1. Discrepancy Management
- Aditi Bhat
Ref: Neeman Medical International,Susann Proksha.

2. Discrepancy
Discrepancies are “Inconsistencies” found in the clinical trial data which need to be corrected as per the study protocol (the guiding document) OR
A Discrepancy is a variance between an actual response and the expected response as defined in question attributes and validation procedures.

3. At Site At Data Center Data entry errors
Electronic Data Acquisition errors
Incorrect database updates based on data clarification form or query
Programming error in user interface or database or data manipulations
Site personnel trial conduct error
Site personnel transcription error
Site equipment error
Human error in reading equipment or print out
Inadequate instructions given to the subject
Subject does not follow trial conduct instructions
Subject completes questionnaire incorrectly or provides incorrect or incomplete answers to questions
Data captured incorrectly on the source
Data entry errors
Fraud

4. Identifying Discrepancies
Manual review of data and CRF by Clinical data management
Computerized checks of data management system or entry application
Computerized checks or analysis of data by data management or biostatistics using external system

5. Discrepancy Classification based on its origin
In EDC discrepancy can be classified as
Discrepancy identified after manual review
Discrepancy identified by System
Discrepancy identified after manual review –
Identified during manual review of the data example –Reconciliation of Lab data, Safety data etc
It cannot be programmed
Example of Manual discrepancy –
Diabetes is recorded in Medical History however Conmeds for diabetes have not been recorded on the Conmed module

6. Discrepancy Classification based on its origin (Contd..)
Discrepancy identified by System –
Discrepancies identified by the system with the help of Edit checks
These are programmed
Example –
Pregnancy have been marked “YES” however the SEX is recorded as “MALE”

7. Types of Discrepancies In OC
In OC following types of discrepancies are observed
Univariate discrepancies
Multivariate discrepancies
Univariate discrepancies :
Univariate discrepancies are for single data point
These discrepancies are generated during data entry
Example: If the response does not meet the specifications such as wrong data type or field length.

8. Types of Discrepancies In OC (Contd..)
Multivariate discrepancies :
Multivariate discrepancies are for two or more data points
These discrepancies are generated post Batch Job (Batch Validation)
Example: The reason for IP withdrawal is recorded as “Adverse event” however the Adverse event has not been recorded

9. Process of Query Management
Create
Send
Track
Re query
Resolve
Update the database

10. Identifying Discrepancies
Manual Review
The first manual review of data frequently takes place when the CRF is received. CRAs or data managers go over the CRF before it is sent to data entry.
During the entry process discrepancies may be identified manually when the data entry operator cannot read a field or when the values from first entry and second entry differ.
Linked discrepancies are harder to identify automatically. Data Managers review discrepancies and try to identify duplicates and links.

11. Computerized Checks Range Check Consistency Check Manual Check
Discrepant Data Listings
HB value should be between mm of Hg
SAE stop date is before SAE start date
Duplicate entry
Discrepancy between CRF and listings

12. Identifying Discrepancies (Contd..)
External System Checks:
The data management system may not be able to support checks across patient records, visits, and pages (and even if it does, CDM staff may not have expertise to write those checks), so other applications are used example –SAS checks etc
These applications may be run by data management specifically to check data, or they may be used by other groups to review data and begin analysis.
Discrepancies may become apparent from any of these reports, analysis, or graphs.

13. Resolving Discrepancies
Answers that resolve discrepancies may come from:
Internal Groups:
Data Management
Clinical Research Associates
Investigator:
Discrepancies that are sent back to the investigators for resolution are called queries or DCFs (data clarifications forms)

14. Inconsistent response
Re queries
In Correct response
No response
Inconsistent response
Incomplete response
Duplicate answer
Same response

15. Query Management in Paper studies
Data Management raises query
Site Staff cross check with the Source document and send DCF
If not satisfied with the answer received sends the re query
Data Management Closes the query if satisfied with the provided answer
Data management updates the Database

16. Tracking of Queries
Data management tracks flow of queries to and fro between self and investigator
Data management ensures that query responses are received and integrated within the specified timeliness

17. Resolving Queries
Data management integrates the query response into the database within 2 working days of the receipt of the DCF in house
Common types of resolutions:
The value in question maybe correct as is
An actual measurement may replace a
missing value
A corrected value may replace an incorrect
value
The value maybe wrong but no corrected value is available

18. Confirmed as (Not a problem)
Resolution Types
Resolved
Confirmed as (Not a problem)
Can’t be Resolved
Data Management Edit
Acceptable Discrepancy

19. Best Practice
When any good discrepancy management system is teamed with an effort to identify discrepancies starting early in the data handling for a study and continuing throughout, it is possible to cut down the time to close of study to within a few days of receipt of the last CRF
Ideally, a problem that has been resolved should not continue to show up in lists of outstanding discrepancies. For e.g., if a discrepancy sent to a site asks them to investigate a value that is slightly high and they respond that the value is in fact correct, then the discrepancy should not be raised again, even if the discrepancy checks run over the same data.
After edits are made, it is essential to assure that all cleaning rules are rerun over the data. It is very common for updates to the data as part of a discrepancy resolution to cause some other problem with the data

20. Formula for Query Writing
The formula of “L S A”
Locate the discrepancy
State the discrepancy
Ask for resolution
A simple way of going about any query text is to first mention the location of the query, then state the discrepancy/issue, and later ask for the resolution

21. Questions

22. Thanks