Transcription of Common Misconceptions when Implementing SDTM
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Common Misconceptions when Implementing SDTM
Common Misconceptions when Implementing SDTMJ erry SalyersSenior Consultant data Standards ConsultingFred WoodSenior Manager and LeadData Standards Consulting Accenture Accelerated R&D ServicesBoston PhUSESDEA pril 27 2017 Confidential and ProprietaryConfidential and ProprietaryAgenda Introduction data Flow Compliance Extra data Supplemental Qualifiers and Findings About Timing Variables Exposure data Trial Design 2017 Accenture All Rights and ProprietaryConfidential and ProprietaryIntroduction In theory, there is no difference between theory and practice. In practice, there Berra 2017 Accenture All Rights and ProprietaryConfidential and ProprietaryIn Theory: Companies Should Have and Enforce data Standards The Reality: Although waning, some believe: data standards stifle creativity The CRF is a vehicle for expressing creativity Many companies outsource legacy- data conversion to multiple CROs/vendors Each has its own way of interpreting the SDTM/SDTMIG data that has various degrees of compliance data may not be able to be integrated without additional time and effort Many companies do not have standard data -transfer specifications 2017 Accenture All Rights and ProprietaryConfidential and ProprietaryIn Theory: Every Variable Should Have an Unambiguous MeaningThe Reality While many variables do, s
(Study Data Tabulation Model) ADaM (Analysis Data Model) SDTM and ADaM Lab Data Lab Model Legacy Data Converted Data. Confidential and Proprietary Agenda • Introduction • Data Flow • Compliance • Extra Data • Supplemental Qualifiers and Findings About • Timing Variables
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