Sample

Clinical Data Management-I Unit 6

Unit 6 Discrepancy Management

STRUCTURE

6.1 Introduction

Objectives

6.2 OVERVIEW OF DISCREPANCY MANAGEMENT6.3 DATA VALIDATION PLAN

6.3.1 BATCH VALIDATION

6.3.2 STUDY VALIDATION PLAN/EDIT CHECK

6.3.3 UNIVERSAL RULING

6.4 DISCREPANCIES6.4.1 HOW TO ACCESS THE DISCREPANCIES

6.4.2 IDENTIFYING THE PATIENTS FOR REVIEWING

6.4.3 Types of Discrepancies

6.5 DISCREPANCY REVIEW AND RESOLUTION STATUS CODES6.6 AUDIT TRAIL6.7 DATA CLARIFICATION FORMS

SELF ASSESSMENT QUESTIONS6.8 Summary

6.9 Terminal Questions

6.10 Answers to SAQ and TQ

6.11 ABBREVIATIONS

6.1 IntroductionThis unit essentially deals with various aspects of Discrepancy

Management. Various topics of discrepancy management like Data

Validation plan, discrepancies, discrepancy review and resolution status

codes are discussed.

Harstern Pubgya University Page No. 103


Objectives

After studying this unit, you should be able to:

Define Discrepancy Management

Describe the process involved in Data Validation Plan

Describe the type of Discrepancies

Define Data Clarification Form

6.2 Overview of Discrepancy Management

Discrepancy Management is a critical part of Data Management. It plays an

important role in cleaning and submitting the data for final study analysis in

a clinical trial. A discrepancy can be defined as a message flagged/stamped

either manually or programmed check following the batch validation process

if the response to a question is invalid or the data recorded is unacceptable.

Verbs can be TRANSITIVE or INTRANSITIVE. i) In ‘transitive’ the action passes from the subject to an object.

E.g.: My cat killed a rat.

The action of “killing” is passed from the

‘cat’ to the ‘rat.’ Here ‘killed’ is a transitive verb.



6.3 Data Validation PlanData Validation Plan plays a key role in discrepancy management. It

involves:

Batch Validation

Study Validation Plan/Edit Check

Universal Ruling

6.3.1 Batch Validation:Batch Validation are programmed checks by Clinical Data Management

System (CDMS) to assure the validity and accuracy of the data.

Subject: The complete subject is the simple subject (a noun or a pronoun)

plus any words or group of words modifying the simple subject that tell who

or what the sentence is about. Thus, a subject is the person, place, or thing that acts, is acted on, or is described in the sentence.

Christopher Columbus discovered America

Subject Verb

The action of the sentence is expressed by the verb - ‘discovered.’

The noun ‘Christopher Columbus’ is doing the action of discovering.

Hence, ‘Christopher Columbus’ is the Subject in the sentence.

Sometimes the verb will express ‘being’ or ‘existence’ instead of action

It validates data against predetermined specifications and is primarily used

to check the efficacy data unique to the current study.

6.3.2 Study Validation Plan/Edit Check:Study Validation Plan is a document which records the edit checks for a

study in order to maintain consistency and validity of the data.



1. Preposition of time : on, in, at, for, before, after, until, till, between, by,

upto.

E.g.: She was healthy till yesterday.

2. Preposition of place: to, at, from, away, on, onto, of, in, into, out, upon,

inside, within, by, over, above, on top of, behind, in front of, below,

beneath, across, through, all over, throughout, between, among.

E.g.: Where do you come from?

3. Preposition of method and manner: by, with

E.g.: The boys skipped going to school with audacity.

4. Preposition of reason and purpose: with, of, for,

E.g.: I rented a house for my holidays

5. Preposition of possession: of, with, by

E.g.: The tomb of Akbar is in Sikandarabad.

The document is normally reviewed by Database Programmer, Project Data

Manager, Biostatistician, Clinical Team Lead.

The edit check document may be modified depending upon the sponsor’s

requirement during the study. The final version of the document after

incorporation of the changes, if any, during the study, is approved before the

database lock. Comments are incorporated into the Draft version and

programming of the checks commence after sponsor approval. The checks

are put into production after validation.

Examples:

1. Inclusion Criteria- The subject should be of the age range 18-25 years.

An edit check will be written in the document and programmed for

subjects, whose age is not within the range 18-25 years.

A discrepancy will fire in the database for which a Data Clarification

Form (DCF) shall be sent.



Some of the edit checks are repetitive in nature and hence can be

combined as per the event while programming. Generally, there are

around 100-250 checks, which can be written and programmed for a

study based on the number of pages and data points.

2. Please find below an example of an Edit Check document. The document

consists of edit checks for Demography Page and Concomitant

Medication Page. It briefly explains as to how the document should

appear.

Check No. CRF Item CRF

Page #Specific

visitCheck

Description DCF/Query text Discrepancy logic

Procedure Name Action

1 Birth date 1 Screening Birth date is missing

Birth date is missing. Please

provide the correct date.

Birth date should be provided.

DM_BLANK_01 DCF

2 Sex 1 Screening Sex is missingSex is missing. Please provide the sex.(M/F)

Response for sex should

be provided.DM_BLANK_02 DCF

3 Race 1 Screening Race is missingRace is missing. Please provide

the race.

Response for Race should be provided.

DM_BLANK_03 DCF

4 Start date 5 and 6 Screening

Name of the medication is

provided, however the start date is

missing.

Name of the Medication ( )

is provided, however the start date is

missing. Please provide the

date.

If Name of the

Medication is provided, Start date should be provided.

CR_BLANK_01 DCF

5 End date/Ongoing 5 and 6 Screening

Name of the medication is

provided, however the End date and

Ongoing is missing.

Name of the Medication ( )

is provided, however the

End date/Ongoing is missing. Please

provide the data.

If Name of the

medication is provided, the

End date /Ongoing should be provided.

CR_BLANK_02 DCF

Figure 6.1



6.3.3 Universal Ruling:A Universal Ruling can be defined as a document, which allows the

authorized Data Management staff to make self-evident corrections to the

CRF and/or database without issuing a Data Clarification Form (DCF).

Example:For an indicator question, the response is missing, however, data is

provided on the CRF for the question.

Action: Check the [ ] Yes [ ] No

Universal ruling is referred to by different names as per preference such as

Global Ruling, self evident correction document etc. However, they all refer

to the same document, Universal Ruling.

6.4 Discrepancies6.4.1 How to access the Discrepancies:

CDMS Process Flow

Identify Patient (s) for Review

Identify Discrepancies

Review Discrepancies against Database and Case Report Form

Change Discrepancy Status

Create Data Clarification Forms



Print Data Clarification Forms

Update the Database

6.4.2 Identifying the Patients for Reviewing: Patients for whom discrepancies need to be reviewed can be identified

in a number of ways based on present study needs.

All patients with unreviewed discrepancies.

Patients for a specific site with unreviewed discrepancies.

Patients with unreviewed discrepancies created during a certain time

period.

6.4.3 Types of DiscrepanciesA discrepancy is a variance between actual and expected responses as

defined in the Data Validation Document. When data fails a validation

check, the system generates and records a discrepancy in the CDMS.

There are four types of discrepancies, which are generated in CDMS, which

is system specific:

Univariate

Multivariate

Manual

Indicator

Univariate Discrepancy:Univariate discrepancy is generated when data is different from that defined

for the Data Collection Module (DCM) question (e.g., length or character in a

numeric field). Created during data entry, update or batch data load.

Univariate failure is errors that occur when data entered fail the predefined



field parameters. A univariate error is system generated, and applies to only

one field.

Relative Pronouns: They are used for the nouns (antecedents) used

before them. They are used in the following:

1.1.1.1.1.1.1.1.1 Subject Object Possessive

For persons who, that whom/who, that whose

For things which, that which/that whose/of which

A relative pronoun must always be placed as near its antecedent as

possible. It must also agree with its antecedent in number, gender and

person

E.g.: This is the woman who stole the ring.

(ant.) (re. pro)

Generally, the relative pronoun in the objective case is omitted.

E.g.: The student (whom) you wanted to punish is absent today.

When the cursor is leaving a response field, the system validates the data

entered based, on the following criteria:

Mandatory value is entered

Length

Decimal precision

Data type (character vs number)

Dates are complete

Value is valid within a discrete value group, if applicable

Value is within predetermined range

Multivariate Discrepancy:Multivariate discrepancy is generated during Batch Validation when data

points are compared within/across visits does not meet criteria for a

validation procedure.



Example;

Did the subject take the pills? [ ] Yes [ ] No

If yes, mention the number of pills taken_ __

Discrepancy message: No pills were taken, however the number of pills

taken is reported as ‘2’. Please check.

Manual Discrepancy:Any word that adds more meaning to the Noun is called an Adjective. It

qualifies a noun. Eg.: Ankur is a good player. The baby drank a little milk.

Correct Use of some adjectives:

a) Little (practically no chance) Deepak has little chance of being

elected.

A little (some chance) There is a little hope of his

success.

The little (whatever available) I shall give him the little money I

have.

b) Few (practically none) Few people are good.

A few (a small number) I have a few friends in my office.

The few (whatever available) I will pack the few things I have.

c) First (first in order) Yuri Gagarin was the first man to

go into space.

Foremost (leading, eminent) Einstein was the foremost scientist

of his day.

Indicator Discrepancy:Indicator Discrepancy is a check for a response to a question, which

determines a set of the remaining questions that require responses.



If a follow-up question is either not collected when it should be, or collected

when it should not be, Clinical Data Management System (CDMS) creates

an indicator discrepancy during batch validation.

Example:

A) Did the subject take the pills? [ ] Yes [ ] No

If Yes, mention the number of pills taken _______

If No, check whether the subject is out of the study.

6.5 Discrepancy Review and Resolution Status CodesPlease find below list of Discrepancy Review Status, Discrepancy

Resolution Status, and Definition which can be viewed in Clinical Data

Management System (system specific).

Discrepancy Review status

Discrepancy Resolution status

Definition

Inv Review A discrepancy that is issued to a site per DCF.

Resolved Global/Universal Ruling To be used when a data change is made per Global/Universal Ruling and the discrepancy is no longer valid.

Resolved Data Entry Error To be used when a data change is made to fix a data entry error and the discrepancy is no longer valid.

Resolved No Action Required For any discrepancy that is valid, yet information is present on the CRF indicating the data is correct and a DCF should not be sent to the site.

Resolved Data Modified For a discrepancy attached to a DCF where the data was updated and the discrepancy is no longer valid.

Resolved Inv Confirmed For a discrepancy attached to a DCF where the data was confirmed and no updated were made.



Resolved Inv-No Information To be used when a response on a DCF is not sufficient to close the discrepancy and the discrepancy is reissued on a new DCF.

Passive Review

A discrepancy that depends on the resolution of a different discrepancy for which a DCF is sent. This discrepancy is linked to the same DCF, but is not sent to the site.

On Hold A discrepancy that we are waiting for more information to process, e.g. a question is out to the client, waiting for another CRF page to come in.

Irresolvable* Inv-No Information To be used when a DCF with this discrepancy is never returned from the site and no changes are made to the database.

Unreviewed A discrepancy that has not been addressed by a CDC. This is the initial status of all discrepancies.

6.6 Audit TrailThe documentation that tracks the changes that have been made to

recorded data (e.g., case report forms) and/or databases. It is particularly

useful for reviewing the history of changes to data in a study.

6.7 Data Clarification FormsData Clarification Forms are queries, which are written to investigators for

clarifications, missing data etc. Edit check form the basis for writing a query.

The query text is written using discrepancies in the discrepancy module. An


INV CORR Update made per DCF

UNIVERSAL/GLOBAL RULING Update made per Universal/Global Ruling

DATA ENTRY ERR Update made because of a DE error

CRA CORR Update made per site-DCF

SPONSOR CORR Updates made at the request of the sponsor without issuing a DCF


automated process of CDMS generates a form called Data Clarification

Form, containing the queries.

The DCFs are sent to the investigators on which they provide clarifications

on the form, sign with the date and send it back. These responses are

updated to the database appropriately.

Data Clarification Forms consist of:

Study Name

Investigator Name

Site/Patient ID

Patient Initials

Reviewer

Date: (Reviewer Date)

From (Clinical Research Organization (CRO) from where the Data

Clarification Form is generated).

Form Name/Visit Name

Page Number/Date

Question/Comments

Resolution

Investigator’s Signature

Date (Investigator signature date)

DCF ID

Few Examples of Data Clarification Forms (DCFs) are as follows:

Example 1On Medical History Page (), the response for Date of Birth is missing.

Please provide the missing response.

Example 2



On Concommitant Medication Page (), the response for "Is the subject

currently taking medications" is yes. However, the data is missing. Please

clarify.

Example 3On Vital Signs Page (), the response for value of Diastolic Blood Pressure is

not within the normal range of 040 – 100 mmHg. Please provide the value

within 040-100 mmHg.

Example 4On Laboratory page (), the response for "Are there any positive results?" is

No. However ‘specify’ is provided. Please Clarify.

Example 5On Chest X-ray page (), "Are there abnormal findings on the Chest X-ray?"

is Yes. However, ‘specify’ is missing. Please provide the missing response.

Requery:Requery is sent when a resolution provided by the investigator requires

further clarification or if the resolution provided is not sufficient. For

example: DCF ID 548564

On Concommitant Medication Page (), the response for "Is the subject

currently taking medications?" is missing. Please provide the missing

response. If Yes, please provide the details.

Resolution: Please check as “Yes”.

Here in this case, the resolution has come back incomplete. The resolution

is Yes, however, details are not provided for the question as asked in the

query.

Hence, a requery will be sent asking for the details in reference to the first

query.



As per DCF ID 548564, the response for "Is the subject currently taking

medications?" is Yes; however, the details are not provided. Please provide

the missing details.

Self Assessment Questions1. DCF stands for

a. Data Construction Form.

b. Data Creation Form.

c. Data Clarification Form.

d. Data Clearance Form.

2. Data validation is synonymous with

a. Data Cleaning.

b. Discrepancy management.

c. Both a and b.

d. None of these.

3. Discrepancies are primarily for any

a. Incorrect clinical data.

b. Illegible clinical data.

c. Inconsistent clinical data.

d. All of the above.

4. Discrepancy management primarily

a. Consists of electronic and manual checks on the data to assure the

validity and accuracy of the data.

b. Validate data against predetermined specifications.

c. Ensure consistency and data quality.


5. Which of the statement is false

a. Electronic and manual discrepancies will be stored within the

confines of a discrepancy management system.



b. Data processing includes receiving, entering, cleaning and

transferring.

c. Univariate discrepancy appears post data entry.

d. Multivariate discrepancy appears post data entry.

6. DCF is raised to

a. Clinical site.

b. Sponsor.

c. Data Management Centre.

d. Pharmacovigilence.

7. Discrepancy management is primarily performed by

a. Clinical data Coordinator.

b. Data entry associate.

c. Database programmer.

d. Project Manager.

8. Universal rulings

a. Decrease total number of DCFs to the site.

b. Are self evident corrections.

c. Reduce time, labour and cost.


9. Discrepancy is a

a. Error.

b. Query.

c. Validation.

d. Resolution.

10. DCF consists of

a. Investigator name.

b. Investigator signature.

c. Resolution.




6.8 SummaryDiscrepancy management is an integral part of Data Management. It

includes cleaning and reporting of data recorded at Clinical trials. It

undergoes intensive quality checks, performed as per the Data Validation

Plan to ensure very high and reliable data. Discrepancy Management forms

a foundation for Data Management in every project and are based on SOPs

followed for the data management operations.

6.9 Terminal Questions1. Define Discrepancy and briefly explain the types of discrepancies?

2. Briefly explain Data Validation Plan.

3. Briefly explain Data Clarification Form.

4. Briefly explain why a requery is sent.

5. Explain how an Edit Check Document is prepared and why?

6.10 Answers to SAQ and TQSAQ1-C 2-C 3-D 4-D 5-C 6-A 7-A 8-D 9-A 10-D

TQ1. Refer to Section 6.4

2. Refer to Section 6.3






The boy climbed when the bus stopped.

In the above example, there are two clauses. Only one of them is a

sentence. i.e. which makes a complete sense.

6.11 Abbreviations

Abbreviation Term

CDM Clinical Data Management

CRF Case Report Form

DCM Data Collection Module

DE Data Entry

CDMS Clinical Data management System

DEA Data Entry Associate

CDMS Clinical Data Management System

SOPS Standard Operating Procedures

DVP Data Validation Plan

DMP Data Management Plan

CRO Clinical Research Organization


The boy climbed (Clause I )

when the bus stopped (Clause II)

Sample

Documents

harstern pubgya university

clinical data management

data clarification form

data validation plan

data entry error

concommitant medication page

discrepancy resolution status

edit check document