>> Christine Gordon: Hello, my name
is Christine Gordon and welcome
to an overview of clinical data management.
An important aspect of the clinical research lifecycle
is the collection, processing,
storage and sharing of data for analysis,
which is the core of data management.
Today we will focus on clinical data management practices,
the need to apply rigorous processes
and procedures to the handling of data
often requires research teams and sponsors
to develop a data management plan.
Let's start by looking at some of the activities
associated with data management
and who is involved with these activities.
In this first session,
we will discuss what clinical data management
is and cover what clinical data management activities are,
as well as cover what a data management plan
is and its components.
So what is clinical data management?
It is a multidisciplinary activity
that includes investigators,
research nurses, study coordinators,
clinical data managers, database programmers,
bio statisticians, monitors and support personnel.
Clinical data management is the activities
involving the handling of information
outlined in the protocol to be collected and analyzed.
A variety of activities make up clinical data management
at the site, as well as for the sponsor.
And these can include data acquisition or collection,
data abstraction or extraction, data processing
and coding, data analysis, data transmission,
data storage and security,
data privacy, and data quality assurance.
So what is the data being collected used for?
It may be used to support the analysis of the primary
or secondary objectives of the protocol,
safety reporting, regulatory reporting,
new drug applications, support for labeling claims,
publications, or even planning future protocols.
As you know, good clinical practice guidelines
are an international ethical and scientific quality standard
for the design,
conduct and record of research involving humans.
GCP is composed of 13 core principles,
two of which apply to data,
specifically all clinical trial information
should be recorded, handled and stored
in a way that allows for accurate reporting,
interpretation and verification.
Also, the confidentiality of records
that could identify subjects should be protected,
respecting the privacy and confidentiality rules
in accordance with applicable regulatory requirements.
The FDA Part 11 regulations address electronic data
and e-signatures.
These regulations apply to all data
residing both at the institution site
and at the sponsor site. Create an electronic record
that will be submitted to the FDA.
The scope of these regulations include validation of databases,
audit trail for corrections in the database,
accounting for legacy systems and databases,
copies of records and record retention.
Implementation of new technologies and systems
to capture, store
and manage data has led to a growing awareness for the need
to document the process to ensure the security,
integrity and quality of data along its lifecycle.
As a result, the development, implementation
and maintenance of data management plans,
also known as DMPs,
has grown as good practice for conducting research
and is commonplace in most organizations that handle data.
The importance of planning and documenting data management
has led some organizations to require
DMPs as a condition for funding of grants,
and often sponsors or auditors will ask to review this document
along with standard operating procedures.
In addition to regulations and requirements,
the key to successful study is how early in the.
Process data planning starts. The quality of the planning
and the effectiveness of the implementation.
Data management plans are living paper or electronic records
that document the processes and procedures
to promote consistent, efficient and effective data management
practices on an individual study.
The reason why it's important to provide documentation
on a study level rather than a department or program level
is because each study has unique project and data requirements
that should be outlined and serve as a record of what
and how the data with handled.
The DMP should be designed to meet the needs of various types
of trials, patient registries, or other therapeutic areas.
The complexities and nature of a study can influence the DMP,
but there are minimum standards recommended by professional
clinical data management societies such as SCDM,
Society of Clinical Data Management, and AMIA,
the American Medical Informatics Association data management,
working groups, and these should include:
1) Having a plan in place prior to the first participant
being enrolled.
2) Assuring that the plan is in compliance with regulations
and oversight agencies.
3) Identifying and defining personnel and roles involved
in decision making, data collection, data handling and data quality control.
And 4) assuring data management
processes are described
and defined from study start to database lock
and steady closure.
It is important that the plan is current in version
to document process changes took place.
The type of research, be it a prospective,
observation, or registry or therapeutic area
can influence the content or design of a DMP
because of the variations in requirements,
types of data and methods used in collection.
Usually the scope of a DMP specifies
who is involved with the data handling,
what the deliverables tasks and processes are,
and how they are carried out.
Let's look at the different components of a DMP.
Roles and responsibilities, maintaining a current list
of all trained study team members
who are working with the data on the study is important.
According to FDA regulations for electronic records,
access must be limited to authorized individuals.
Some guidelines recommend maintaining a list of user roles
and access to security safeguards
which includes a plan for removing access.
Organization organizations may manage this
with a list of all individuals
who have access to the database and the dates of their access.
Though is not required as part of a DMP,
it is strongly recommended,
and some regulatory agencies require that there is a record
that individuals have the trained
or the skills documented to perform the task on the trial.
Description of data collected, some organizations
have a metadata file, a data dictionary,
or a document listing data collected for a study.
This will include any data
that is generated or received from sources
other than paper or electronic data capture forms
completed by the principal investigator,
study coordinator, data manager, or study participant.
The list of standards or terminology dictionaries.
The use of standard terminology or dictionaries
usually is in reference to coding of medications
and adverse events and user research submission
exchange standards, researchers, sponsors and regulators
want to know what standards and terminologies
are used along with the versions.
For a long term trial, versions may change
and it is important to document all versions.
Because versions impact the data,
there should be a description
of how the version was implemented for the study
if the entire database was versioned up
or only data after certain date.
If using an auto encoder, it is important to document
the workflow of how manually coded items
are handled and approved.
How data is collected, processed, and stored.
Planning how data is collected will help ensure
it is collected in a timely manner.
Documenting if data is captured using Electronic Data Capture,
EDC, paper, or directly from a participant
or even downloaded from a device is important.
For organizations using paper to collect data,
data will ultimately be entered into a sponsor's database,
so determining if single entry or double data entry
will be used is important.
Double data entry is entering the same data twice
to ensure data accuracy.
With the increase of electronic tools
it should be documented what sources are electronic.
For example some studies allow participants to enter data
directly into a tablet
that is automatically uploaded into a database.
In this case, the data uploaded is the source data,
and that should be documented.
Listing data transfers and how that data is processed
or where it is stored once it arrives should be described.
Data handling rules.
There should be written procedures on how data
is entered and cleaned for data provenance.
To ensure that changes or self-evident corrections
on case report forms are documented,
necessary and endorsed by the investigator.
Examples include conversions of units of measure,
guidelines for manual queries or corrections of misspellings.
Data sharing or access.
This component is fairly new to DMPs,
but is becoming increasingly more common
because of regulations requiring data sharing plans
to expedite translational science and increase knowledge.
Planning data sharing and access early in the study
can make certain there's compliance to regulations
and establish resources and processes
that will enable effective data sharing.
The plan should help ensure data is made available publicly
in a timely manner
and describe the methods used to provide access to the data.
More extensive DMPs may include the following components:
form design, edit checks, system validations
and testing, data flow diagrams, reporting metrics,
risk analysis, data exchange, auditing and quality control.
In this session, we learned that clinical data management
is a multi-disciplinary activity
involving the handling of information
to be outlined in the protocol to be collected and analyzed.
Clinical data management activities
include data acquisition and collection,
data abstraction and extraction, data processing encoding,
data analysis, data transmission,
data storage and security, data privacy,
data quality assurance.
Data management plans are often required
and are living paper or electronic records
that document the processes and procedures
needed to promote consistent, efficient
and effective data management practices for study.
At a minimum the components of a DMP
should include the roles and responsibilities
that all study team members a list of standards
or terminology dictionaries, how data is collected processed
and stored, data handling rules, data sharing or access.
A DMP may be more extensive
and include other components as well.
Here are two questions to check your understanding so far.
Can you answer what activities are part
of a clinical data management
and what is a data management plan?
Thank you for your time.