Data Surveillance process and other areas
• Strategy
ØZOOM-OUT è 10 Steps process
ØZOOM-IN è Macros Functionalities
ן CONSIDERATIONSØS&A is an Organizational attitude è Need for support of managers and colleaguesØCaution è Do not fall into the "prototype trap"
15
CONCLUSION - CONTINUED16
16
Usability&
Maintainability
Improve QualityImpact in ROI
Increases AccuracySimplicity
Round The Clock Operations
Automated Notification
Compliance (Audit Trails)
Externalization
Operation Efficiency
Modularization
Cost Effective
Quick To Deploy
Full Automation
Increases Productivity
Enable Collaboration
Adherence to Company Standards
Comprehensive &
Intuitive
1 2
3
4
5
67
8
9
10
16
End to End S&A RBM
Process
The authors would like to thank
Brigitte Bernhoff
Without her contribution all this would have not been possible.
ACKNOWLEDGMENTS17
Questions?18
Thanks!