Error Detection and Correction in Data Collection

1. Error Detection and Correction in Data Collection Julia Challinor, RN, PhD Assistant Adjunct Professor of Nursing University of California, San Francisco INCTR annual meeting 10-12 December 2005 Chennai, India

2. Data Audit • Questions about omissions and errors • NO “white-out” ink • Typographical mistake? • Due to poor training of the data managers for this study? • Is the mistake significant to the findings? • Does this site have more than average number of omissions and errors?

3. Data Manager • What if YOU make an error? • Data entry • The wrong value was inserted by hand • NO erasure • NO block coverage

4. Problems

5. Corrected

6. Problems

7. Corrected

8. Problems

9. Lab Problems

10. Lab Problems

11. Lab Problems • More labs than spaces • What to do? • ADD MORE CRF lab pages…

12. Data Entry Error • Put a single line through the value, write the correct value and date and initial the change • Notify your data center or appropriate person • Correct database Error Correction 14 mg 14 mg jc 4/5/03 17 mg

13. Finding Errors • It is essential that data entry is routinely verified • Double data entry • Expensive • Time consuming • Checking case report forms chosen at random • Two data managers check each other’s data entry • The principal investigator does a routine random check • A member of the research team does a routine random check

14. Reporting Errors • Who needs to know the error occurred? • Depends on the error • Hierarchy for reporting errors should be described in the study PROTOCOL • The principal investigator needs to be kept informed • A regularly scheduled review of data entry

15. History and Trail • Make a written notation of omissions and errors that have been corrected • Monitors will not expect perfection • But will need to be able to trace the omission or error for clarification if needed • It is not the responsibility of the data manager to determine the severity of an omission or error • This is the responsibility of the principal investigator and the sponsoring agency among others

16. Humans • Data managers are humans • Humans are not machines • Humans make errors

17. Errors • It is important that errors are noted and a monitor can follow a trail to clarify any questions • A group of case study forms that are perfect are more suspect than a group with some corrections

18. “Red Flags” • Items that alert you to a potential error • Test result value is significantly larger or smaller compared to the last test for the patient • A dose level or test result value is significantly different for this patient than all other patients on same protocol

19. Protocols • KNOW your protocols • Read the protocol • Ask questions if you do not understand any part of the protocol • Review the protocol if you have a question on a specific patient’s data • Data Managers usually see all the results for all the patients in a center on the same protocol • Individual physicians do not

20. Recommendations • Internet based training program for clinical studies • NIH has an elementary training at • http://ohsr.od.nih.gov/ • St Jude Children’s Research Hospital • Free training site in English and Spanish • http://www.cure4kids.org • “Educating Clinical Staff in Clinical Research Data Collection & Data Management