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Gowthami.Saripalli
Executive-CDM
Executive CDM
Paradigm IT
What is CDISC?

  CDISC is a global, open, multidisciplinary, non-profit organization
                                              non profit
  that has established standards to support the acquisition,
  exchange, submission and archive of clinical research data and
  metadata.


It is for :-
    “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.




                                                                        2
CDISC
       Founded in 1997; incorporated in 2000
       Nearly 200 member organizations
•   Biopharmaceutical companies
•   Academic Research Institutes
•   Technology Vendors etc
                Vendors, etc…
       Active Coordinating Committees
•   Europe
•   Japan
       Additional activities
•   Australia
•   India
•   S. America and Africa


                                               3
CDISC
• CDISC standards catalyze information flow through the entire
  pre-clinical and clinical research process, from study protocol
  and various sources of data collection to analysis and reporting
  through regulatory submission and electronic data archive.




                                                                     4
CDISC
• Standard for Exchange of Nonclinical Data(SEND):
   – The SENDIG is intended to guide the organization, structure,
      and format of standard nonclinical tabulation datasets for
      interchange between organizations such as sponsors and
      CROs and for submission to the US Food and Drug
      Administration (FDA)
                       (FDA).
• Protocol Representation Model (PRM):
   – The content and format standard supporting the interchange
      of clinical t i l protocol i f
        f li i l trial     t   l information. Thi i a collaborative
                                        ti    This is     ll b ti
      effort with Health Level Seven (HL7).
• Trial Design Model (TDM):
   – The content standard that defines the structure for
      representing the planned sequence of events and the
      treatment plan of a trial. This is a subset of the SDTM and
                  p
      Protocol Representation.

                                                                      5
CDISC
• Operational Data Model (ODM):
   – The XML-based content and format standard for the
             XML based
      acquisition, exchange, reporting or submission, and archival
      of case report form (CRF)-based clinical research data.
• Clinical Data Acquisition Standards Harmonization (CDASH):
   – A CDISC-led collaborative initiative to develop the content
      standard for basic data collection fields in case report forms.
      This standard is based upon the SDTM
                                        SDTM.
• Laboratory Data Model (LAB):
   – The content and format standard for data transfer between
      clinical laboratories and study sponsors /CROs.




                                                                       6
CDISC
• Study Data Tabulation Model (SDTM):
   – The content standard for regulatory submission of case report
     form data tabulations from clinical research studies.
• Analysis Data Model (ADaM):
   – The content standard for regulatory submission of analysis
     datasets and associated files.
• Case Report Tabulation Data Definition Specification (CRTDDS)
  (define.xml):
  (d fi      l)
   – The XML-based content and format standard referenced by
     the FDA as the specification for the data Definition for CDISC
     SDTM datasets. This standard, also known as define.xml,is
     an extension of the ODM.



                                                                     7
Global CDISC Integration




                           8
Clinical Data Acquisition Standard Harmonization

• To develop a set of ‘content standards’ (element name,
  definition,
  definition metadata) for a basic set of global industry
  wide data collection fields that support clinical research
• The initial scope - ‘safety data/domains’
• These safety domains cut across all therapeutic areas
• (TA independent)
Why CDASH?
• Most Clinical trials…
  – D ’t employ a standard f d t capturing
    Don’t   l      t d d for data   t i


• Result
  Result…
  – Analyzing of clinical trial data efficiently and
    systematically is difficult and time consuming.
     y           y                                   g
  – Especially for multicentre trials
     • Ex: How many women participate in trial?




                                                         10
Study 2 -Demog

                ID        GENDER

                A1        FEMALE

 Study 1-Demo   A2        MALE          Study 3-Dmog

SUBJID   SEX    A3        FEMALE       USUBJID GENDER

0001     M      A4        MALE         00011   0

0002     F                             00012   1
                Study 4-Demographics
0003     F                             00013   0
                 PID       SEX
0004     M                             00014   1
                0R1       2

                0R1       1

                0R3       2

                0R4       2

                                                        11
Difficulties in understanding the data…
• No standard file names
• N standard variable names
  No t d d        i bl
• No standard terminology
   – Which code resembles which sex?
Result:-
Analysis of data is
          f
         Difficult


                      Time Consuming

                                       Expensive


                                                   12
Benefits of CDASH
• The main benefit is standardizing the definitions
  for the data that is collected over multiple
  studies.
• CDASH defines data that can be used in the
  cleaning of data and for the conformation of
  missing data.
          g
• CDASH is valuable for reducing the production
  time for CRF design, reducing the training time
                    g          g            g
  for sites.



                                                      13
CDASH benefits
• Eliminates some of the variety in CRFs seen at
  sites

• S
  Streamlines
        li       training
                    i i     &   increases
                                i           common
  understanding of CRF completion instructions

• Reinforces collecting only key data

• Reduces collection of duplicate data

• decreasing the potential for error
                                                     14
Standard Domains
•   Common Identifier Variables    •   ECG (EG)
•   Common Timing Variables        •   Exposure (EX)
•   Adverse Events (AE)            •   Inclusion Exclusion (IE)
•   Concomitant Medications (CM)   •   LAB Test Results (LB)
•   Comments (CO)                  •   Medical History (MH)
•   Drug Accountability (DA)       •   Physical Exam (PE)
•   Demographics (
          g p      (DM))           •   Vital Signs (VS)
                                               g ( )
•   Disposition (DS)               •   Subject Characteristics (SC)
•   Protocol Deviations (DV)       •   Substance Use (SU)




                                                                      15
Standards variables
Highly Recommended:
• A data collection field that should be on the CRF (e.g., a
  regulatory requirement, if applicable)
• (e.g. Adverse Event Term)
Recommended/Conditional:
• A data collection field that should be collected on the CRF for
  specific cases
• (may be recorded elsewhere in the CRF or from other data
  collection sources)
• (e.g. AE Start Time)
   ( g                  )
Optional:
• A data collection field that is available for use if needed
• (
  (e.g. W any AE experienced?)
        Was                 i      d?)

                                                                    16
Expectations
• Highly Recommended data collection variables
  should always be present on the CRF

• S
  Sponsors will need t add data collection
              ill   d to dd d t       ll ti
  fields as needed to meet protocol-specific
  and other data collection requirements
  (e.g. therapeutic area specific data variables
  and others as required per protocol, business
  practice and operating procedures)


                                                   17
To Implement
Existing Standards?
Gap Analysis – do not forget terminology!
Negotiation
        1. Internal stakeholders
        2. External stakeholders
• Training
• Establish Relationship with other standards
                         p
• Follow the guidelines




                                                18
References
http://www.cdisc.org/standards/cdash/downloads/
  CDASH_STD-1_0_2008-10-02.pdf
  CDASH STD 1 0 2008 10 02 pdf

http://www.cdisc.org/models/sdtm/v1.1/index.htm
htt //      di      / d l / dt / 1 1/i d ht




                                                  19
20

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CDISC-CDASH

  • 2. What is CDISC? CDISC is a global, open, multidisciplinary, non-profit organization non profit that has established standards to support the acquisition, exchange, submission and archive of clinical research data and metadata. It is for :- “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. 2
  • 3. CDISC Founded in 1997; incorporated in 2000 Nearly 200 member organizations • Biopharmaceutical companies • Academic Research Institutes • Technology Vendors etc Vendors, etc… Active Coordinating Committees • Europe • Japan Additional activities • Australia • India • S. America and Africa 3
  • 4. CDISC • CDISC standards catalyze information flow through the entire pre-clinical and clinical research process, from study protocol and various sources of data collection to analysis and reporting through regulatory submission and electronic data archive. 4
  • 5. CDISC • Standard for Exchange of Nonclinical Data(SEND): – The SENDIG is intended to guide the organization, structure, and format of standard nonclinical tabulation datasets for interchange between organizations such as sponsors and CROs and for submission to the US Food and Drug Administration (FDA) (FDA). • Protocol Representation Model (PRM): – The content and format standard supporting the interchange of clinical t i l protocol i f f li i l trial t l information. Thi i a collaborative ti This is ll b ti effort with Health Level Seven (HL7). • Trial Design Model (TDM): – The content standard that defines the structure for representing the planned sequence of events and the treatment plan of a trial. This is a subset of the SDTM and p Protocol Representation. 5
  • 6. CDISC • Operational Data Model (ODM): – The XML-based content and format standard for the XML based acquisition, exchange, reporting or submission, and archival of case report form (CRF)-based clinical research data. • Clinical Data Acquisition Standards Harmonization (CDASH): – A CDISC-led collaborative initiative to develop the content standard for basic data collection fields in case report forms. This standard is based upon the SDTM SDTM. • Laboratory Data Model (LAB): – The content and format standard for data transfer between clinical laboratories and study sponsors /CROs. 6
  • 7. CDISC • Study Data Tabulation Model (SDTM): – The content standard for regulatory submission of case report form data tabulations from clinical research studies. • Analysis Data Model (ADaM): – The content standard for regulatory submission of analysis datasets and associated files. • Case Report Tabulation Data Definition Specification (CRTDDS) (define.xml): (d fi l) – The XML-based content and format standard referenced by the FDA as the specification for the data Definition for CDISC SDTM datasets. This standard, also known as define.xml,is an extension of the ODM. 7
  • 9. Clinical Data Acquisition Standard Harmonization • To develop a set of ‘content standards’ (element name, definition, definition metadata) for a basic set of global industry wide data collection fields that support clinical research • The initial scope - ‘safety data/domains’ • These safety domains cut across all therapeutic areas • (TA independent)
  • 10. Why CDASH? • Most Clinical trials… – D ’t employ a standard f d t capturing Don’t l t d d for data t i • Result Result… – Analyzing of clinical trial data efficiently and systematically is difficult and time consuming. y y g – Especially for multicentre trials • Ex: How many women participate in trial? 10
  • 11. Study 2 -Demog ID GENDER A1 FEMALE Study 1-Demo A2 MALE Study 3-Dmog SUBJID SEX A3 FEMALE USUBJID GENDER 0001 M A4 MALE 00011 0 0002 F 00012 1 Study 4-Demographics 0003 F 00013 0 PID SEX 0004 M 00014 1 0R1 2 0R1 1 0R3 2 0R4 2 11
  • 12. Difficulties in understanding the data… • No standard file names • N standard variable names No t d d i bl • No standard terminology – Which code resembles which sex? Result:- Analysis of data is f Difficult Time Consuming Expensive 12
  • 13. Benefits of CDASH • The main benefit is standardizing the definitions for the data that is collected over multiple studies. • CDASH defines data that can be used in the cleaning of data and for the conformation of missing data. g • CDASH is valuable for reducing the production time for CRF design, reducing the training time g g g for sites. 13
  • 14. CDASH benefits • Eliminates some of the variety in CRFs seen at sites • S Streamlines li training i i & increases i common understanding of CRF completion instructions • Reinforces collecting only key data • Reduces collection of duplicate data • decreasing the potential for error 14
  • 15. Standard Domains • Common Identifier Variables • ECG (EG) • Common Timing Variables • Exposure (EX) • Adverse Events (AE) • Inclusion Exclusion (IE) • Concomitant Medications (CM) • LAB Test Results (LB) • Comments (CO) • Medical History (MH) • Drug Accountability (DA) • Physical Exam (PE) • Demographics ( g p (DM)) • Vital Signs (VS) g ( ) • Disposition (DS) • Subject Characteristics (SC) • Protocol Deviations (DV) • Substance Use (SU) 15
  • 16. Standards variables Highly Recommended: • A data collection field that should be on the CRF (e.g., a regulatory requirement, if applicable) • (e.g. Adverse Event Term) Recommended/Conditional: • A data collection field that should be collected on the CRF for specific cases • (may be recorded elsewhere in the CRF or from other data collection sources) • (e.g. AE Start Time) ( g ) Optional: • A data collection field that is available for use if needed • ( (e.g. W any AE experienced?) Was i d?) 16
  • 17. Expectations • Highly Recommended data collection variables should always be present on the CRF • S Sponsors will need t add data collection ill d to dd d t ll ti fields as needed to meet protocol-specific and other data collection requirements (e.g. therapeutic area specific data variables and others as required per protocol, business practice and operating procedures) 17
  • 18. To Implement Existing Standards? Gap Analysis – do not forget terminology! Negotiation 1. Internal stakeholders 2. External stakeholders • Training • Establish Relationship with other standards p • Follow the guidelines 18
  • 19. References http://www.cdisc.org/standards/cdash/downloads/ CDASH_STD-1_0_2008-10-02.pdf CDASH STD 1 0 2008 10 02 pdf http://www.cdisc.org/models/sdtm/v1.1/index.htm htt // di / d l / dt / 1 1/i d ht 19
  • 20. 20