1. NCICB Jamboree February 25, 2005 NCICB Clinical Trials Modeling Activities

2. Part 1 - CDISC Modeling Activities Part 2 - Clinical Trials Modeling (caBIO extension)

3. CDISC Modeling Effort - Background  Clinical Data Interchange Standards Consortium (CDISC) is a non-profit organization committed to development of industry standards to support electronic exchange of clinical trials data and metadata.  HL7 provides standards for data exchange to allow interoperability between healthcare information systems. The Clinical Trials technical committee of HL7 (RCRIM) has it’s own models for clinical trials  To achieve true semantic interoperability between these 2 major standards in Clinical Trials domain – CDISC and HL7 have partnered to develop ONE common representation of the Clinical Trials domain across the industry

4. Shared Objectives – CDISC, HL7 & FDA  To improve the quality of public health  To have ONE overarching standard model for data interchange for healthcare information and clinical trial/clinical research data  Ensure interoperability among the various CDISC standards  Develop interoperability between CDISC standards and the HL7 Reference Information Model (RIM)

5. CDISC – Domain Analysis Model  Being developed as a UML class diagram and documented in Rational Rose XDE  Main subject areas (packages) are as follows: – Entities and Roles – Interventions and Observations – Structured Protocol – Meta-data (ODM) – Statistics  Modeling team represents various stakeholders: – Pharmaceutical companies – Federal Agencies (FDA, NCICB) – Cancer Centers and Academic Institutions (caBIG CTMS developers) – CDISC and HL7  Modeling effort lead by Dr. Charlie Mead

6. CDSIC Domain Analysis Model – Entities and Roles

7. CDISC Domain Analysis Model – Structured Protocol Package (draft)

8. CDISC Model – Next Steps  Once the model is complete, it will be mapped to the Clinical Trials DMIM and essentially to the HL7 Reference Information Model (RIM)  Some of the subject areas of the CDISC Model are further ahead than others: – Meta-data (ODM) to RIM mapping is already in progress – Structured Protocol Representation is very actively modeling at present and have begun early mapping to RIM.  Both these areas hope to work towards developing HL7 v3 message specifications in late 2005 - 2006

9. HL7 Development Framework (HDF) – Model Flow

10. Process Flow - Requirements to Message specification Requirements ( CDISC, FDA, NCICB, Pharma, vendors, etc.) Domain Analysis Model (UML class diagram) HL7 Clinical Trials DMIM RMIM & Message Types will be mapped to the DMIM/RIM Req. will be Modeled into the DAM specifications will be derived From DMIM DAM = Domain Analysis Model DMIM= Domain Message Information Model RMIM=Refined Message Information Model

11.  NCICB Internal Clinical Trials Modeling Effort Part 2

12.  Design Clinical Trials related classes as an extension to caBIO model  Focus on Results and Outcomes reporting requirements. Use the areas identified during Outcomes KA analysis  Identify and develop Use Cases to support this effort and scope the model in this Phase  Focus on Intervention Trials ONLY for the first iteration of the model  The data source for these objects would be C3D, therefore limit the scope of the model based on what is available in Oracle Clinical  Since C3D is based on CDEs, this Clinical Trials UML model will be CDE based  The Clinical Trials UML classes/objects developed here MAY serve as Clinical Trials API’s to C3D (Oracle Clinical) Clinical Trials Modeling - Objectives

13. Clinical Trials Modeling – Objectives (cont’d.)  The CT APIs will eventually be used to retrieve data and build Decision Support Applications  Ensure integration with other NCICB Clinical Trials related UML models (classes) – thru interfaces – caBIO, SPORES, etc. Long Term Objectives  The model should be reflective of the CDISC Domain Analysis Model in future phases  The model should also be reflective of the HL7 CT DMIM – ensure placeholders and structure to be able to receive data from HL7 v3 messages (some of this will happen naturally since the CDISC DSAM is being adopted by HL7 as the Domain model for HL7 Clinical Trials committee and will be mapped to the HL7 CT DMIM)

14. Data Areas Identified by Outcomes Requirements  Response  Biomarkers Modulation  Survival  Disease Recurrence  Adverse Events  Quality of Life  Baseline Data

15. Clinical Trials Modeling – Sample Use Cases  RESPONSE – Do men with early stage prostate cancer suffer complications after treatment with Proscar has ended? ( Show patient data where Disease Stage Numeric = I and where the Adverse Event Onset Date is after the Treatment End Date )  DISEASE RECURRENCE – What is the average time to disease progression (in days) for all patients on breast cancer trials? What is the most common reason for progression?  SURVIVAL – Is a high BMI associated with lower survival among breast cancer patients? Is there a correlation between BMI and disease stage? Metadata Loader – Implementation of HMD meta loader

16. Example Use Case Documentation Patient Survival Status : 3ALIVE DISEASE STATUS UNKNOWN1ALIVE WITH DISEASE2ALIVE WITH NO EVIDENCE OF DISEASE5DIED4 UNKNOWN Disease Stage Numeri c (limited set): IStage IIAStage IAIBStage IBIIStage IIIIIStage IIIIVStage IVIIAStage IIAIVAStage IVAIVBStage IVBIIBStage IIBIIIAStage IIIAIIIBStage IIIBIIIC Stage IIIC Prima ry Site of Disea se Nam e (limit ed set): Name/Quer y: Is a high BMI associated with lower survival among breast cancer patients? Is there a correlation between BMI and Disease Stage? Source:Center for Cancer Research KA Report Detail:The user is able to view survival status of breast cancer patients and determine if a high BMI is associated with a lower survival status (1 or 5). Background:BMI is calculated from a patient’s height and weight. Survival Status is based on caDSR DEs. Purpose:This will provide the survival rate and disease stage values for breast cancer patients with various BMI measurements. Potential CDEs used for this Query: The user selects to generate a report of Survival status for breast cancer patients. The system responds by requesting the user to select a set of breast cancer trials. Patient Identifier Protocol Number Patient Survival Status Patient Body Mass Index Measurement (can also be calculated by the system using height and weight CDEs) Diagnosis Date Disease Stage Numeric Primary Site of Disease Name Valid Values/ Meanings Associated with Potential CDEs: Patient Survival Status : 3 ALIVE DISEASE STATUS UNKNOWN1ALIVE WITH DISEASE2ALIVE WITH NO EVIDENCE OF DISEASE5DIED4UNKNOWN Use Case #3 – Survival – Across Trials

17. Key Features of the Clinical Trials Model  Completely based on CDEs – CDEs from CCR Context with CS/CSI of C3D – all the class attributes are existing CDEs – only CDEs with a workflow status of “Released” have been used – actual CDE Long Name captured in the EA as Alias – experimenting with adding a Tag in EA for capturing the PublicID – currently using 210 CDEs from a total set of 576 from CCR context  Although currently focused on C3D, the model is a generic Clinical Trials model to support other data sources in future iterations

18. Clinical Trials UML Class Diagram (work-in-progress)

19. Clinical Trials UML Diagram

20. Example – CDE Long Name as Alias CDE Long Name

21. Status and Next Steps  The CT model is scheduled to be complete in first week on April  Would like to begin meeting with other NCICB development teams by mid-March for model reviews  Will incorporate as much as possible of the CDISC Domain Analysis Model into this iteration  This is just the first Phase of the effort – additional requirements will be accommodated in future iterations  Implementation of this model will be done with other NCICB developers/members

22. Acknowledgements Sue Dubman Christo Andonyadis Dianne Reeves Peter Covitz Bill McCurry Smita Hastak Becky Riggle Val Bragg Charles Yaghmour Jane Harrell