Transcript Data Management - Cardiff University

Data Management seminar
05th October 2011
Gwenlian Stifin & Aude Espinasse
South East Wales Trials Unit, Cardiff
University
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.
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.
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.
DATA SEQUENCE
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.
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.
DATA SEQUENCE
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.
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
Format
Variable Label
Title
Data Type
Name
Length
Value
Labels
Recruitment
CRF 02
Linked
Validation
Condition
Type
Validation
Missing
Codes
datecons
date of
consent
date
sugender
service user
gender
category
1 = Male, 2 =
Female
1
consss1
consent for
SS1
category
0 = no; 1 = yes
1
dd/mm/yyyy
Skip
10
range
warn if
<01.11.2010 >
01.06.2012
CRF AND DATABASE
DESIGN
• Study outcomes in
protocol define what
questions are asked
in the CRF.
• Database is built
to receive data
extracted from the
CRFs.
• Use of validated
scales and
questionnaires.
• Database needs to
include querying
and reporting tools.
•User-friendliness
and ease of
completion
important.
•Data needs to be
coded into numbers
to facilitate
statistical analysis.
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).
DATABASE DESIGN
Functionalities to consider in both types of
database:
• Validation rules (Ranges, skips, inconsistencies…).
• Queries / report.
• Audit trail.
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
DATA SEQUENCE
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).
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.
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
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.
Participant flowchart
Participant progress
Data collection
Woman identified and agrees to be
approached
Assessed for eligibility and consented
Baseline data (CAPI)
Randomisation
Control
Intervention
FNP visits
& routine
antenatal care
Routine
antenatal care
Birth
34 - 36 weeks gestation (CATI)
Birth (CRF)
6 month post partum (CATI)
1 year post partum (CATI)
FNP visits &
usual services
Usual
services
18 month post partum (CATI)
2 years post partum (CAPI)
Key
CAPI: Computer Assisted Personal Interview
CATI: Computer Assisted Telephone Interview
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.
ELECTRONIC DATA COLLECTION
WHAT IS THIS?
Variety of software and hardware now
being used to collect data:
•
•
•
•
•
•
•
PC
Laptops
mobile devices
audio
visual
email transmission
web-based systems
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.
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.
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.
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.
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.
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.
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
………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.
DATA SEQUENCE
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…).
DATA CLEANING
Data manager cleans
and validates data
entered in the database
Problems found such as
missing values or
inconsistencies
Corrections are entered
onto the Database
Yes
Data manager checks
queries resolution
Queries addressed to
sites
Site resolves and sends
back the queries
No
Data
validated
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
Thank you!
Any questions?