I remember specifically a study team coming to me a
number of years ago, and, and they used that phrase.
You know Paul, this is a real simple study.
We're going to have people come in for two visits.
And we're really just going to
do two things.
And we're going to do blood draw, and we're do from that a metabolic panel.
And we're going to shoot an MRI image. And, from that.
Well we're going to, we're going to do the study over, over two visits maybe it was
a randomization of, you know, you know a
little bit of randomization with the patients etcetera.
But, but from the, the standpoint of the data it was very, very simple.
Two visits, we're going to measuring a couple of things and then we're done.
So,
I started asking questions at that point.
I said, well, you know, I understand a little bit about imaging.
So, I also understand enough about it that I know that you
can't just sort of feed an MRI image into a statistics package.
So, maybe let's think about it, a little bit, what you're planning on doing there.
Is it the tumor size, is it a diameter, is it a volume type measurement?
Think about those things that are going to be coming out of that high-density
image data that you're going to be using for your quantitative analysis.
And from that, you know, maybe we came up with five or six
measurements that were going to be important at each of those imaging events.
And then we started talking about metabolic panel.
You know, that's not just one thing.
That's really a number of things, you know,
that might be the cholesterol, the glucose level.
And so we started thinking about that and
particularly putting things in, in terms of units.
It really helped to study the study team start thinking very discretely.
Oh yeah, yeah, I guess we do need a field for
glucose, I guess we do need one for, fo-, for, blood pressure.
Oh, and, and while we're collecting blood pressure, maybe we
could split that up into Systolic and Diastolic blood pressure.
So, so I'd say by the time that, study team left my office that day.
We probably had them up
to about 60 measurements that they were going to be collecting.
So the things that I just mentioned, as well
as things like, you know, do you really need
to know, you know, some sort of identifier for
the patient, or do you need to know a name?
An address, phone number, maybe gender or ethnicity.
Those things that might, might really play a
factor in helping you analyze this data later on.
So they're left with about 60 measurements
that they were going to be collecting rather
than two. But also left them with this exercise.
But I said, you know, go home and think about your primary, secondary hypothesis.
[INAUDIBLE].
Let's look at those outcome measurements.
Think about how you're going to organize that, and then come back to me, and come
back with an even richer set of
information and data that you're going to be collecting.
And I stressed, as I always do, the thinking
about this in terms of what exactly would be
stored in a you know in, in an Excel type field or a, a data table type field.
And what units would belong
to that particular measurement and if you, if, if
they do that, what I've found is you know,
really, it really is hard to start thinking about
MRI images in one of those Excel spreadsheet type cells.
And so I say well go, go do this exercise and really sort of
think about these things in a discrete way and then get back to me.
By the time we launched that study I think we had
about 400 variables that we were collecting on, on every patient.
And so, I started with two and
ended with 400 and, and I would say that, you know,
that, that's pretty common when you start thinking things are, are simple.
Lot of times they become more complex later on.
And that's good because we really want to make sure that
we are collecting the right things that we're going to analyze later.