An introduction to CDISC and CDASH
Bring on the Standards!
Emmanuelle Denis M.Sc., MICR
Global Health Clinical Trials Research Programme
What is CDISC?
Clinical Data Interchange
Standards Consortium
Good data management practices are
essential to the success of a trial because
they help to ensure that the data collected
is complete and accurate.
The objectives of good clinical data
management are to ensure:
• That the trial database is complete, accurate
and a true representation of what took place
in the trial
• That the trial database is sufficiently clean to
support the statistical analysis and its
interpretation
Where do I start?
Start with the protocol
Data to be collected are defined by the protocol’s:
• Primary Objectives
• Secondary Objectives
• Other major pre-planned analysis
Designing the data collection instrument
A well designed Case Report Form (CRF) is key
to obtaining accurate and complete trial data.
CRF design should begin in parallel with
protocol development as the CRF is essentially
the data capture system for the protocol.
Designing the data collection instrument
•
Necessary data only: collect only data that will be used for analysis and avoid
collecting redundant data
•
Statistical Analysis Plan to define essential data that needs to be collected and
ensure that there are no redundancies
•
Ensure that all members of the study team have adequately reviewed the CRFs
before they are finalised
•
Keep the end-user in mind so that CRF is quick and easy for site personnel to
complete. Also, consider the source data.
•
Keep the CRF questions clear and unambiguous to ensure that they do not ‘lead’
•
Avoid collecting ‘free text’ as it will require coding before it can be analysed
•
Use ‘yes/no’ checkboxes whenever possible or to provide ‘picklists’
•
Ensure that translated CRFs are reviewed to ensure that the questions have a
consistent meaning in all languages
•
Prepare CRF completion guidelines to assist site personnel in completing the forms
Adapted from CDISC Clinical Data Acquisition
Standards Harmonization (CDASH), 2008
How should I process my data?
Clinical Data Management Systems
• A clinical data management system or CDMS is
software used in clinical research to manage clinical
trial data
• CDMS used to process data from source through to
validation checks, analysis, reporting and storage
• CDMS also used for coding (particularly for adverse
events and medications)
– MEDRA (Medical Dictionary for Regulatory Activities)
– WHOART (WHO Adverse Reactions Terminology)
FDA 21 CFR Part 11
Many trial sponsors and funding agencies specify
that a CDMS must be 'FDA' compliant.
US FDA 21 CFR Part 11 requirements:
• System validation
• Robust audit trail
• Security access controls
• Specification for system design and edit checks
• Archiving procedures
• Electronic signatures
Free, open source, web-based software for EDC developed
by Akaza Research
Features:
• FDA 21 CFR Part 11 compliant
• 7000 users in 76 countries
• Java-based server system
• Management of diverse clinical studies
• Clinical data entry and validation
• Data extraction
• Study oversight, auditing, and reporting
www.openclinica.org
Paper or electronic CRFs?
• The choice to use paper or electronic influenced by the CDMS in use and the
local infrastructure
• e-CRFs are less expensive to process but need to factor in the extra cost of
computers or mobile devices and internet or GPRS access
• If paper CRFs must think ahead about the flow of data and account for the
extra time that will be required for data entry
• Consider shipping costs for multi-site trials
• Once trial enrolment begins participant information must be entered onto
the trial database as quickly as possible to enable accurate tracking of trial
progress and monitoring of safety data
• Also importance to track the flow of paper CRFs from the study sites to the
coordinating centre.
How do I ensure quality data?
ALCOA Principle
“Data quality” refers to the essential characteristics of each piece of
data; in particular, quality data should be:
•
•
•
•
•
Accurate
Legible
Complete and Contemporaneous
Original
Attributable to the person who generated the data
WHO Handbook for Good Clinical Research Practice
(GCP) : Guidance for Implementation, 2005
ALCOA Principle
Quality data should be:
Accurate
•
Trial database accurately captures the source data
•
Any corrections or changes are documented
•
Audit trail!
WHO Handbook for Good Clinical Research Practice
(GCP) : Guidance for Implementation, 2005
ALCOA Principle
Quality data should be:
Legible
• Clear handwriting on CRFs
• Do not obliterate information when making
changes/corrections
• All data (including meta-data and audit trails) must be in
human-readable form
WHO Handbook for Good Clinical Research Practice
(GCP) : Guidance for Implementation, 2005
ALCOA Principle
Quality data should be:
Complete and Contemporaneous
• Avoid blank data fields or provide explanation (e.g.
unknown, unobtainable, not applicable)
• Data must recorded at the time the activity occurs
• Audit trails to provide evidence of timing
WHO Handbook for Good Clinical Research Practice
(GCP) : Guidance for Implementation, 2005
ALCOA Principle
Quality data should be:
Original
• Original data (e.g. lab results, study questionnaires
• Accurate transcriptions of source data
WHO Handbook for Good Clinical Research Practice
(GCP) : Guidance for Implementation, 2005
ALCOA Principle
Quality data should be:
Attributable
• Who recorded the information
• Only designated study staff should have access to data
• Audit trail!
WHO Handbook for Good Clinical Research Practice
(GCP) : Guidance for Implementation, 2005
References
Food and Drug Administration (FDA). Guidance for Industry Part 11, Electronic Records; Electronic
Signatures - Scope and Application. 2003; Available from:
http://www.fda.gov/CDER/GUIDANCE/5667fnl.htm
CDISC. Clinical Data Interchange Standards Consortium. Available from:
http://www.cdisc.org/about/index.html
CDISC. Clinical Data Acquisition Standards Harmonization (CDASH). 2008; Available from:
http://www.cdisc.org/standards/cdash/index.html
Fegan, G.W. and T.A. Lang, Could an open-source clinical trial data-management system be what
we have all been looking for? PLoS Med, 2008. 5(3): p. e6
Mats Lörstad, Data quality of the clinical trial process - costly regulatory compliance at the
expense of scientific proficiency. The Quality Assurance Journal, 2004. 8(3): p. 177-182
Knatterud, G.L., Guidelines for Quality Assurance in Multicenter Trials A Position Paper. Controlled
clinical trials, 1998. 19(5): p. 477
Baigent, C., Ensuring trial validity by data quality assurance and diversification of monitoring
methods. Clinical Trials, 2008. 5(1): p. 49
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An Introduction to CDISC - presentation