Error Detection and Correction in Data Collection Julia Challinor, RN, PhD Assistant Adjunct...

Error Detection and Correction in Data Collection

Julia Challinor, RN, PhDAssistant Adjunct Professor of NursingUniversity of California, San Francisco

INCTR annual meeting10-12 December 2005

Chennai, India

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?

Data Manager

What if YOU make an error? Data entry

The wrong value was inserted by hand NO erasure NO block coverage

Problems

Corrected

Problems

Corrected

ProblemsProblems

Lab Problems

Lab Problems

Lab Problems

More labs than spaces What to do? ADD MORE CRF lab pages…

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 Correction14 mg 14 mg jc 4/5/03

17 mg

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

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

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

Humans

Data managers are humans Humans are not machines Humans make errors

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

“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

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

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