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SDTM Validation

SDTM Validation. Delaware Valley CDISC user network Ketan Durve Johnson and Johnson Pharmaceutical Reasearch and Development May 11 th 2009. Context and Scope. Safety and study population SDTM data and metadata for 2 FDA submissions, mapped from legacy sponsor standards

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SDTM Validation

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  1. SDTM Validation Delaware Valley CDISC user network Ketan Durve Johnson and Johnson Pharmaceutical Reasearch and Development May 11th 2009

  2. Context and Scope • Safety and study population SDTM data and metadata for 2 FDA submissions, mapped from legacy sponsor standards • Tweaked for acceptance checks in data transfers from data management to programming Johnson and Johnson Pharmaceutical Research and Development

  3. Tools and Processes • Programmatic and non-programmatic checks • Programmatic data and metadata checks executed using SAS • FDA / WebSDM based checks as well as sponsor defined checks • Data in SAS v8 datasets and metadata in an oracle database metadata repository • Custom IG with default Origin, Length, Comment / Computational Method based on sponsor standards Johnson and Johnson Pharmaceutical Research and Development

  4. Metadata Repository • Tables and relationships for datasets, variables, codelists and valuelists • Study metadata as well as a customized IG • Metadata source for Define.Xml • SAS Views provide access to metadata for SDTM validation • Minimal checks when loading metadata as SAS datasets or Excel spreadsheets • Provides attribute file for checking and creation of permanent SDTM SAS datasets Johnson and Johnson Pharmaceutical Research and Development

  5. Study Metadata Vs IG Metadata • Variables match those in IG or are acceptable optional variables • Label, role, core, order and length match IG • Data Type, significant digits, origin, comment and computational method match IG or are appropriate study specific modifications • Account for extensions of the general observation classes Johnson and Johnson Pharmaceutical Research and Development

  6. Metadata Checks • Consistency across dataset, variable, value level and codelist metadata • If variable has value level metadata or controlled terminology specified, then appropriate codelists and valuelists are defined in study metadata • Existence of value level metadata for –TESTCD and QNAM • Codelists defined with beginning and ending values or dictionary and version Johnson and Johnson Pharmaceutical Research and Development

  7. Data Checks • Amended version of WebSDM IR4100 - IR4140 within domain data validity checks, with additional variables included and updates for • --STRF/--ENRF or --STDTC/--ENTC null when --OCCUR is N • --ORRES and --STAT both null when --DRVFL is Y • Null values in --ORRESU for titers and sample status • Null values for --STDTC when --ENDTC is valued for prior onset records • Cross domain data checks for presence of records for subjects in DM, EX and DS if found in any other domain who are not screen failures Johnson and Johnson Pharmaceutical Research and Development

  8. Data Vs Metadata Checks • No missing values for required variables • Check data type and origin are valid values • Significant digits populated when data type is float and reflect precision • SAS dataset and variable attributes for label, type and length as well as variable order match metadata • All variables in Metadata exist in dataset and no extraneous variables exist in dataset • If controlled terminology associated with variable, then values match valid values per codelists and valuelists Johnson and Johnson Pharmaceutical Research and Development

  9. Questions? Johnson and Johnson Pharmaceutical Research and Development

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