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Leveraging Oracle's Life Sciences Data Hub to Enable Dynamic Cross-Study Analysis
 

Leveraging Oracle's Life Sciences Data Hub to Enable Dynamic Cross-Study Analysis

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    Leveraging Oracle's Life Sciences Data Hub to Enable Dynamic Cross-Study Analysis Leveraging Oracle's Life Sciences Data Hub to Enable Dynamic Cross-Study Analysis Presentation Transcript

    • Copyright  BioPharm Systems, Inc. 2009. All rights reservedLeveragingOracles LifeSciences Data Hub toEnable Dynamic Cross-Study AnalysisMike GrossmanVP Clinical Warehousing andAnalytics
    • Agenda• Dynamic Analytics Overview• Approach to Dynamic Analytics• Data Preparation• Data Selection• Model Building, Analytics, and Reuse• Framework and LSH• Questions and Answers2
    • Agenda• Dynamic Analytics Overview• Approach to Dynamic Analytics• Data Preparation• Data Selection• Model Building, Analytics, and Reuse• Framework and LSH• Questions and Answers3
    • Examples of Dynamic Analytics• Study and Program Feasibility– Enrollment success prediction– Modeling around inclusion/exclusion criteria– Cost prediction– Investment decision support– Marketing approach determination• Predicting risk factors for diseases in patientpopulations– Product monitoring and risk assessment– More focused labeling– Modeling and simulation for portfolio management4
    • What do we mean by Dynamic Analytics?• Data preparation and conforming• Data selection and analysis• Longitudinal data mart preparation• Model building, training/confirmation• Applying new data to the model to obtain results• Evaluating results, revising the model5
    • Dynamic Analytics – SystematicApproachIs there a way to establish a systematicapproach to dynamic analytics so itbecomes part of the standard clinicaldevelopment processes?6
    • Agenda• Dynamic Analytics Overview• Approach to Dynamic Analytics• Data Preparation• Data Selection• Model Building, Analytics, and Reuse• Framework and LSH• Questions and Answers7
    • Dynamic Analytics - OverviewIn this use case, Dynamic Analytics involves four stages:– Data Preparation (Acquire, Transform, Enhance, Standardize)– Data Selection & Preliminary Exploration– Model Building & Analytics– Deployment & ReusePreparationSelection &ExplorationAnalytics &ModelBuildingDeployment& Reuse8
    • Dynamic Analytics ProcessStage 1. Data Preparation(Acquire, Transform, Enhance, Standardize)Historic Dataset FilesStudy DataEDC data and otherstudy data DataStandardizationAEDM …OutcomesStage 3. Analytics & Model BuildingAnalyze, Define andTrain ModelSecurityWorkflowControl Data Blinding Life Cycle ManagementWorkflow ManagementStage 4. Deployment & ReusePredictive Analysis ComponentsSelection ComponentsAd hoc &Std AnalysisValue AddedProcessingStage 2. Select & Explore(Acquire, Transform, Enhance, Standardize)Selection Components9
    • Holistic Reference, Clinical IT ReferenceArchitectureOutcomesCommon DataModelProject levelConformed DataValue AddedStudy DataConformed StudyDataOperational TrialMetricsInboundDataSourcesMaster Meta DataAES & ComplaintsOutcomesExternal StudyDataLIMS/PKCentral LabsCDMS/ EDCCTMSStagingAreaAES & ComplaintsSource SpecificOutcomes DataShared Study andProject MetaDataStudy SpecificData StagingTrialsManagementWarehouseAreaSpecialized DataMarts forScientificExploration andMiningSpecialized DataMarts forScientificExploration andMiningSpecialized DataMarts forScientificExploration andMiningPatient SubSetting andSafetyWarehouseClinops DataMartsMeta Data Libraries, Version Control, Compliance Change MgtAd-Hoc Query Dashboards Structured Reports Analytical ToolsStrategicAnalysisRegulatoryReportingData MiningClinicalDevelopmentPlanning10
    • Agenda• Dynamic Analytics Overview• Approach to Dynamic Analytics• Data Preparation• Data Selection• Model Building, Analytics, and Reuse• Framework and LSH• Questions and Answers11
    • Stage 1 - PreparationGet the data into a form which supports exploratory analysis.This involves:– Gathering the data• EDC data, SAS historic data sets, other internal or external sources– Conforming the data• Clear understanding of the original meaning of the data• Mapping to a standard• Clear identification of study and subject characteristics• Establish a library of reusable data conformance components– Storing the data in a repository for subsequent selection andanalysis12
    • Stage 1 – Preparation - Conforming• Study specific conforming for EDC and other study data• Any standard conformed structure should work• Most companies use a modified SDTM+• Conformed data can be used by many other parts of thebusiness. For example:– Data Cleaning– Formal status analysis– Data listings and reporting– CDISC SDTM• Initially conform to the same shape and focus on the samemeaning with terminologies, such as MEDDRA and codelists, and standard units. Expand common meaning asgoals as experience increases13
    • Agenda• Dynamic Analytics Overview• Approach to Dynamic Analytics• Data Preparation• Data Selection• Model Building, Analytics, and Reuse• Framework and LSH• Questions and Answers14
    • Stage 2 - Data Selection & PreliminaryExplorationInteractively examine the data in order to gain the correctpatient population for analysis.• Select – Subset the data based upon study and subjectcharacteristics in order to create an exemplar set of data totest the hypothesis.• Preliminary Exploration - Identify the outcome variables,dependent variables, independent variables and domainsto be used by the analytical methods.15
    • Stage 2 - Data Selection• Interactive subsetting of studies and subjects• Subset based on study characteristics and limited set ofsubject domains• Dimensional model required to increase performance anddynamic nature of subject subsetting• Example facts/domains for initial implementation– Study Characteristics– Trial Inclusion/Exclusion Criteria– Trial Summary– Demographics– Exposure and Concomitant Medications– Adverse Events/Diagnosis16
    • Stage 2 - Data Selection – Study StarStudyFactIndicationMEDDRAHierarchyStudyPhaseProgramSub-PopulationRegionCompound(WHOD)or DeviceDesign17
    • Stage 2 - Data Selection – DM StarDMFACTSTUDYSITE/REGIONGENDERSUBJECTRACEAGE INYEARS18
    • Stage 2 - Data Selection – CM/EX StarEX/CMFACTSTUDYSUBJECTStart DateEnd DateDrug PTHierarchyDoseFormRoute ofAdmin19
    • Stage 2 - Data Selection – AE StarAEFACTSTUDYSUBJECTStart DateEnd DateMEDDRAPTHierarchySeveritySerious20
    • Stage 2 - Data Selection – SharedDimensionsAEFACTMEDDRAPTHierarchySTUDYSUBJECTStart DateEnd DateSeverityDMFACTSITE/REGIONGENDERRACEAGE INYEARSSUBJECTSTUDY21
    • Step 2 – Data Selection ExampleDashboard22
    • Step 2 – Data Selection ExampleDashboard23
    • PoolsStage 2 – Data Selection – Delivery ofPooled Data Mart using LSHIndividualStudies(Domain)MyStudyASubsettingStarsSubsettingAStagingConformingDevDevQCProdQCProdDevQCProdMyStudyBSubsettingBStagingConformingDevQCProdDevQCProdMyStudyCSubsettingCStagingConformingDevQCProdDevQCProdDevQCProdDevQCProdAll DatasetsSubsetted bydata selectionprocessSubset NameA Subset NameB Subset NameC24
    • Agenda• Dynamic Analytics Overview• Approach to Dynamic Analytics• Data Preparation• Data Selection• Model Building, Analytics, and Reuse• Framework and LSH• Questions and Answers25
    • Stage 3 –Model Building and Analytics• Select and build a model to validate the statedhypothesis.• Build a set of parameterized methods that will test thehypothesis.• Execute the methods against the data produced in stagetwo, capturing results.26
    • Stage 4 - Deployment & Reuse• For useful analytical methods in step three, create a set ofuser accessible components that can be used with newsets of data.• Produce repeated results by:– Selecting patient sub populations– Utilizing predefined analytical methods• Results can be stored and shared with a wider community27
    • Stage 3,4 – Using Methods against DataSelectionIndividualStudies(Domain)MyStudyASubsettingStarsSubsettingAPoolsSubset NameAStagingConformingDevDevQCProdQCProdDevQCProdMyStudyBSubsettingBStagingConformingDevQCProdDevQCProdMyStudyCSubsettingCStagingConformingDevQCProdDevQCProdDevQCProdDevQCProdAll Datasetssubset bydata selectionprocessSubset NameB Subset NameCPoolsAnalysis MethodResult AAnalysis MethodResult BLibraries ofStandard andspecialtymethods28
    • Agenda• Dynamic Analytics Overview• Approach to Dynamic Analytics• Data Preparation• Data Selection• Model Building, Analytics, and Reuse• Framework and LSH• Questions and Answers29
    • Proposed Environment• Overall framework for managing data, results and methods– Oracle Life Sciences Data Hub• Primary tool for authoring analytical methods– SAS, Others such as R?• Ad hoc analysis and patient population selection– Spotfire, OBIEE, Others• Conforming the data– Informatica, SAS30
    • Oracle LSHAcquire• Rapid acquisition of data– No coding using reusable components– Automatic creation of target structures from source– Familiar use of Oracle tables and views, SAS datasets, Text files– Automated batch loads (scheduled or triggered by message)• Snapshots, Auditing and Security out-of the-box• Multiple data types– Clinical and Safety data– PK/PD data (including blinding)– Laboratory Data– Pharmacoeconomic data• Supports both warehouse and federated approaches– Data loads– Pass-through views31
    • Oracle LSHTransform, Enhance, Standardize• Multiple parallel data models– Standard data structures, e.g. JANUS, CDISC SDTM/ADaM, or Company Specific– Enables evolution of data models over time• Open technology– Use technology best suited to purpose/skill set• SAS, Oracle PL/SQL, Informatica• Version control, Snapshots, Auditing and Security out-of the-box• Multiple environments in a single application– Development, Test, Production• Data manipulation– Enhance for analysis– Pool across multiple different sources and studies– Slice data for in-depth analysis• Classification– Customer-definable folder structures– Powerful embedded search engine32
    • Oracle LSH - ControlSecurity, Data Blinding, Life Cycle Management• LSH APIS can automate complex tasks such as– Automatically adding studies to dimensional models– Automatically generate longitudinal data marts from subject subsets• In-built user management and security model– Roles and privileges– User and user group access– End-user administration tool• Data blinding/unblinding– Ensure blinding during ongoing clinical trials (GCP)– Privileged access to blinded data• Outputs generated on blinded data are stored in secure area• Reusability– All objects stored in libraries for easy re-use• Life Cycle Management– Designed to explicitly support SDLC according to Life Sciences regulations• Production Areas: Cannot make destructive changes, e.g. delete tables, columns, etc.33
    • Agenda• Dynamic Analytics Overview• Approach to Dynamic Analytics• Data Preparation• Data Selection• Model Building, Analytics, and reuse• Framework and LSH• Questions and Answers34
    • BioPharm Services for Integration andAnalytics• Business case development and cost analysis• Requirements and design management• Best practice analysis and recommendations• Installation and configuration• Oracle CDA and LSH pilots and proofs of concept• Hosting• Oracle CDA and LSH implementation• CDA and LSH validation• CDA and LSH training• CDA and LSH extension development35
    • Contact InformationIf you have additional questions, please contact:United States: +1 877 654 0033United Kingdom: +44 (0) 1865 910200Email Address: info@biopharm.comWebsite: www.biopharm.com36