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CDISC 360: Evolving our standards towards end to end automation

Explore the CDISC 360 project, which aims to add a conceptual layer to standards and enable end-to-end automation in data processing. Learn about the expected outcomes and the relationship to other initiatives.

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CDISC 360: Evolving our standards towards end to end automation

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  1. CDISC 360:Evolving our standards towards end to end automation Peter van Reusel Sam HumeBarry Cohen

  2. Agenda • Where are we today • What is CDISC 360 • Project Approach • Relationship to Other Initiatives • Expected outcomes

  3. 1. Where are we today

  4. Today we are here TAUGS

  5. Defined structures • CDISC Foundational models provide much needed structure • Normative Content • 2 dimensional (tables, columns) • Standard to represent data • The information itself is not defined • We do not need new structures • We need to define • Entities • Semantics (meaning) • Relationships between information • Rules in the data lifecycle

  6. Why Change?Industry needs are maturing • Machine-readable standards • Move beyond normative structural description of data • Provide semantic relations between data – add meaning • Add process metadata to enable end-to-end automation • We want non-standard experts to use ourstandards

  7. 2. What is CDISC 360

  8. What is the CDISC 360 Project?Adding a conceptual layer to standards • Create and store standards as concepts which create meaning between data • A serious attempt to store and use data standards as linked metadata • Add computer readable process metadata which enables end to end automation • Evolve from normative to informative standards • CDISC 360 will develop concept-based standard definitions, and test and demonstrate end-to-end automation of study specification and data processing  Test and demonstrate, but not building software

  9. Biomedical Concept • Data is expressed through the CDISC Foundational Models • Canbemappedto BRIDG Reference Model

  10. Biomedical Concept Triple Store

  11. Biomedical Concept • Attributes are linked to the element

  12. Biomedical Concept c • Linking controlled terminology to the variable

  13. Biomedical Concept • Standardize value level metadata

  14. Biomedical Concept 40 • Machine readable definition of validation rules

  15. Analysis Result

  16. Analysis Concept

  17. One Model • Biomedical Concept Map • Analysis Concept Map  The Biomedical Concept and Analysis Concept are ONE MODEL

  18. The Power of a Conceptuel Model for Data Standards • Linking controlled terminology to the variable – standardize value level metadata • Machine readable definition of validation rules • Linking derivations and algorithms to variable(s) • Include process metadata (ETL instructions) • Possibility to standardize Analysis outputs and Collection instruments • Combining layout, variables, process information together • Link Analysis Concepts to Biomedical Concepts • Choose an analysis and automatically obtain all related end-to-end metadata  All of the above: enables automation, increase confidence in results, true analysis traceability

  19. Use Case 1 : End to Start Specification Selecting standards concepts and linked metadata needed for a study Data Standards Biomedical ConceptsAnalysis ConceptsFoundational Standards Endpoints TFL Data CollectionModules Collection Metadata Retrieve Collection Standards Tabulation Metadata Retrieve TabulationStandards AnalysisMetadata RetrieveAnalysisStandards TFL Protocol Outline (Hypothesis) Standards Metadata Selection StandardsSelection

  20. CDISC Library API extension LOAD API API Knowledge graph Biomedical ConceptsAnalysis ConceptsFoundational Standards Call ExtendCurrent Set of APIs Return API metadata Return PAA BBB X-3 Y+H DEF 1+2 … • Dataset metadata • Concept metadata • Concept relations • Controlledterminology • Process metadata • Configuration Defaults and options

  21. DISCLAIMER NOTE The following is not a software demonstration Sole purpose is to illustrate how data standards can enable tools

  22. Welcome Login: CDarwin Password: ************ SIGN IN >>

  23. Enter yoursearchhere Selection CDASH DOMAINCDASH VariableValue Level MetadataControlled Terminology SELECTION ANALYSIS SDTMDOMAINSDTM Variable Value Level MetadataControlled Terminology Computational algorithm AE LB VS MH AdverseEvents CM MedicalHistory Laboratory Test Results Vital Signs ADaM DOMAINADaM VariableADaM ParametersControlled Terminology Computational algorithm DATA COLLECTION EX ConcomittantMedication Exposure Figures Listings Tables End Points

  24. Enter yoursearchhere Selection CDASH DOMAINCDASH VariableValue Level MetadataControlled Terminology SELECTION ANALYSIS SDTMDOMAINSDTM Variable Value Level MetadataControlled Terminology Computational algorithm ADaM DOMAINADaM VariableADaM ParametersControlled Terminology Computational algorithm DATA COLLECTION Figures Cumulative distribution (with SEs) of time to first AE of special interest Graphical Approaches to the Analysis of Safety Data from Clinical Trials”. Amit, et. al. From “Graphical Approaches to the Analysis of Safety Data from Clinical Trials”. Amit, et. al. Mean Change from Baseline in QTc by time and treatment. Distribution of ASAT by time and treatment Distribution of maximum LFT values by treatment. Panel of LFT shift from baseline to maximum by treatment LFT Patient profiles Most Frequent On Therapy Adverse Events x

  25. SAMPLE TEMPLATE

  26. Enter yoursearchhere Selection CDASH DOMAINCDASH VariableValue Level MetadataControlled Terminology SELECTION ANALYSIS SDTMDOMAINSDTM Variable Value Level MetadataControlled Terminology Computational algorithm AE LB VS MH AdverseEvents CM MedicalHistory Laboratory Test Results Vital Signs ADaM DOMAINADaM VariableADaM ParametersControlled Terminology Computational algorithm DATA COLLECTION EX ConcomittantMedication Exposure Figures Listings End Points Tables

  27. Enter yoursearchhere Selection CDASH DOMAINCDASH VariableValue Level MetadataControlled Terminology SELECTION ANALYSIS SDTMDOMAINSDTM Variable Value Level MetadataControlled Terminology Computational algorithm ADaM DOMAINADaM VariableADaM ParametersControlled Terminology Computational algorithm DATA COLLECTION Listings Listing 4.5 Adverse Event Listing. Serious Treatment Emergent Adverse Events Related To Treatment Listing 2.4 Current Cancer History – All Treated Patients Experiencing Critical Events Listing 2.5 Prior and Concomitant Medication – All Treated Patients Experiencing Critical Events Listing 2.6 Physical Examination at Screening – All Treated Patients Experiencing Critical Events Listing 3.1 Reference Chemotherapy and Concomitant Chemotherapies – All Treated Patients Experiencing .. Listing 4.1 Adverse Event Listing. All Pre-Treatment Adverse Events – All Treated Patients Experiencing … Listing 4.2 Adverse Event Listing. Treatment Emergent Adverse Events – All Treated Patients Experiencing … Listing 4.3 Adverse Event Listing. Serious Treatment Emergent Adverse Events – All Treated Patients .. Listing 4.4 Adverse Event Listing. Serious Treatment Emergent Adverse Events Related To Study Drug … x

  28. SAMPLE TEMPLATE x SELECT Close

  29. Filter active: ADAE Domain Selection CDASH DOMAINCDASH VariableValue Level MetadataControlled Terminology SELECTION ANALYSIS ADaM Variables ComputationalAlgorithm Domain Dataset Description ADAE One record per subject per adverse event, per date x SDTMDOMAINSDTM Variable Value Level MetadataControlled Terminology Computational algorithm ADaM DOMAINADaM VariableADaM ParametersControlled Terminology Computational algorithm DATA COLLECTION SDTM Related metadata: CDASH Domain ComputationalAlgorithm Variables Domain Variables Tables Listings Figures DCM

  30. Filter active: ADAE Domain Filter active: ADAE Variables Selection CDASH DOMAINCDASH VariableValue Level MetadataControlled Terminology SELECTION ANALYSIS ADaM Variables ComputationalAlgorithm Domain SDTMDOMAINSDTM Variable Value Level MetadataControlled Terminology Computational algorithm Domain Name Label Computational Algorithm ADaM DOMAINADaM VariableADaM ParametersControlled Terminology Computational algorithm ADAE USUBJID Unique Subject Identifier DATA COLLECTION subject identifier for the study ADAE SUBJID Study Site identifier ADAE SITEID Study Drug Dose at AE Onset Units ADAE DOSEAONU ADAE.DOSEAEONU ADAE DOSEAEON ADAE.DOSEAEON Study Drug Dose at AE Onset ADAE COUNTRY Country Analysis Start Time ASTTM ADAE ADAE.ASTTM Analysis Start Time ASTDT ADAE ADAE.ASTDT SDTM Related metadata: CDASH Domain ComputationalAlgorithm Variables Domain Variables Reported Term for the Adverse Events ADAE AETERM x Tables Listings Figures DCM

  31. Filter active: ADEA Computation.. Filter active: ADAE Variables Selection CDASH DOMAINCDASH VariableValue Level MetadataControlled Terminology SELECTION ANALYSIS ADaM Domain Variables ComputationalAlgorithm SDTMDOMAINSDTM Variable Value Level MetadataControlled Terminology Computational algorithm Reference Description ADAE.DOSEAEON Equals to % SDTM_DATE_VARIABLE % transformedinto %DATE_NUMERIC_FORMAT% whenlength (%SDTM_DATE_VARIABLE%) > 9 ADaM DOMAINADaM VariableADaM ParametersControlled Terminology Computational algorithm ADAE.AENDT DATA COLLECTION Equals to ADAE.AENDT – ADAE.ASTDT + 1. ADAE.ADURN Equals to EX.EXDOSE where the numeric version of EX.EXSTDTC <= ASTDT <= the Numeric version of EX.EXENDTC. ADAE.DOSEAEON Equals to EX.EXDOSU where the numeric version of EX.EXSTDTC<= ASTDT <= the Numeric version of EX.EXENDTC. ADAE.DOSEAEONU ADAE.DOSEAEON Equals to ‘’DAYS’’ x SDTM Related metadata: CDASH Domain ComputationalAlgorithm Variables Domain Variables Tables Listings Figures DCM

  32. Filter active: DOSEAEON Selection CDASH DOMAINCDASH VariableValue Level MetadataControlled Terminology SELECTION ANALYSIS ADaM Domain Variables ComputationalAlgorithm SDTMDOMAINSDTM Variable Value Level MetadataControlled Terminology Computational algorithm Reference Description Equals to EX.EXDOSE where the numeric version of EX.EXSTDTC <= ASTDT <= the Numeric version of EX.EXENDTC. ADaM DOMAINADaM VariableADaM ParametersControlled Terminology Computational algorithm ADAE.DOSEAEON DATA COLLECTION x Domain Name Question Name Label Origin Role Core AESTDTC AE AESTDAT Start date/Time of Adverse Event Start Date CRF Timing Exp Start Time Derived Record Qualifier Exp EXDOSE AE Dose per administration AESTIM Dose CRF Timing Exp EXTDTC EX EXAMONT Start date/Time of treatment Units CRF Timing EXAMONTU End date/Time of treatment EXENDTC EX Perm x EX End Date EXENDAT SDTM Related metadata: CDASH Variables ComputationalAlgorithm Variables Domain Domain DCM End Time EXENTIM EX EX EXSTDAT Start Date x Tables Listings Figures

  33. Selection CDASH DOMAINCDASH VariableValue Level MetadataControlled Terminology SELECTION ANALYSIS ADaM Variables ComputationalAlgorithm Domain DCM’s SDTMDOMAINSDTM Variable Value Level MetadataControlled Terminology Computational algorithm ADaM DOMAINADaM VariableADaM ParametersControlled Terminology Computational algorithm DATA COLLECTION x SDTM Related metadata: CDASH Domain Domain ComputationalAlgorithm ComputationalAlgorithm Variables Variables Domain Domain Variables Variables << >> < < Tables Tables Listings Listings Figures Figures DCM DCM

  34. Use Case 1 : End to Start specification Selecting standards concepts and linked metadata needed for a study Data Standards Biomedical ConceptsAnalysis Concepts Foundational Standards Endpoints TFL Data CollectionModules Collection Metadata Retrieve Collection Standards Tabulation Metadata Retrieve TabulationStandards AnalysisMetadata RetrieveAnalysisStandards TFL Protocol Outline (Hypothesis) Standards Metadata Selection StandardsSelection

  35. Use Case 2 : Start to End Study Metadata Adding study design, concept configuration & generateartifacts Endpoints CDASH SDTM ADaM Generate Studyartifacts Clinical StudyReports Tabulation Datasets Analysis Datasets Create Operational Database Operational Database Create Tabulation Datasets Create ADaM Datasets Create Analysis Results structures & shells TFL Study Build and configuration Define Define Standards Metadata Selection Configured study metadata

  36. Study Build Study buildertool Createartifacts (use case 2) StandardsSelection SDM / XML Configuredstudymetadata • Study parameters (TS) • Eligibilitycriteria • Schedule of activities (SOA) • Study workflow • Study design • Visits • Arm’s • Epochs ….. Study Configuration Study workflow Schedule of Activities (SoA) Study Design Study Parameters (TS)

  37. Study Parameters (TS)

  38. Study Design Run-in Epoch First Treatment Epoch Second Treatment Epoch Follow Up Epoch Run-in A 5 mg A 10 mg B 5 mg B 10 mg Follow Up Arm AB Run-in B 5 mg B 10 mg A 5 mg A 10 mg Follow Up Arm BA

  39. Schedule of Activities (SoA)

  40. Use Case 3 : Start to End Data Processing Automatic population of data intoartifacts Endpoints CDASH SDTM ADaM ePRO EDC Clinical StudyReports EDC ExtractDatabase Tabulation Datasets Analysis Datasets Operational Database ADaM Creation Analysis ResultsCreation eHR eDT TFL ProcessStudy Data Configured study metadata

  41. Project Standards ScopeDiabetes TAUG • 1 or 2 statistical endpoints • 3 to 4 ADaM datasets • 7 to 8 SDTM datasets • 15 Data Collection Modules  Reason forthis scope: the Diabetes TAUG provides standardized artifacts from analysis outputsto data collection. Thisallowsthe project team to focus on innovationandnot on establishing a new data standard.

  42. Diabetes TAUG

  43. Biomedical Concept Map

  44. Analysis Results Shells

  45. Analysis Dataset Metadata

  46. Tabulation Metadata

  47. Collection Metadata

  48. 3. Project Approach

  49. Transform concepts in machine readable form WS 1 WS2 Loadintolibrary Extend API’s Create concepts in knowledge graphs API Biomedical ConceptsAnalysis ConceptsFoundational Standards WS3 WS 5 WS 6 WS 4 Add transformationsemantics Configure study specification and createartifacts(Use Case 2) Automatically process and transform data (Use Case 3) Identify and select standards specification (Use Case 1)

  50. Workstream 1 & 2 • Workstream 1 - End-to-end concept development • Design concept maps • Semantic end-to-end expression of concepts • Final analysis output to data collection instruments • Includes transformation information • Combine Biomedical Concepts (BC) with Analysis Concepts (AC) • Workstream 2 - Machine-readable End-to-end concept development • Transform concepts in machine readable form • Load in to CDISC Library • Extend API’s to extract multifunctional metadata

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