This course presents critical concepts and practical methods to support planning, collection, storage, and dissemination of data in clinical research. Understanding and implementing solid data management principles is critical for any scientific domain. Regardless of your current (or anticipated) role in the research enterprise, a strong working knowledge and skill set in data management principles and practice will increase your productivity and improve your science. Our goal is to use these modules to help you learn and practice this skill set. This course assumes very little current knowledge of technology other than how to operate a web browser. We will focus on practical lessons, short quizzes, and hands-on exercises as we explore together best practices for data management.
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Del curso dictado por Vanderbilt University
Data Management for Clinical Research
371 calificaciones
De la lección
Planning a Data Strategy for a Prospective Study
This module reviews the process of planning data elements for a real-world research study.
Conoce a los instructores
Stephany Duda, PhDAssociate Professor
Department of Biomedical Informatics
Paul Harris, PhDProfessor
Biomedical Informatics and Biomedical Engineering
So we finished talking about the demographics and
what we might collect in terms of the
one time data about information and, and the
information that we might collect on their baseline visit.
we didn't talk yet about how we might collect the
data for the, for the thermal stimuli for that baseline visit.
But, we'll kind of, kind of cover that as we look at what we might
record and how we might measure information
we're collecting in every single follow up visit.
In this case you remember we're taking the
individual, were, we're randomizing at the beginning of the
study for that individual how they will have a
drug combinations will be administered from visit to visit.
And, and each given visit we going to pick the number out of a hat as well.
And we going to, we going to choose a random
combination of, of pain sequences to, to put these
individuals through.
So let's talk about the the measurements at the individual visit level.
So, so we bring patient and we, we know what drug combination
they're in because that was predefined at the beginning of the study.
We, we gave them that drug combination, we wait for the appropriate amount of time.
We've got everything set up, it's consistent from
patient to patient and from visit to visit.
This thermal stimuli.
And what we're going to do, is we going to run
through a sequence of of, of.
providing this thermal stimuli to these individuals' forearms.
And we're going to do the thermal, thermal sequences in a
randomized order as, as we see here on the left.
This Combo one equals 37, 49 and then 51 degrees.
and then the next one might be 37, 51 and 49 etcetera.
So that we're not.
following the same protocol for the same individual
within a given visit or, or across visits.
So, so once we start administering those those, those rods to
the, to the forearm for five seconds, then we're going to ask them.
We're going to measure both the sensory pain,
the intensity as well as the effective pain.
And we're going to do those according to the way
the study did it and the way the study published.
We'll do those in, in sort of separate components.
So,
so we administer the pain in the first
three sequences and we'll ask about the sensory pain.
How, how intense is this?
And, and, then we'll go, we'll give a rest,
we'll come back and we'll, and we'll record again.
the affective pain that, that we're administering there.
We'll do that four cycles and that way we've got.
two sets of measurements on each pain type.
and for each of those pain types we have
two measurements per per, per, temperature.
Two measurements of sensory at 37 degrees to, to,
to at 51 degrees for the affective pain etcetera.
So we'll have a pile of data there as we, as we get done by measuring
these visual analogue scales, measuring how, how they're
indicating their pain levels on those visual analogue scales.
The drug cocktail,
again we talked about that earlier but as we collect
visit data and as we store information about the visit.
In addition to the date of the visit as we talked about earlier.
Maybe the weight of the individual on that particular day
and those individual measurements of pain associated with individual temperatures.
that, that were applied then, then we also need to tell at the visit
level what we need to record and be able to maintain the data around what
drug cockti, cocktail that they had earlier.
And we talked through this this, this whole randomization
sequence in a, in a previous slide in this.
In, in a previous deck in this section. So they had
[INAUDIBLE],
you know one other thing that that we might collect at that visit level.
in reading the paper, reading through the the manuscript that we've been kind
of following along with that you see again in the top right hand corner.
I said that at the end of every visit,
they also ask if there were any physical or
mental sensations other than those related to the heat
probe testing that you experience after receiving the drugs today.
As well as monitoring for adverse events.
And again, adverse events here might
be something that's coming up that's unexpected.
Maybe maybe an individual.
Maybe there is some sort of allergic reaction and, and
they're having a very strong you know reaction to this
heat stimuli rather than the normal course, so we want
to make sure we are keeping track of those things.
If, if they are happening then, then we would act immediately
to sort of, sort of look at look at any types
of consideration that, that, that, might put this.
Or any future individuals in, in, in any kind of harms
way, way or any kind, in a kind of unexpected inconvenience.
So, so in any given study we're going to be
looking for certain types of expected or unexpected adverse events.
So th, they ask open ended question.
And then we'll have some places that we need to think about.
how would we, how would we code and how would we
structure in these adverse events.
Okay so, so really we're, it's a very sort of simple
study and we've simplified it a little bit for this this
[UNKNOWN]
for this particular course.
But I think it does sort of drive home some
of those best practices that we've talked about in words.
We're applying it to this real world. Use case this real world study.
So, so in the next series of videos, what we're going to be doing is actually
going to the keyboard and we're going to be
looking at implementing these types of data measurements.
Sort of putting this plan together in an electronic data capture system.
And again, we'll learn lessons
along the way.
D, during during that process and at the end we'll come back and.
And review some of the best practices that we've applied in both of
the thinking phase of this as well as in the implementation phase of it.
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