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Ensuring high quality data

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Ensruring the collection of high quality research data

Ensruring the collection of high quality research data

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  • 1. Implementation Science Workshop
    Ensuring High Quality Data
    28 October 2010
    Day Munatsi - Chief Data Manager
    Centre for the AIDS Programme of Research in South Africa
  • 2. Agenda
    Data Quality – Definition
    Quality Assurance Aspects
    Quality Management Considerations
    Quality Planning
    Quality Control
    Quality Improvement
    Further Analysis
    Summary
  • 3. Data Quality - Definition
    The degree of excellence exhibited by the data in relation to the portrayal of the actual scenario.
    The state of completeness, validity, consistency, timeliness and accuracy that makes data appropriate for a specific use.
    The processes and technologies involved in ensuring the conformance of data values to project requirements and acceptance criteria
  • 4. Quality Assurance
    "A task well done is its own reward.” Ralph Waldo Emerson
  • 5. Quality Assurance
    Quality assurance (QA) is the prevention, detection, and correction of errors or problems
    QA is closely tied to regulatory compliance
    Good practice must be closely tied to following regulations.
  • 6. Quality Management Considerations
    quality planning,
    quality control,
    quality improvement
  • 7. Quality Planning
    Make use of :
    Data Management Plan
    Standard Operating Procedures (SOPs)
    Best Practices (Guidelines)
  • 8. Quality Planning
    What data collection instrument will be used ?
    Where will we store the data ?
    Who will perform data entry?
    Training? On-line help?
    How data entry will be performed?
  • 9. Where do we start?
    Paper Data Collection Options
    Case Report Forms (CRFs)
    e.g. questionnaire, data collection forms
    Patient forms
    Existing data sources
    Databases
    e.g. MS Access, Epi Data, DataFax, OpenClinica
    Spreadsheets
    e.g. MS Excel
  • 10. CRF -Example
    VL600
    VL
    600
    02/03/2009
    15:00
  • 11. Database Design Consideration…1
    Hidden text fields, where text appears along with numeric values.
    E.g. 10-15 , <22
    Dates of all kinds
    E.g. “6/-/95” for June 1995 , mm/dd/yyyy Vs dd/mm/yyy
    Text fields and annotations
    E.g. Categorical (coded) values, Short comments, Reported terms, Long comments.
  • 12. Database Design Consideration…2
    Header information
    E.g. PID, Visit Date, Visit Code, Study ID
    Single check boxes
    E.g. Check if any adverse events: [ ]
    Calculated or derived values
    E.g. age ,number of days on treatment, weight in kilograms
  • 13. Dates – Special Note :
    Dates on a CRF typically fall into three categories:
    Known dates related to the study
    (visit date, lab sample date)
    Historical dates
    (previous surgery, prior treatment)
    Dates closely related to the study but provided by the patient
    (concomitant medication, adverse events [AEs])
  • 14. Other Considerations
    Avoid repetition
    Identify a unique identifier or primary key
    Ensure that individual column values are valid
    Follow “Do and Review” policy
  • 15. Case of Bad Data
  • 16. Keep it Simple
  • 17. MS Access – Database Design
  • 18. MS Access – Database Design (2)
  • 19. Even for MS Excel
  • 20. Data Entry Form – Microsoft Access
  • 21. Training
    SOP training
    Data management system training
    Specific workflows
    Study specific procedures
  • 22. Quality Control
  • 23. Data Entry Form – Epi Data
  • 24. Data entry considerations….1
    Define “must enter” fields –
    No record is complete unless: such and such is entered;
    Define “skip patterns” –
    If answer on field 1 is ‘No’ then jump to field 5.
    Define “edit checks”
    If Date Of Birth is inconsistent with today’s date , raise a flag
    Make data entry fool proof.
    Grade level can be entered as a number (4 or 4th or four). By using a pull-down menu with the correct data format these mistakes can be avoided.
  • 25. Data entry considerations….1
    Have at least 2 levels of data validation if possible.
    Double Data Entry,
    Define missing value codes
    D for Not Done , U for Unknown, A for Not Applicable
  • 26. Must Enter Fields
  • 27. Skip Patterns
  • 28. Check Constraints on Date Fields - 1
  • 29. Check Constraints on Date Fields - 2
  • 30. Pull Down Menu’s
  • 31. Quality Control Tools
    Make use of:
    Discrepancy management
    Weekly/monthly reports
    Quality Control reports
    Audit trails (if applicable)
    Visit reminders
    Data queries
    External system checks
  • 32. Quality Improvement
  • 33. Quality Improvement
    Relies on:
    Continuous training
    Interim Quality Assurance audits
    Change management procedures
    Documentation
    Knowledge Base
  • 34. Using Existing Data….1
    It should be:
    Consistent
    coding , variable naming, annotated
    Complete (almost)
    few missing data
    Relevant to study question
    avoid selection bias
  • 35. Using Existing Data….2
    Also consider the impact of:
    Free text
    Outliers
    Double / Single data entry
    The QC process
    Source Data Verification
  • 36. Source Data Verification
  • 37. Further Analysis
    Data can be exported to other programs for analysis.
    Examples include
    SAS
    SPSS
    MINITAB
    Excel
  • 38. THE END
    Questions and Discussion

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