I have a
question about the threshold for certainty. Yes.
Or uncertainty?
So in particular, I guess I was wondering, let's
say that,
scientists just want to, like, see
if a study is is replicable.
It seems like they'd have a certain degree of certainty,
but
it's still valuable to, to do it again.
So. Yeah.
Great point.
And so.
Equipoise for having this community
agreement
about uncertainty is about the first time
you're doing a study right now
in terms of replication to a couple important things.
One is replication is always good, right?
Like if you find an outcome that is positive,
it's good to replicate that finding,
at least in one more trial.
It's really the exception to the rule
that,
one trial changes clinical practice.
We could talk more about
examples of that, but often it's the case
that this group is doing a trial.
They find this another group is doing a trial.
They find this a group of people look at that data and say, okay,
we have enough now to change clinical practice.
So there's value in that replication, but
it's a different expectation of uncertainty.
Once you've done the initial trial,
the other important thing though is that
as I'm sure you all know, not every study,
especially those with negative results,
appear in the literature.
So it's another reason why it's really important
to have negative results in the literature.
And I don't mean negative by, you know, bad results.
What I mean is
let's say you test A versus B and
you find out that they are no different.
Well, maybe the company that sponsored
you isn't interested in moving forward.
Maybe the journal doesn't really care.
Like you didn't find anything.
You just found that they're the same.
So if that doesn't go into the literature,
then someone else says, hey,
I want to ask this question.
I wonder if anybody has.
They look it up. No. Right. So,
sometimes that community
level of agreement has to be more
than just looking at the literature.
Sometimes there's, like a state of the art
NIH will often sponsor things like
state of the art treatment for hepatitis
C, and they get all the experts in a room,
and they develop what they think might be a research
agenda that ought to influence how the field goes forward.
So that's a more formal, you know, community
engagement of finding, you know, what to do next.
But, often it's just relies on the
literature.
Yeah. Yeah.
This is, super interesting.
My question is regarding the data
monitoring, community. Yes.
So for an IRB, it's usually like,
there's like some clinician scientists
and other scientists
and then some non-scientists.
But I'm wondering for the,
the monitoring communities, are those usually just comprised
of people
that are able, that are going to be able
to, like, interpret raw data?
Or do they also have non-scientists?
And does it like if it's, I don't know, like
a,
a trial related,
to, cancer or is it going to be mostly
like ecologists, like.
Great question.
So and I'm sorry I didn't mention this earlier.
So thank you so much for the question.
So in general, they are, as I said,
either assigned to a trial or assigned to a set of related
trials.
And so there will always be
experts in that field on the data
monitoring committee or whoever
is creating the data monitoring committee,
whether it's the a steering committee
for the study or a sponsor is saying
you must have a DMC.
There's always going to be expertise
about the particular condition
on the DMC.
In addition to that, in my experience,
there's also always a biostatistician.
And so.
Right, I mean, I know enough bio stats to sort of get me to
hear,
but you really need someone who knows a lot
about biostatistics with some of these studies
that are very complicated in terms of their analysis.
And in my experience, there's an ethicist at the
table.
So I don't know.
That is not every group is going to have content
experts,
a biostatistician and an ethicist,
but a lot of some of them do.
Now, the other thing that is true
is that there's usually someone on the study
who's a biostatistician who is not blinded to the
outcome,
and his or her job is to prepare
a report for the data monitoring
committee.
And part of the data monitoring committee is going to be closed.
That is, that only the data monitoring
committee members are on,
because they're going to be given data
that if the Pi saw, it might bias
his or her, you know, choices or decisions
or engagement with participants.
So there's a inside man or inside woman
who's preparing that data, and they're an expert in their
field.
And then the biostatistician can also look at that and give
feedback.
And there might in my experience,
I know Dave has experience too.
They're relatively small.
You know, there are 3 or 4 content
experts.
If it's a network trial, there might be a couple more
because you need a little more expertise
to cover the waterfront, as it were.
But that's it's different in the.
Not every IRB has the specific
expertise, like a data monitoring
committee.
What,
Maxine asks, could you give example
of the most common reason studies
fail in phase three or phase four?
I'm trying to understand why so many interventions do
well through phase two and then crash and burn.
So basically, one of the reasons why it goes
down from,
you know, 100 in phase one, down to 70 in
phase two, down to 38 ish in phase three.
Is is time right that those early studies are
about safety
and biological metabolism
for looking at whether or not in a phase two, whether or not
the particular intervention has an effect
on a particular type of tumor.
In oncology, for example, it isn't until the phase three
trial
where you are now giving the intervention
to a large group of regular old people
in the sense that, you know, you've only
exposed
a relatively small number in those phase one and phase two
trials.
And so it's in the phase three where you're actually
doing this sort of head to head testing of compound
A versus compound B, or placebo versus compound A,
and you're not going to find
that the new compound is effective.
Right. You're doing the trial in order to answer that
question.
You answer the question. The question is no.
It doesn't move forward to market.
Now, there could be some other, you know,
commercial market forces that influence
whether a particular intervention
goes to phase, you know, out to market.
It could be that the difference is really
small or not
large enough to say we should therefore change
practice.
So but generally it's because the trial, the,
the question is answered, is it more effective?
And the answer is no.