Ensruring the collection of high quality research data

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