Download DataManagement - The King`s Health Partners Clinical Trials

Data Entry and Management
for Clinical Research
Matthew Simpson
Joint Clinical Trials Office
The Basics of Data
What is Data Management
The main stages of the data management process:
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The raw data are collected and entered into the computer, and
checked;
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The data have then to be organised into an appropriate form for
analysis (often in different ways, depending on the analysis)
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The data have to be archived, so that they remain available
throughout subsequent phases of a project, and afterwards (5, 7,
21 years).
Data & the Law
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All data stored and used relating to an individual is governed by the
Data Protection Act (DPA)
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Any data relating to a clinical trial with an investigational medicinal
product (IMP) has legislation based on Good Clinical Practice (GCP)
governing its storage and use
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Clinical research not involving an IMP must adhere to aspects of
DPA and it is good practice to adhere to GCP
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Investigators are ultimately responsible for the data they collect.
Legal Frameworks w.r.t Data
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The Medicines for Human Use (Clinical Trials)
Statutory Instruments:
 2004 no 1031 (‘EU Directive’)
 2006 no 1928 (‘GCP Directive’)
 2006 no 2984
 2008 no 941
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Declaration of Helsinki (1996)
Data Protection Act (1998)
Human Tissue Act (2004)
Mental Capacity Act (2005)
Second Research Governance Framework (2005)
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Anonymous Data
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Truly anonymous recordings made for
treating/assessing patients may be used within the
clinical setting for education or research purposes
without express consent as long as this policy is
well publicised.
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Truly Anonymous:
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Apparently insignificant features may still be capable of
identifying the patient to others, such as distinguishing
marks, tattoos, body piercing, posture and gait.
Research shows it is usually impossible to be sure that a
patient will not be identifiable from a image or voice
recording
Data Protection
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If data collected for research purposes is anonymised or pseudoanonymised, it does not fall within the scope of the Data
Protection Act and therefore will not require the usual procedures
to be followed.
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Special provisions for research (Research Exemption):
 Data must be used exclusively for research purposes
 Data must not be used to support measures or decisions relating
to any identifiable living individual
 Data must not be used in a way that will cause, or be likely to
cause, substantial damage or distress to any data subject
 The results of research or resulting statistics must not be made
available in a form that identifies any data subject.
Sensitive Data (non-Anon.)
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You must have the specific written permission of the data subject to hold
sensitive data unless you already have a legal requirement to process those
data.
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Security must be appropriate to the degree of harm caused by the misuse of
data
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Types of Sensitive Data:
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Racial or ethnic origin
Political opinions
Religious, or other similar beliefs
Trade Union membership
Physical or mental health or condition
Sexual life
Convictions or alleged criminal acts
Written, informed consent is obtained before each subject's
participation in the trial
Data Protection: Role of the Investigator
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The Investigator ensures that data is to be collected (prospectively
or retrospectively) with informed consent given by the data subject.
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The Investigator documents in the protocol what data is to be
collected and how it will be analysed (deviations from this will
require an amendment to the protocol and likely resubmission to
REC)
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The Investigator ensures that data will not be used for anything
additional to what is specified at the time of consent.
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The Investigator ensures appropriate security arrangements for both
electronic (back up/ password protection) and paper (locked
cupboard) files.
What data are you collecting?
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Four types of data are collected in most clinical research
protocols:
 Baseline data
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Efficacy data
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Assessments specific to the objective of the study
Safety data
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Patients state of being prior to initiation of protocol
Ongoing records of patients health until E.O.S.
Compliance data
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Subject deviation from protocol
Collecting Data
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Prior to beginning study:
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Review research protocol
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Identify all the data points you need to collect
Determine complexity
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The data is simple if all the records are a single type of unit,
e.g. numbers (Heights, weights, ages, BP’s, single values).
The data is complex where data have been collected from a
number of different units or levels. For example, oncology
studies will have lesion data as images, blood and serum data
from labs (separate files?).
Good Clinical Practice and Data
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Case Report Forms / Data Collection Forms
 Do you need to create a separate form to collect the clinical data
– how big is your study?
 Separate forms allows for more effective Auditing and Quality
Control.
 Designing data collection forms facilitates the collection and entry
of data and reduces the number of recorded errors.
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Electronic Data Storage
 Will you need to record the data beyond the paper version
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Can you guarantee your meeting your regulatory requirements
Do you have an audit trail of your data changes – electronic files can
be locked prior to analysis – providing a guarantee to the data validity
Data Management
Data Entry Basics – pt 1
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When planning a strategy for data entry:
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the aim should be a fully-documented archive of validated,
correct & reliable data that can be subjected to scientific scrutiny
without raising any doubts in the minds of subsequent
researchers.
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Many research projects do not achieve this.
Data Entry Basics – pt 2
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When planning the system, aim to make the data entry stage as
simple as possible:
 For example, in a replicated experiment it should never be
necessary to type long names or long codes within each visit
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Simplifying the keying process will speed the task, make it less
tedious and hence also less error-prone.
The logical checking phase should be done by trained staff who
understand the nature of the data. Usually this phase involves
preliminary analyses, plotting etc (more later).
Annotated CRF’s?
TRIAL MEDICATION
1- Adalimumab
Statistician requirement?: Define numeric values
2- Azathioprine
for all defined data fields.
3- Ciclosporin
4- Depo-Medrone
5- Etanercept Folic Acid
Annotate a document to provide to the
6 -Gold Injections
Statistician. It greatly accelerates and inproves
7- Hydroxychloroquine
the job of analysing the data
8- Infliximab
9-Kenalog
10- Leflunomide
11- Methotrexate
12 -None - refer to Periods off Trial Medication Form
13 -None - withdrawn patient who is not taking any of the listed trial meds
14 -Penicillamine
15 -Prednisolone
16 -Sulfasalazine
Further Data Entry
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Data should be collected and recorded carefully. Consider what checks
can be incorporated into the data collection routine.
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For example, the best and worst patients/ animals could have a one-line comment to
verify, and perhaps explain, their exceptional nature. This will confirm that they were
not recorded in error.
Build in further checks if your software allows.
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The simplest are range checks, but other, logical checks can also be used. For
example, for a particular inclusion/ exclusion criteria or that visit dates are
sequential.
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If possible, use software for data keying that has some facilities for data
checking (validation or logic assessment).
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Do not trust visually comparing the computerised data with the original
records.
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Though often used, it is not considered a reliable method of finding key-entry
errors, print out computer and compare with originals (sign and date the
comparison as proof of validity).
Designing a Data Entry System
Few projects generate simple data;
 Try to foresee the full range of different types of data that will be
collected
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Build facilities in the data collection for recording all such information
e.g.: comment areas, image recording, etc.
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Often data will be collected from the same patient on a number of
visits.
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Dates of such records must be kept, with space available on the
recording sheet for notes about the patient or visit issues that the
investigator feels may warrant recording but is not part of the
protocol
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Such secondary information will be valuable at the data analysis stage to
explain any curious behaviour of the data.
Designing a Data Entry System – pt2
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Ensure that the database system clearly specifies the
units of measurement used for all quantitative variables.
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Changes in research staff, or in methods of data collection, may
bring about changes in measurement units.
Consideration must be given at an early stage of the database
design to allow for such changes to be incorporated into the data
recording system
Codes maybe needed to distinguish between information
collected on different visits. (Eg; continuation of an AE
with change to severity)
Databases for clinical research
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Defining standardised tables for your clinical
research can accelerate creating subsequent
management programs and help identify the
data you need:
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Adverse Events
Concomitant Medications
Past Medical History
Demographic Data
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All patients in research will have information unique to them, to which
all other information is related – principally their name or subject ID
(blinded study)
Data Cleaning
Interim Analysis
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The interim analysis is a continuation of the checking
process and should include a first look at summaries of
the data. Useful things to produce at this stage are:
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extreme values, in particular the minimum and maximum
observations;
boxplots, to compare groups of data and highlight outliers;
scatterplots, especially if you use separate colours for each
treatment
tables of the data in treatment order.
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- Maintaining study blindness? CTIMP SAE’s?
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End of Study
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How do you guarantee data validity at EOS?
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End point data review; check all critical data for every
subject with source/ raw data
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Sample review; take 5% or 5 patients at random, which
ever is greater, and review the entire data set for each of
those subject.
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Write SOP’s for each process and sign and date the review
upon completion.
Inform REC and R&D office of study completion
Archiving
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The data and programs from a research
project must be archived in such a way that
they are safe and can be accessed by a
subsequent user.
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In the absence of a proper archiving scheme,
a common outcome is that the researchers
leave, carrying with them the only copy of
their part of the data, and hoping that the
analysis and write-up will be continued later.
Electronic Data
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Ensure and document that the electronic data processing
system(s) conforms to the sponsor’s established requirements
for completeness, accuracy, reliability, and consistent intended
performance (i.e. validation).
Maintains SOPs for using these systems.
Ensure that the systems are designed to permit data changes in
such a way that the data changes are documented and that there
is no deletion of entered data (i.e. maintain an audit trail, data
trail, edit trail).
Maintain a security system that prevents unauthorized access to
the data.
Maintain a list of the individuals who are authorized to make data
changes
Maintain adequate backup of the data.
The Audit Trail
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An audit trail is a complete record of changes to the data and
decisions made about the data and the analysis, rather like a log
book.
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A well-maintained audit trail greatly eases the subsequent tasks of
writing reports on the data and of answering data queries.
It is a legal requirement for a drug trial
It is important to record everything you do at the time that you do
it
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E.g.: when errors are found during checking and changes are made
to the master copy of the data, a note should be made in the audit
trail.
Keep notes also on the analyses that you do (including the
preliminary ones done for checking purposes), writing down the
names of all files created. Every change should be dated and
initialled (with a pen colour different from the original – use black for
recording data first time, and red for changes).
Validity of your Data
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There is nothing new here:
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just re-stating a fundamental requirement of the
scientific method that you should ensure that your
work is repeatable by keeping good records of what
you do
Backing Up
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It is essential to develop a system for regular "back-ups"
(copies) of your data files:
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Omitting to do so may result in important parts of the research
data being lost.
Project managers should establish a documented routine for
regularly making safe copies of the data, and should insist that all
members of the research team follow the routine.
Always record your processes as SOP’s (Standard
Operating Procedures)
Further Thoughts/ Questions?
My Details:
Matthew Simpson
Joint Clinical Trials Office
[email protected]
www.jcto.co.uk