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CDISC and how Stata can help us implement it

CDISC and how Stata can help us implement it. Dr Adam Jacobs Dianthus Medical Limited. CDISC. What is CDISC? Overview of the standards Brief look at some data standards How can Stata help us work with the standards?. What is CDISC?.

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CDISC and how Stata can help us implement it

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  1. CDISC and how Stata can help us implement it Dr Adam Jacobs Dianthus Medical Limited

  2. CDISC • What is CDISC? • Overview of the standards • Brief look at some data standards • How can Stata help us work with the standards?

  3. What is CDISC? CDISC is a global, open, multidisciplinary, non-profit organization that has established standards to support the acquisition, exchange, submission and archive of clinical research data and metadata. The CDISC mission is to develop and support global, platform-independent data standards that enable information system interoperability to improve medical research and related areas of healthcare. CDISC standards are vendor-neutral, platform-independent and freely available via the CDISC website. Source: www.cdisc.org

  4. Protocol CRF Operational database Results Analysis datasets Tabulation datasets Overview of CDISC standards PRM CDASH ODM ADaM SDTM

  5. Protocol CRF Operational database Results Analysis datasets Tabulation datasets Overview of CDISC standards Terminology PRM CDASH ODM ADaM SDTM

  6. ODM The Operational Data Model (ODM) is a vendor neutral, platform independent format for interchange and archive of data collected in clinical trials. The model represents study metadata, data and administrative data associated with a clinical trial. Source: www.cdisc.org

  7. What can we do in ODM? • Primarily for interchange and archiving of data • Basic information about the study • Can define CRF design (eCRF or paper) • Both data and metadata • Show clinical data: • all data • partial data • changes to data

  8. <SubjectData SubjectKey="SS_1002"> <StudyEventData StudyEventOID="SE_SCREENING_9605"> <FormData FormOID="F_HEMATOLO_1"> <ItemGroupData ItemGroupOID="IG_HEMATOLOGY" ItemGroupRepeatKey="1"> <ItemData ItemOID="I_HEM_LBTEST" Value="WBC"/> <ItemData ItemOID="I_HEM_LBORRES" Value="5.8"/> </ItemGroupData> </FormData> </StudyEventData> </SubjectData>

  9. SDTM • Study data tabulation model • Specifies format of datasets for raw data • Designed for storage of data • Flexible model to accommodate many kinds of trial data • Some basic information on study design

  10. SDTM domains • SDTM data divided into domains (datasets) • 2 letter abbreviations, eg: • DM: demographics • VS: vital signs • EX: exposure • AE: adverse events • TA: trial arms

  11. SDTM domain classes • 6 classes of domains: • Interventions (eg EX) • Findings (eg AE) • Observations (eg VS) • Special purpose (DM and CO) • Trial design (eg TA) • Relationships (eg SUPPDM)

  12. SDTM example STUDYID DOMAIN USUBJID SUBJID RFSTDTC RFENDTC AGE SEX RACE xxx DM xxx-101 101 2009-02-05 2009-03-25 42 F Caucasian xxx DM xxx-102 102 2009-02-05 2009-03-25 24 M Caucasian xxx DM xxx-103 103 2009-02-04 2009-03-10 33 F Caucasian xxx DM xxx-104 104 2009-02-04 2009-03-25 29 F Caucasian xxx DM xxx-105 105 2009-02-04 2009-03-25 28 M Caucasian

  13. Another SDTM example VSTESTCD VSTEST VSCAT VSPOS VSORRES VSORRESU VSSTRESC VSSTRESN BMI BMI Height and weight 23.2 kg/m^2 23.2 23.2 HEIGHT Height Height and weight 162 cm 162 162 WEIGHT Weight Height and weight 61 kg 61 61 DIABP DBP BP and heart rate STANDING 77 mm Hg 77 77 HR Heart Rate BP and heart rate STANDING . bpm . SYSBP SBP BP and heart rate STANDING 116 mm Hg 116 116 DIABP DBP BP and heart rate SUPINE 61 mm Hg 61 61 HR Heart Rate BP and heart rate SUPINE 56 bpm 56 56 SYSBP SBP BP and heart rate SUPINE 111 mm Hg 111 111

  14. ADaM • Analysis dataset model • Designed for analysis of data • Many variables are copied from SDTM, others are derived • Should be just one step away from producing results • Often many variables in the dataset • Derived variables must be easily traceable

  15. Workflow for clinical study XML db export (ODM) Pseudo-SDTM SDTM Results Analysis datasets

  16. Questions

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