Hello everyone.
My name is Marjorie Good, or Marge Good, I go by.
I am a nurse consultant with the Division of Cancer Prevention within NCI.
And in that role, I'm a nurse consultant working with NCI grant-funded,
community-based research programs of which there are about 46 across the U.S.
now and one in Puerto Rico.
And I actually came from a community-based program, which has really helped me
with this new role to help new sites that are coming on board.
I was with the program in Wichita,
Kansas, actually, and did that work for over 20 years,
managed the research program there, which was quite busy and active.
We actually averaged usually between 600 and 800 patients
enrolled in clinical trials in a year's time.
So we were busy.
Was heavily involved in clinical research and with that I came away
with a lot of knowledge that I like to share with as many as possible.
So today is one of those efforts to let you know
a little bit more about data management and the development of case report forms.
And I know that there's a varying degree of expertise, as well as job roles.
I think that we'll be participating in this training, so
I kind of tried to base this around that -- with that in mind.
So some of it may seem really basic to others
and maybe a little bit above standards for others, as well.
So I'm happy to take questions at the end.
So today I hope we can discuss the importance of the proper data collection
efforts, identify the types of data collected for clinical trials, list
potential sources of the -- can be used for source documents for that data
collection, name key factors to consider during case report form development.
Discuss things that we need -- really I think are important
to consider when you're developing CRFs.
And I add a little more information with this one from my last presentation
as a request of some of the comments after the presentation.
So hopefully that additional information will be informative for you, as well.
I also want to then cover what constitutes a poorly designed case report form.
So hopefully it'll give you -- inform you not to go down that road.
Then we want
to talk about data management itself, which is quite large.
And within that, it's going -- I like to review the audit process
as themselves, describe adverse event reporting, and then describe some of
-- very lightly on regulatory requirements for data collection.
So the use of data is very, very important.
Data analysis and reporting, the actual capturing of the data
is what informs the analysis and the reporting,
the publication that goes on after the study's completed.
It's a method of tracking the patient or the subjects.
It's used for FDA safety monitoring.
It's used in new drug application submissions.
It supports those labeling claims
for new drugs that have gone through the FDA process.
It's reviewed by data safety monitoring boards
to assess the trial safety and ongoing activity.
It's used in publications and medical journals,
and it definitely helps to inform future development of trials.
So the actual data management reporting.
It's the outcomes of the clinical trial are dependent on this data
that is collected, and it needs to be collected accurately,
and in a timely manner.
It needs to be verifiable.
So those are those things we're going kind of talk about today.
Data must also reflect the actual aims of the clinical trial,
and it must comply with regulatory agencies.
And really, all of this is key.
You're going to get all this information if you have adequately designed
case report forms.
So basically, there's this old adage that I hear still frequently.
It's garbage in, garbage out.
If you don't have good case report forms,
you're not going to get the data and the information that you need.
The research teams play a really vital role in this process.
They can be made up of such levels I've listed on this slide.
The principle investigator, clinical research nurse,
or CRA, the data manager, data base administrator, and statistician.
All these people play a key role.
However, there are other people that can play a role in it, as well,
depending on the type of study that you're trying to conduct.
If it's --
you also may need to include radiation oncologist, nutritionist, case workers.
Again, it depends on your study that you're trying to develop.
Part of the main thing to remember here
is the principle investigator really does play a key role.
He's responsible -- he or she is responsible
for all aspects of the trial, including the ethical conduct
of it, of the research, the eligibility, protocol adherence, adverse event
reporting, and the reporting of timely and adequate data.
It's a lot to carry, but it's --
and PI is principle investigators need be aware of these roles.
They also determine the response to therapy and intervention and determines
whether the adverse event
may be attributed the actual agent or drug that's used in a trial.
Each member of the team, however, does play a key -- a role in conducting
research ethically, maintaining confidentiality,
and following the code of federal regulations, and complying HIPPA.
So in
order to do that, the training is essential.
The protocols often considered a roadmap.
It's a document that includes information and hopefully it's written clearly
so everyone understands that, but it provides info regarding why
this study's been done, the hypothesis behind it, the aims in
how the study is going to be conducted to meet those aims.
The protocol should contain sufficient detail so that there is uniformity
in the selection and treatment of patients, and uniform
data collection, and submission.
So things to consider whey you're developing the protocol.
What data elements are you going to be collecting?
What is the design and the content of the data collection instruments
then that need to be considered to capture that data?
And then what type of computer database are you going to be utilizing?
Common data elements is a term that's often used.
And it's actually quite important in developing your case report forms.
They're elements that have been determined to be identical between projects
and context, and the examples might be name, age, gender.
There's not too many ways to define those.
They tend to be fairly common across studies.
So they're considered common data elements.
And I'll go into a little bit more detail as we go further into this discussion,
but they really do help to facilitate the understanding
and sharing of that information across studies.
So if someone else wants replicate, or do a similar type of study,
would want to use the same data elements, and that would then inform
maybe a future meta-analysis that someone would like to do.
Meta-analysis are much easier to do when there are common
data elements being used between trials.
So at study entry, some data elements that could be captured,
or the demographic data, eligibility criteria, family history, patient history.
All of these are pieces of information that are collected at the time of study
entry and each of them may have multiple data elements within them.
So on study, each category, again, can contain multiple data elements
and they may consider capturing the treatment, the assessments of labs,
radiology, things that have been done in the interim, during treatment.
What kind of concomitant meds were taken, adverse events,
hospitalizations, treatment response?
And then there are patient reported outcomes, forms that may be completed,
as well, such as diaries and quality of life questionnaires.
These are things you want to consider
as you're developing your case report forms.
Source documents are basically any document where the data is first recorded.
It's where the information for the forms
-- case report forms is captured from.
They confirm the protocol adherence, they confirm and validate
the data being submitted is actually there and real.
It also serves audit trail, allowing the recreation of the study.
And it also confirms the actual existence of the study participants
I think you have or will be having your lecture on scientific conduct
and hopefully within that there was some examples shared regarding fraud.
And it actually does happen.
Some investigators make up patients.
And doing an audit or actually having source
documents helps to track and find those issues.
So source document examples might be simply is the hospital
records, the clinic and office charts, and may include various reports.
The actual progress notes, including physicians as well as nurse notes.
Other
examples might be letters from referring physicians, actual radiological films,
tumor measurements,
participant diaries, participant
interviews, and pharmacy dispensing records, and photographs.
So actual data abstraction then is the anything that basically is considered
in this, is anything recorded on a CRF should be in the source document.
If there's something recorded on the CRF,
it has to be tracked back to a source document.
And a common statement or common post-it-note,
IR post, or I would put around oftentimes for
my staff was basically to keep in mind
if it's not written, it didn't happen.
That's how the auditors will see that.
It may have actually happened, you wrote it on a CRF,
but if it's not written in the source, it's actually not replicated
or found in the source document, then they're basically going
to consider it falsified information, possibly.
Any correction or change should be dated, initialed, and explained,
and should not -- the original information should not be obscured.
And this applies to the CRF as well as the source document.
Only provide the requested data.
Some research staff I've had like to go on and write
a book sometimes, or a lot of information that's not necessary.
Just -- the CRF should be designed well enough
to capture the information if they want and what is needed.
And use standard medical terminology, as well.
And as far as the corrections and the data abstraction, too,
the thing to keep in mind is I've had occasionally staff
that would use post-it-notes kind of as a reminder
for themselves to go back in and make that note.
Then they forget or
they forget to take the post-it-note out when the auditors appear.
And so, it doesn't look very good.
So post-it-notes should be not used in these cases.
So again, data abstraction.
The auditors when they come in should be able to reconstruct
that patient's study course by piecing together all the data
that's obtained from those source documents.
So the methods of data collection.
Just to give you kind of a pictorial view, very simple,
of a possible electronic captured form, as well as paper.
Those are the two main types of forms that are currently in use.
A case report form is basically a preprinted,
if it's printed, form, developed by the sponsor or the PI
to determine the data elements that are going to be outlined in the protocol.
It basically translates the protocol specific activities into data.
They ensure there's actual standardization
and consistency of the data captured across multiple sites,
so the data that's collected in
Kansas is the same data that's collected in Colorado.
Design.
The protocol report case report forms are hopefully designed
to impact the quality of the data collected.
And basically, proper designed CRF actually streamline audits
make them easier and also help with the data analysis and reporting.
So I put together a bit of a pictorial based on a previous presentation
of the actual protocol, which describes the study conduct.
It goes through the purpose objectives and operational aspects.
And then there's the analysis plan, which is part of the protocol document, also.
But the data analysis then describes the data that's going to be collected,
key endpoints that are going to be looked for, as well as the statistical methods
that are going to be used to analysis the data.
The case report form supports those two things.
That's how vital a case report form is.
So, I think this view of it says a lot.