conclusion and, and maybe having something that's inconclusive.
Again taking that concept one step further.
It's an ethics thing.
So we are no matter how safe we define a study,
no matter how much societal benefit there is to that study, and,
and that's what we're, that's our end objectives in any research study
that we are conducting, there is always going to be some risk.
Or some inconvenience to the individuals that are working with us on studies.
And so I always teach in data management that, that doing this, planning
things up front it's one of the most important things that we do.
If we don't, and we don't get as much scientific benefit out of
that even potential risk, or discomfort, or
inconvenience to our subjects we're really doing
a disservice to everyone.
So, another good practice that we'll think about as we're
going through as being exact and discrete when defining our variables.
We know that at the end of this study, we want to be able to analyze
this data, and we want to be able to sort of come up with an answer.
Of whether there is a synergistic effect between morphine and marinol.
if we don't think hard about it, if we don't come up
with things that are quantifiable in, in a way that is structured and
measurable, then you know all we'll have at the end of this study is a hunch.
Or, or, or something that we're not quite sure of
[COUGH]
We will, we'll look, especially at the beginning,
of how we'll kind of start brainstorming this.
We'll look at the confounding factors.
You know, making sure that at the end of the day, when we
get ready to study this, and if, if we see variations, that we've measured.
Things that, up front may be confounding factors that, that could potentially, play
a role in, separating our responses, even among healthy volunteers.
So, so first let's think about that.
So, demographics you know, I, I've worked
many, many studies over many, many years and
whether you call these demographics or participant
data, or, or, you know, somewhere in between.
There's always sort of this collection of one
time data about an individual that, that you're studying.
So, so if we look at the, you know, maybe, maybe taking the first one of
those might be name.
You know we need to identify that patient for later.
And, and again, here we could digress a little bit and we could talk about
the fact that some studies are, are
completely deidentified in terms of the data management.
They, they don't know and they don't need to keep
track of who, who an individual is after a visit.
So maybe it's completely deidentified.
in this case, we'll just make an assumption that we do need identifiers.
And so,
the first thing that we might we might consider is a name.
So, you know, name is pretty easy.
Mine is Paul Harris.
We could, we could have a, a, if we were doing this on paper, we could have
a put your name here line or if we
were doing it electronically, we could put your name.
You know, in this box, and I might type
Harris comma Paul, and I might type Paul Harris.
I might type P.
Harris.
So, so to get rid of the ambiguity there, it's probably a
good idea, even thing about things like the
discreetness factor when we, when we get into names.
And so we'll collect that as a first name and a last name.