1. Data Management seminar 05th October 2011
Gwenlian Stifin & Aude Espinasse
South East Wales Trials Unit, Cardiff University

2. Data management Overview
Aim of the session:
General understanding of the principles
underpinning data management for
clinical studies.
Overview of the data cycle in a clinical
study.
Overview of data management
procedures.

3. Background
Regulatory Framework
Good clinical practice is an international ethical and scientific quality standard for the design, conduct and record of research involving humans.
GCP is composed of 13 core principles, of which the following 2 applies specifically to data.

4. GCP – core principles for data
Background
GCP – core principles for data
The confidentiality of records that could identify
subjects should be protected, respecting the
privacy and confidentiality rules in accordance
with the applicable regulatory requirement(s).
All clinical trial information should be recorded,
handled, and stored in a way that allows its
accurate reporting, interpretation and
verification.

5. Data Sequence

6. WHAT IS A CRF?
A case report form (CRF) is a printed or electronic form used in a trial to record information about the participant as identified by the study protocol.
CRFs allow us to:
record data in a manner that is both efficient and accurate.
Record data in a manner that is suitable for processing, analysis and reporting.

8. KEY QUESTIONS Designing CRFs, key questions:
What data is required to be collected?
Only data we specified in the proposal/protocol.
Only data required to answer the study question.
When will this data be collected?
Baseline / follow-up .
What Forms will need to be designed.
Who is going to collect/complete this form.
Are there validated instruments available?
How is the data going to be analysed.

9. Data Sequence

10. WHAT IS METADATA? Metadata is structured data to organise and
describe the data being collected.
It is centralized data management.
It is a tool to control and maintain data
entities:
Content and variable definitions
Validation rules
Metadata consistently and effectively
describes data and reduces the probability
of the introduction of errors in the data
framework by defining the content and
structure of the target data.

11. Metadata File
Name of Trial/Study: PAAD (Probiotics for Antibiotic Associated Diarrhoea) - stage 1
Metadata Author: H S
Number of Data Collection Forms for Trial/Study: 10
Name of File (Corresponding Data Collection Form): Recruitment CRF 02
Form
Variable
Variable Label
Data Type
Format
Length
Linked
Skip
Validation
Title
Name
Value
Missing
Condition
Type
Labels
Codes
Recruitment CRF 02
datecons
date of consent
date
dd/mm/yyyy 10
range
warn if < >
sugender
service user gender
category
1 = Male, 2 = Female 1
consss1
consent for SS1
0 = no; 1 = yes

12. CRF AND DATABASE DESIGN
Study outcomes in protocol define what questions are asked in the CRF.
Use of validated scales and questionnaires.
User-friendliness and ease of completion important.
Database is built to receive data extracted from the CRFs.
Database needs to include querying and reporting tools.
Data needs to be coded into numbers to facilitate statistical analysis.

13. DATABASE DESIGN
Database allows for adequate storage of study data and for accurate reporting, interpretation and verification of the data.
2 database systems tend to co-exist alongside one another:
Study management database: personal
information, recruitment, data completeness
(CRF receipts) follow-up triggers…
Clinical database: clinical information (study
outcomes).

14. DATABASE DESIGN Functionalities to consider in both types of database:
Validation rules (Ranges, skips, inconsistencies…).
Queries / report.
Audit trail.

15. TEST/VALIDATE THE DATABASE
Check
Ranges, Skips, inconsistencies, missing data i.e. what is on your metadata is exactly what is applied when entering the data on the form
Check output file for data export (for clinical database)
Variable names match up/are all there
Coding of categories correct
Numbers when alpha required
What is on the form is transferred exactly into CSV / SPSS

16. Data Sequence

17. DATA COLLECTION Validity of data collection must be ensured.
Source data is identified and data transcribed
correctly onto data collection system.
Process of data collection/transcription is audited
throughout the process (monitoring – Source data
verification).
Next we will discuss Data collection
It is very important to ensure the validity of the data collected
You will need to ensure that source data is identified and data transcribed correctly onto data collection system.
It is also very important that the process of data collection/transcription is audited at all times
We will discuss these points in more detail over the next few slides

18. DATA COLLECTION Before starting data collection Testing SOP and PRA
Training
During data collection
Audit
TESTING
After set-up, test or pilot the system before you use it.
Maintain an adequate record of this procedure.
Firstly I will discuss what to do before starting data collection, and then what to do during data collection
Testing
After the data collection system has been set-up and before you start collecting data you must test or pilot the system
You must also maintain an adequate record of this procedure.

19. DATA COLLECTION SOP and PRA
Good idea to write a Standard Operating Procedure or a working practice document detailing how you set up your electronic data capture systems.
The appropriate persons need to be trained in these.
Need to write a Privacy Risk Assessment, this document includes:
Personal data items held in study e.g. name, DOB
Individuals who are granted access to this data
Procedures for colleting, storing, and sharing personal data
How personal data will be anonomised
Identifying possible breaches of confidentiality and how these can be reduced
It is a good idea to write a Standard Operating Procedure or a working practice document detailing how you set up your data collection systems.
The appropriate persons need to be trained in these.
You will need to write a Privacy Risk Assessment, this document includes:
Personal data items held in study e.g. name, DOB
A list of individuals who are granted access to this data
The procedures for colleting, storing, and sharing personal data
How personal data will be anonomised
The document will also identify possible breaches of confidentiality and how these can be reduced
The PRA needs to be updated on a regular basis, this is normally held in the Trial Master file, although you may have a similar document called a delegate log in the site file

20. DATA COLLECTION TRAINING
After piloting, when it is working as it should, next step is to train all users of the system
A record should be kept of the training
A detailed diagram and description of how data will be collected should be provided at training.
After piloting, when the data collection system it is working as it should, the next step is to train all users of the system
A record should be kept of the training
A detailed diagram and description of how data will be collected should be provided at training.
There should be documented training on the importance of security and data protection, we will discuss this more later.

21. Participant flowchart
Woman identified and agrees to be
approached
Assessed for eligibility and consented
Baseline data (CAPI)
Randomisation
Intervention
Control 34 -
36 weeks gestation (CATI)
Birth (CRF)
6 month post partum (CATI)
1 year post partum (CATI)
2 years post partum (CAPI)
Birth
Routine
antenatal care
FNP visits &
usual services
Usual
services
18 month post partum (CATI)
FNP visits
& routine
Key
CAPI: Computer Assisted Personal Interview
CATI: Computer Assisted Telephone Interview
Data collection
Participant flowchart
Participant progress
This is an example of a data collection flowchart used at training

22. DATA COLLECTION AUDIT
Maintain an audit trail of data changes made in the system.
Procedure in place for when a study participant or other operator capturing data, realises that he / she has made a mistake and wants to correct data.
Important that original entries are visible or accessible to ensure the changes are traceable.
You will need to maintain an audit trail of any changes made in the data collection system.
A procedure should be in place to address the situation when a study participant or other operator capturing data, realises that he/she has made a mistake and wants to correct the recorded data.
It is important that original entries are visible or accessible, for example in the audit trail, to ensure the changes are traceable.

23. ELECTRONIC DATA COLLECTION WHAT IS THIS?
Variety of software and hardware now
being used to collect data: PC
Laptops
mobile devices
audio
visual
transmission
web-based systems
Electronic data collection – what is this?
A variety of software and hardware are now being used to collect data, these include different systems such as: PC
laptops
mobile devices such as BlackBerrys
Audio equipment such as voice recorders
Visual equipment such as camcorders
transmission
and web-based systems

24. ELECTRONIC DATA COLLECTION WHAT IS THIS?
Some of the fundamental issues we have
discussed are common to all modes of
electronic data collection as well as data
collection on paper.
IMPORTANT: There should be no loss of
quality when an electronic system is in
place of a paper system.
The fundamental issues that have been discussed (for example, testing, SOPs, training, and audit) are common to all modes of electronic data collection as well as non-electronic data collection.
And importantly, there should be no loss of quality when an electronic system is in place of a paper system.

25. ELECTRONIC DATA COLLECTION SPECIFIC TRAINING ISSUES
Training on the importance of security; including the need to protect passwords, as well as enforcement of security systems and processes.
System user should confirm that he / she accepts responsibility for data entered using their password.
Maintain a list of individuals who are authorised to access data capture system and add to PRA.
Ensure that the system can record which user is logged in and when. Timely removal of access no longer required, or no longer permitted.
There are however some specific training issues that are relevant to electronic data collection .
Training on the importance of security; including the need to protect passwords, as well as enforcement of security systems and processes.
The system user should confirm that he/she accepts responsibility for data entered using their password.
Ensure that you maintain a list of individuals who are authorised to access your electronic data capture system and list these individuals on your trial’s Privacy Risk Assessment.
You should also ensure that the system can record which user is logged in and when. There should be timely removal of access when no longer required, or no longer permitted.

26. DATA ENTRY Different types of data entry exist,
(manual /optical mark recognition system,
online/offline, etc…).
Type of data can also influence the method of
data entry (numerical, free text, images etc…).
It is important to have documented procedures
(SOPs) defining who is performing data entry and
how it is performed.
Different types of data entry exits
These include:
manual
optical mark recognition system, for example teleforms
Online
Offline
The type of data collected can also influence the method of data entry.
It is important to have documented procedures (SOPs) defining who is performing data entry and how it is performed.

27. DATA ENTRY Data entry procedures should be tested at the
earlier design stage, and testing adequately
documented before sign-off.
Adequate training on these procedures should be
provided.
Appropriate quality control procedures have to be
set up.
So to summarise what we have already discussed:
Data entry procedures should be tested at the very start of the design stage, and testing must be adequately documented before sign-off.
Adequate training on data collection procedures should be provided.
Appropriate quality control procedures also have to be set up.

28. ELECTRONIC DATA ENTRY
Electronic entry does not usually have to be a separate ‘data entry phase’, normally entered during collection straight onto an electronic CRF.
Data can be entered straight onto a website, or can be entered onto a laptop and uploaded using the internet onto a server.
When designing forms to collect data electronically you can include ‘validation rules’. An electronic system can stop the Researcher from proceeding with data collection if they break a validation rule.
Electronic data entry
Electronic data entry does not usually have to be a separate ‘data entry phase’ as it is normally entered during collection straight onto an electronic CRF.
Data can be entered straight onto a website (which is automatically sent to a server), or can be entered only a laptop and uploaded using the internet onto a server.
Please also keep in mind that when designing forms to collect data electronically that you can include ‘validation rules’. An electronic system can stop Researcher from proceeding with data collection if the break a validation rule, for example, in a pregnancy study researcher cannot enter a number over 42 when asked for ‘weeks gestation’

29. AFTER DATA COLLECTION
Regular backups should be made of your data, if outsourcing data collection or storage ensure that the company have backup systems in place.
After trial has finished using data capture systems, you may need to dispose of these or send them to another company e.g. if they are loaned. Before doing this, you may need to professionally erase the hard drive as it may still contain participant information.
May need to archive whatever data you collect, includes both hard copy and electronic data, documents not archived need to be disposed of securely.
After data collection, regular backups should be made of your data, if outsourcing any data collection or storage please ensure that the company have backup systems in place.
After the trial has finished using the data capture systems, you may need to dispose of these or send them to another company, for example, if they are loaned. Before doing this, you may need to professionally erase the hard drive as it may still contain participant information.
You may need to archive whatever data you collect, this includes both hard copy and electronic data, and documents not archived need to be disposed of securely and confidentially.

30. COLLECTING DATA SAFELY
The safe collection of data in clinical trials is essential for compliance with Good Clinical Practice (CPMP/ICH/GCP/135/95) and the Data Protection Act 1998.
Because of increased use of information technology in the collection of trial data there is a need to have clear guidance on how to safely collect data in this manner.
Need to protect your data capture systems from loss or unauthorised access, at the same time ensuring that it is accessible to those who need it.
Collecting data safely
The safe collection of data in clinical trials is essential for compliance with Good Clinical Practice and the Data Protection Act.
With increased use of information technology in the collection of trial data there is a need to have clear guidance on how to safely collect data in this manner.
You will need to protect your data capture systems from loss or unauthorised access, while at the same time ensuring that it is accessible to those who need it.

31. COLLECTING DATA SAFELY ………CONTINUED
Need to protect participants’ identity by using Participant Identifiers (PID). PID’s should be used when communicating with other trial team members.
Electronic info particularly vulnerable to security threats:
can be physically accessed.
could be loss or damage to computer.
can be remotely accessed through internet or virus.
For each tool that you use to collect data, must ensure that system is password protected and encrypted.
You will need to protect participants identity by using Participant Identifiers instead of names, Date of Birth etc. Participant Identifiers should be used when communicating with other trial team members.
Electronic information can be particularly vulnerable to security threats, it can be physically accessed, or there could be loss or damage to computer, or can be remotely accessed through internet or virus.
For each tool that you use to collect data, for example a PC, you must ensure that the system is password protected, and if possible, encrypted.

32. Data Sequence

33. DATA CLEANING Errors / inconsistencies / missing data spotted at
different time points depending on the study and
methods used.
Errors should be corrected where possible, but no
changes should be made without proper
justification.
Appropriate audit trails should be kept to document
changes in the data (queries form, SPSS syntax…).

34. DATA CLEANING No Yes Data validated
Data manager cleans and validates data entered in the database
Problems found such as missing values or inconsistencies
Queries addressed to sites
Site resolves and sends back the queries
Data manager checks queries resolution
Corrections are entered onto the Database No
Yes
Data validated

35. REPORTING DATA
Throughout the course of the study it is usually the responsibility of the Data Manager to report on study progress, these kinds of reports include:
Recruitment progress
Follow-up rates
SAEs
Data completeness
Withdrawals
Throughout the course of the study it is usually the responsibility of the Data Manager to report on study progress, these kinds of reports include:
Recruitment progress
Follow-up rates
SAEs
Data completeness
Withdrawals

36. Thank you!
Any questions?