Leveraging JReview as a Data Quality Solution

This presentation will provide an overview on how reconciliation and/or validation rules can be defined and trial data can be checked against these rules. By utilizing JReview’s built-in browser and advanced functionalities, objects can be defined with drill-down capabilities to perform these data validation checks. For e.g. reconciliation checks between EDC data and external lab data can be easily performed by reviewing a summary object with discrepant information such as subject id, discrepancy category and discrepancy message with an ability to drill down to detailed discrepant data listings. This approach should support pro-active data management for ongoing trials increasing the overall data quality. Similar approach can be applied to review data against sponsor defined data standards checks.

Transcript

1. April 30, 2019 Confidential Presentation Leveraging JReview as a Data Quality Solution Raj Indupuri & Chandi Kodthiwada
2. Agenda • Data Quality Challenges • JReview Solution Overview • Data Reconciliation Business Case • Data Standards Business Case • Q&A
3. Data Quality Challenges Data Reconciliation • Very tedious ü Different sources and systems ü Variant structures and formats ü Labor intensive • Access and Ease of use ü Different refresh cycles ü Error-prone if performed using spreadsheets • JReview ü Interactive with drill-down capabilities ü Self-service ü Why did it happen? ü What’s happening now? • Proactive Data Management ü Ongoing review and verification • Reusable across trials ü Global Objects ü Customizable Data Standards • Difficult to validate compliance checks ongoing • Difficult to validate sponsor and protocol related checks • Difficult to get visibility during trial conduct ü Intensive programming and SAS based backend processes
4. JReview Solution Overview – How? Specifications • Define Categories and Items for creating an analysis friendly discrepancy panel • Add Notes to provide further insight into the discrepancy • Conceptualize Run-time parameters
5. JReview Solution Overview – How? Design/Programming • Implement a Materialized View • Programming will abstract all the source data type disparities & structure variances in source data from end-user JReview Integration/Object Development • Import SQL development [Discrepancy Item Categorization & Identification] • Develop Objects based on business needs: ranging from Discrepancy metrics per site to Subject level discrepancy listings • Slice and Dice data: Allow Object drill-down from a high-level summary to a detail subject level listing
6. Data Reconciliation - Requirements 5 Define discrepancy details Category Item Notes Subject Identifiers Subject Initials Subject Initials Mismatch Date of Birth Date of Birth Mismatch Sex Sex Mismatch Visit Discrepancies Visit/Planned Time point Name Not in eCRF Data Visit/Planned Time point Name Not in External Vendor Data Data Discrepancies Date/Time of ECG Date Mismatch ECG Result Result Mismatch Completion Status Test marked complete but not in External Vendor Data
7. Field Name Column Heading Derived Category Derived Item Derived Notes EG.USUBJID/EP.USUBJID Unique Subject ID EG.EGTEST/EP.ECTEST ECG Test Name EG.VISITNUM/EP.VISITNUM Visit Number EG.VISIT/EP.VISIT Visit EG.EGTPT/EP.ECTPT eCRF Planned Time Point Name EP.EPTPT External Planned Time Point Name EG.EGSEQ eCRF Sequence Number EP.EPSEQ External Sequence Number EG.EGDTC eCRF Date/Time of ECG EP.EPDTC External Date/Time of ECG EG.EGSTAT eCRF Completion Status EP.EPSTAT External Completion Status EG.EGSTAT1 eCRF Completion Status at each Time point EP.EPSTAT External Completion Status DM.SEX eCRF Subject Sex EP.EPSEX External Sex DS.SUBINIT eCRF Subject Initials EP.SUBJINIT External Subject Initials DM. BRTHDTC eCRF Birth Date EP.EPDOB External Birth Date EG.EGORRES eCRF Result EP.EPVAL External ECG Evaluation Variables to reconcile (ECG eCRF vs. ECG External Provider) Data Reconciliation - Requirements
8. Data Reconciliation - Objects 7
9. Data Reconciliation - Objects 8
10. Data Reconciliation – Design and Develop 9 Source Dataset/Table •Identify Sources: •EG (eCRF ECG Data) •EP (External Vendor ECG Data) View Programming •Develop a view with aggregated Identifier information from both sources and join the source data back to the aggregated Identifier information effectively joining data wherever applicable Materialized View/Table •Performance: Run the view every time? Query a static table [Maintenance] ? Import SQL •Discrepancy Categorization •Discrepancy Identification JReview Object Development • Build Objects • Summary, Detailed & Graphs
11. Data Reconciliation – Merged View
12. Data Reconciliation – Design and Develop Import SQL
13. Data Standards - Requirements 12 Define data standards checks Data Validation Category Data Validation ID Data Validation Item Severity Consistency C0001 Duplicate --SEQ Error Consistency C0002 Duplicate USUJID, with different SUBJID Error Presence SD0001 No records in data source Warning Presence SD0069 No Disposition record found for subject Warning Presence SD0070 No Exposure record found for subject Warning Presence SD0002 Null value in variable marked as Required Error
14. Data Standards - Objects 13
15. Data Standards - Objects
16. Data Standards – Design and Develop 15
17. Q & A 16
18. April 30, 2019 Confidential Presentation Thank You!