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

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.

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

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

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

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”

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.

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

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

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.

Computerized Checks Range Check Consistency Check Manual Check Discrepant Data Listings HB value should be between 12-14 mm of Hg SAE stop date is before SAE start date Duplicate entry Discrepancy between CRF and listings

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.

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)

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

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

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

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

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

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

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

Questions

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