should the lead dm be the “conductor” of the extensive ...... · approach to monitoring...
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The intelligent choice for clinical trials
Should the Lead DM be the “conductor” of the extensive orchestra that is Data Management?
Kirsten Bulpitt10 MAR 2020
cmedresearch.com
Evolution for Clinical Data Management
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Where we were:
• Limited external data
• Minimal use of integration with EDC database
• Research “savvy” sites and vendors
• Review based on data listings
• All data were “equal”
Limited snapshots of data and lock requirements
Locked
All data in All data reconciledAll tasks completed
Data complete
Listing based manual reviewAll data treated equally
Review of all data
Limited integrations between systems
IXRS
Few vendors with limited third party data.
Large, industry-knowledgeable
vendors
Third party data
Entry screensEdit checks
Straightforward dynamics
EDC Database
Lead CDM
Where are we and what is changing?
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Where we are now:
• Increasing external data• Increased use of
integrations with EDC database
• Variety of review tools• Listings• Visualisations• Dashboards
• Increased focus on risk based and targeted approaches
• Increased specialist research naive users and vendors
Frequent snapshotsMore locks for analysis
Frequent data locks
All relevant data in All data reconciledAll tasks completed
Data complete
Listing based manual reviewTargeted review based on data criticalityVisualisations for trends
Review of all dataComplex protocols
Complex dynamics and calculationsIncreasing amendments
EDC Database
Lead CDMDiverse third party vendorsIndustry naive and specialist
Third party data
Increasing range of system integrations
Integrations
Where are we and what is changing?
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cmedresearch.com
The Evolution of Clinical Data Management to Clinical Data Science: SCDM June 2019
The future?
• Increasing technology
• Increasing diversity
• Increasing trial complexity
• Increased risk based management of data
• Increase in automation with Artificial Intelligence and Machine Learning
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Where are we and what is changing?
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Regulatory influenceICH –E6(R2)
Risk based approach. Risk identification, management and mitigation as well as data integrity
MHRA ‘GxP’ Data IntegrityEU guidance on the data integrity expectations that
should be considered by organisations involved in any aspect of the pharmaceutical lifecycle
Evolutions in technology and risk management processes offer new opportunities to increase
efficiency and focus on relevant activities
• Manage quality throughout all stages of the trial process using a risk-based approach
• Use of a systematic, prioritized, risk-based approach to monitoring clinical trials
• Sponsor should ensure oversight of any trial-related duties and functions carried out on its behalf
The degree to which data are complete, consistent, accurate, trustworthy, reliable
and that these characteristics of the data are maintained throughout the data life cycle
• Risk based approach to management of data• Data criticality and risks• Validated systems• Robustly designed and validated procedures
for data generation, transfer, processing and storage
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Technology influence
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Data Managers have typically managed all aspects of a trial having knowledge of the processes and documentation to collect, manage and validate the data.
So where do we go from here?
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So what does this mean?
Specialise
Multiple siloed roles• Integrations• Third party• Database• Validate• Holistic review• Data flow
Diversify
Single oversight role• Data acquisition
• Technical Specialists• Data Flow
•• Data Review
• Programming Specialists
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To specialise…
Integration Specialist
Programming specialist
Clinical Database Specialist
Database programmer
Third party data specialist
Programming specialist
Third party data reviewer
Data review specialist
Analyst programmers
Validation specialist
Programming specialists
Database programmers
Data flow specialist
What then of the end-to-end data strategy for a study?
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More in terms of planning, implementation and adjustment of requirements and strategy for the data.
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To diversify…
What is required to comply with the protocol?
What are the sources?
How is the data coming to us?
What is the timing and frequency?
Data acquisition Data Flow Data Review
When are we expecting the various data?
How do we ensure it is complete?
What are the risks?
What is the impact?
What is most important?
When is it important?
How can it be validated?
How does it “fit” with the rest of the data?
What is the impact?
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Key collaborations
Data collectionQueries
Data issues
SAE consistencyMedical review/queries
Medical/clinical issues
Statistician: relevance and issues Statistical Programmers: Issues and data structures
Key clinical decisionsStudy progress
Technical set-up & requirementsData flow
Data consistency
External Vendors (providing
data)Sites / CRAs
PVG / Medical
Biostatistics
Clinical LeadLead Clinical
Data Manager
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Protocol Understanding the indication
and therapeutic area nuances.
Understanding of objectives of the study.
Clarity on what the “important” data are and potential impact
on analysis.
CollaborationClear on what the sources
are.
Input into requirements for acquisition.
Clear on expected data flow and what are the risks.
Integral member of the study team
Proactively seeking specialist input (programmers,
statisticians, medical monitors)
Convey the “big picture” to ensure holistic approach
Data Sources and Flow ProcessesUnderstanding of risks in
management of data.
Approaches to validation of data
Quality in terms of impact – when is action required
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“Conductor” - knowledge and experience
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The Future of Clinical Data Management
Technology Advances
eSourceSensors & wearablesArtificial intelligenceNatural language processingVisualisations
Centralized & Risk Based Data Monitoring
Changes in data quality expectationsReliability of dataData & executionIdentification & mitigation of risks
Data Diversity
LaboratoriesImagingeCOABiomarkersGenomics
Data Volume
Increase data flow complexityAggregated reviewAdaptive & complex protocol designs
Clinical Data Science?
Broader Oversight
Increased Interactions
Increased breadth of knowledge
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Final Thoughts
Change is inevitable• Technology advances will continue• Scientific and medical advances will provide more data
Data Management already have many important skills-sets• We understand data• We understand databases and data transfer• We understand monitoring of data flow
However, if we are to ensure end-to-end control of the data, Data Management will need to broaden and deepen our understanding of
• The diversity of external data and “competence” data providers• The techniques to review increasing volumes of data • The meaning and implications of data integrity and quality
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If not us, then who?
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Its body has evolved for speed and agility: long legs, elongated spine, adapted claws for grip and a long tail for balance.
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Thank you!
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