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Introduction Communication in Clinical Trials
SDTM transformation with SAS®
Conclusion and Outlook
Dia
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Introduction Communication in Clinical Trials
SDTM transformation with SAS®
Conclusion and Outlook
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Introduction
Communication in clinical trials
Clinical data
management: A
big amount of
data has to be
transferred in
clinical study
environment
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Introduction
Communication in clinical trials
Current state: Transfer of
clinical data in a clinical
study
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CDISC
Overview
• Clinical Data Interchange Standards Consortium
• Founded in 1997 in Austin Texas
• global, multidisciplinary, non profit organization
• About 200 corpoarte sponsors
• Main goal: describing a standard for data
communication and data management in clinical
trials
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Introduction
StandardsBSI British Standard : “standards
help to make life simpler and to
increase the reliability and the
effectiveness […]”
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Introduction
Standards
• Some advantages of standards:
– Increased speed and accuracy
– Faster trial set-up
– Fewer errors in completion, editing & coding
– Reduced rework and training
– Increased efficiency and cost effectiveness
– Standard SAS ® programs
• Disadvantages:
– Implementation
– Touching a running system
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CDISC models
SDTMDemographics DM
Comments CO
Concomitant Medications CM
Exposure EX
Substance Use SU
Adverse Events AE
Disposition DS
Medical History MH
Protocol Deviations DV
Drug Accountability DA
ECG Tests EGInclusion/ Exclusion
Exceptions IE
Laboratory Tests LB
Microbiology Specimens MB
Questionnaires QS
Microbiology Susceptibility MS
Physical Examinations PEPharmacokinetics
Concentrations PC
Subject Characteristics SC
Pharmacokinetics Parameters PP
Vital Signs VS
Trial Elements TE
Trial Arms TA
Trial Visits TV
Subject Elements SE
SDTM
- Study Data Tabulation Model
- model is accredited to be submitted to
FDA
- model for representing clinical study data
- 25 domains
- interventions, events and findings
- bases on the Implementation Guide (see
next pages)
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CDISC models
SDTM –Implementation Guide
SDTM - IG:
- rules
- assumptions
- implementation examples
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SDTM transformation with SAS®
Overview
Transformation Tool vs SAS
- SAS ist in vielen Betrieben schon vorhanden und muss nicht
angeschafft werden.
- ‘A transformation software safes only 15% of time, most of
manpower is needed to match the variables and domains’
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SDTM transformation with SAS®
OverviewThe transformation process must arrange the following points to create SDTM format:
• Renaming of variable names
• Reorganization of variables to SDTM domains
• Dataset names must be matched to SDTM standards
• The descriptive labels of SAS® datasets must be modified to conform to SDTM
standards
• Each variable within a SAS® dataset has a unique name
• Each variable has an associated label that describes the variable in more detail
• A variable’s type are SDTM Conform (numeric or character)
• A character variable can vary in length from 1 to 200 characters
• Combine values of multiple source variables into one destination variable
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SDTM transformation with SAS®
Domain Dictionary
• Contains
information about
used domains
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SDTM transformation with SAS®
Validation process - Proc CDISC
Proc CDISC validation: ‘content checking against the domain
provided by the SDTM’
• supports 15 of 23 standard domains
• verifies that all required variables are present in the data set
• reports all variables in the data set that are not defined in the
domain
• reports warnings for all expected domain variables that are not
in the data set
• notes all permitted domain variables that are not in the data set
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SDTM transformation with SAS®
Validation process - Proc CDISC
The result of Proc CDISC:’
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Conclusion and outlook
Conclusion
• SAS kann ein Transformationstool einfach ersetzen
• Die Transformationsarbeit ist nicht zu umgehen
• “Currently, the FDA does not make a clear statement regarding an introduction of the
CDISC standards”
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Conclusion and outlook
Literature
• http://www.lexjansen.com
• I:\bio\projects\LR-QB_Biostatistics\CDISC
• Thomas Wandt (2008): A CDISC Strategy for Roche Diagnostics Biostatistics -
Alternatives and Implementation Case Study
• Müller, Nadja (2007): Diploma Thesis – Reorganization and Conversation of Clinical
Trial Data of diverse Formats into CDISC compliant Data Repository Reorganization
and Conversion of Clinical Trial Data of diverse Formats into a CDISC compliant Data
Repository
• Questions ?
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