So, welcome to the third
session of ethical and Regulatory
Aspects of clinical Research.
I'm Holly.
You might recognize me from the first two sessions.
Today I'm going to start with a relatively brief lecture on
study design.
And then Dave Wendler from our department
is going to join us in person
and then see who's at, northwestern is going to join
us.
By zoom, you'll recognize my disclaimer.
The bottom line, here is that good study design
is key to conducting ethical research.
This is from. Oops.
Yeah.
Document from now almost, you know, quite a long
time ago.
But it remains true.
Researchers us have a fundamental obligation
to plan, design and conduct studies
with honesty, truthfulness and integrity
values demonstrated by how researchers
observe, record and interpret their work.
So I'm going to, spend a little time
on the first two of these circled
ethical requirements today.
We already covered independent review,
but that's a sort of key component,
of the review of the study design.
So I'm just going to give us, some basics.
So we're really all on the same page.
So there are basically two types of research.
And this is a little oversimplifying,
but you'll you'll see
why I'm making these distinctions.
So there's observational research which is
merely though importantly descriptive right.
You are observing something and collecting data.
You can do that retrospectively.
Looking backwards or prospectively looking
forwards.
Retrospectively, it may be that you're
going to go and look at medical charts
and abstract information from the past
five years of medical charts, for example.
Experimental research includes
some sort of intervention, and it's almost always
perspective.
It would be pretty hard to do a retrospective
interventional trial
now with in that experimental design.
There are a couple of things that often happen.
First is there's some sort of manipulate
changing artificially, right.
We're not just observing or manipulating
at least one variable.
Sometimes there is control rule,
and you do that in order to reduce the
likelihood
that external factors will influence the study
outcome.
And then randomization to hopefully ensure
that you have an unbiased selection
among those who get the intervention
and those who don't.
Now there's a sometimes it's impossible
to randomize, right?
You may want to compare two interventions, but
one intervention, requires
an injection, the other requires a pill.
You can't really. I'm sorry.
You can't.
Really randomize in a way that is meaningful to the person
because they're going to know which one they get.
You could randomize the, assignment,
but you can't blind the individual
to what they're getting.
And so a quasi experimental design
is when there's manipulation
and some sort of control, but no randomization.
So the first question we're
going to ask about any study is whether or not it has social
value.
And Dave Wendler, who's going to follow
me is going to talk a bit about social value.
And so I'm not going to say much about it now.
But for the sake of argument, we're going to say
that a project shouldn't go forward
unless it has social value, right?
That there's some benefit, direct benefit
to patients or some value
to future patients for gaining knowledge
that will lead you to benefit
for future patients and or for, individual patients.
And then scientific validity is a component of this.
And an important question from the study design
perspective,
is this study design valid and,
easy way to think about this concept
is to think about sample size, right?
When you're designing
your study, you need to have enough
participants
in order to answer your scientific
question.
If you don't have enough, you can't answer your question.
And you have, let's say you you enroll 100, you really need
200.
You have wasted the resources
that it took you to do that half of the trial.
And you're not going to get, an answer.
So that would be a bad study design
because you have the sample
size is too small or it's too large.
Right.
Let's imagine that you only need 100 people to be exposed
to the intervention or intervention and control.
But you and you know, you've enrolled 200.
That means you've exposed 100 people to more risk
than they needed to be, and also wasted some resources.
Now, an important
question about study design and scientific validity is
is there uncertainty about the potential
benefit of the proposed intervention?
If there is no uncertainty, then we can't go forward, right?
If we already know the answer, we can't ethically
or, you know, scientifically move forward.
If yes, if there is uncertainty, then
a randomized control trial might be acceptable.
And a follow up question would be
is randomization to a placebo acceptable?
So when we talk about a randomized control
trial
there is the there is uncertainty right.
There's an indifference
among a community of clinicians about which treatment is
superior.
This is often referred to as equipoise
in the literature.
But it really means indifference
among usually a community of experts. Right.
If you're randomly if you're if you're doing a study about
diabetes,
it would be really important for those physicians
who are experts in diabetes or providing care and diabetes.
There's indifference among them, not among all
clinicians.
For example,
as I mentioned
before, there's this concept of blinding
where there's
you may want to blind the individuals
from their assignment.
You may also want to, blind the investigators
so you can do a single blind or a double blind
where the investigator and the subjects are blinded.
So what about whether
we can use a placebo or an active control?
So, just as a reminder,
a placebo, right, is generally.
Let's take the classic example of a pill.
It looks it's a, it's a pill that looks
just like the drug but doesn't contain any
medicine.
And, you know, we do this and it's important that we share
this
with potential participants.
We do this in order to find out whether the real medicine,
in this case, the drug works like we hope it
to.
And we need to compare people who get the medicine
to those who don't to see if the medicine actually works.
And so the next question is when is it ethically appropriate
to include a placebo?
And the bottom line is that, or the most common reason
to use a placebo is when there is no good
standard treatment, right?
So imagine it's 1988.
We know that HIV causes Aids.
But we have no treatment.
So the first trials of AZT, the first antiretroviral that
was
tested, was tested in a trial where half of the participants
got a placebo, the other half got AZT,
and that was ethically appropriate
or ethically, acceptable
because there was no good standard treatment.
When a standard treatment exists,
there is an expectation
that the study will be have an active control, meaning
that
the old medicine is going to be tested
against the new medicine.
There are a couple of other,
examples that when a standard treatment
exist,
it is ethically acceptable to use a placebo.
So one is maybe obvious when the placebos are additive,
right.
So there's a standard of care it's
acceptable
to randomize individuals to the standard of care.
Plus the new thing and standard of care.
Right. It's it's merely an a placebo.
It's merely additive.
Another example when the medical question,
medical condition in question is not serious.
So, let's say a drug company wants to develop a new pain
reliever,
and they want to enroll people who are likely to have a
headache
within the next month.
Just making this up.
You know, for we're talking about
a regular old headache, not a migraine.
It would be acceptable to ask people
to be willing to not take a pain reliever
for that period of time.
So it would be okay to randomize.
And then another example is when being off the standard
medication
for a length of time won't cause serious,
irreversible harm.
So, this is.
You have to be careful, right?
Because there are some medications
that people take that
if you take them off of them, they could experience
irreversible harm.
But if we're talking about a medication
that won't result in that,
it might be okay to ask participants
to stop taking the drug that they're
taking, be randomized to a new drug
versus a placebo and be followed over
time.
But this right, this will all depend
on the context, the justifications, etc..
Oh, so this is another interesting thing.
Piece of placebo, right?
Why not just have a no intervention arm.
Right. So one example.
So let's say you're trying a new, treatment.
Why not just have people
get no intervention and the intervention
and meaning that half the people
would get the pain patch, the other half
wouldn't get the pain patch.
So the concern here is that if you just give half the people
the pain patch and the other group
nothing.
When the outcome can be subjective,
like pain or emotional or endurance
or something like that, the individuals
who get the pain patch might
report information in a way
that is biased by the fact that they have
the pain patch on, or vice versa.
So you'd want to have individuals
have a pain patch that has an active
ingredient, and a pain patch, or a patch
that doesn't have the active ingredient.
Another reason why you,
you want to have something in terms of,
a comparison is when risk behaviors
might be affected by thinking one receive
the experimental treatment so we can think back to Kovit,
for example, that if you were in a, in a
trial and,
you knew that because you got a shot,
you got the vaccine, if you didn't
get the shot, you didn't get the vaccine.
One possibility is that if you didn't get the
shot,
you would be hyper vigilant about avoiding Covid.
And when comparing you to those who got the vaccine,
we might not be able to tell the difference.
So it would be important for everybody to get the shot.
So I want to transition a little and bring your attention to a
document
that was just published last month,
the World Health Organization.
Published guidance for best practices in clinical
trials.
I have the link for those of you who are
actively engaged in or interested in clinical
trials.
This is a really nice document in hitting
some of the sort of most important
key aspects of thinking about clinical research and or
clinical trials.
And I've included this little, diagram
because they're also talking
about the research ecosystem and the important parts of that
system
that support all the research that's being done.
So I recommend that those of you who are interested, take a look
at that.
And I just pulled this one,
piece of the
puzzle out of the document where they talk about
the key scientific and ethical considerations for clinical
trials.
And you won't be surprised
by any of these, pronouncements that it should be designed to
produce
scientifically sound answers, respects
the rights and well-being of participants.
And then I, I think these three are sort of,
not new, but I like that they put these
in the list
that the research needs to be collaborative and
transparent,
be designed to be feasible for their context,
and manage trial quality effectively and efficiently.
So the last three are ethical,
but also important practically
when thinking about design.
And then, another article that I just wanted
to bring your attention to or another
set of principles is, again, things
you wouldn't be surprised to hear,
but they add the last item here.
Study findings must be reported completely
and promptly during the trial and after the trial.
And I would say in the last ten years
or so, there's been more, maybe even 20.
There's been more emphasis on making sure that study
findings go back to the study population
that participated.
So back in the day, the expectation was you did your
research,
you did your clinical trials, and then you published it and
that's it.
You have conveyed your findings.
You've conveyed them clearly in this case,
right, to the scientific community. But
maybe you also have an obligation
to share those findings with those who participated in
the trial.
And it doesn't mean you give them like here are your
findings,
but provide them with the aggregate
findings.
The primary findings in and lay language
in a way that they can understand
that it's important for
individuals to appreciate that
their contribution led to an outcome.
And I would say that's true whether it's positive or
negative,
that their contribution
led to something that will advance
science again, regardless of whether it's positive or
negative.
In terms of whether they answered,
we're able to answer the question
they set out to.
So a couple of more comments about really about terminology.
When we think about trials and you,
I'm sure that we have all mentioned these,
in the first couple of sessions.
But just want to clarify. So there are
again,
probably I think it was in the 1970s,
the FDA
formally defined trials
as phase one, phase two, phase three, and phase
four.
And this was sort of, I know, doctor letter presented,
you know,
sort of the early history of the FDA
and how they came to develop
even guidelines about how you can prepare
a particular,
solution, how it can be labeled,
whether you've found it to be safe,
whether you found it to be effective.
In all of that momentum, there was also, an interest
in defining these trials, specifically
as investigators, pharma companies, etc.
moved forward in their trial development.
It was important for there to be, sort
of expectation of how things move forward.
So when we talk about phase one studies,
those are the ones that are first
in human right.
There's in most cases there's some sort of animal
research
and you now are going to see what happens
when you put it into a human.
And generally,
we're going to talk
a little more in detail, but generally those are for
safety, right?
Is it okay for a human to receive it?
And a relatively small number of people
from those 100 trials, about 70
go to phase two about, you know, a third of those go
to phase three and 6 or 7, make it to phase four or to
market.
So again, just to give you a sense
of how much research happens
at one end to get relatively few
effective outcomes in the end.
And, just again, to give you a little context,
the NIH Clinical Center is unique
in the sense that 95% of the trials conducted
here
are early phase trials, phase one and phase two.
That's not the
case in most academic medical centers.
Just to say that we are at the sort of exploratory
end of the pipeline
in 2023, which is the most recent data
that I could find.
You know, almost half were phase two,
a little less than that.
Phase one very few phase three.
Phase three would be a large
comparative trial.
So let's talk a little more about each one.
As I said, the phase one is usually
a relatively small population.
First in human safety, sometimes
dosage for the maximum tolerated dose,
meaning that
you give the individuals in the trial
perhaps
larger and larger doses in order to find
the one that is too much and back off one
and you use that dose to go into phase two.
So it's one lower than that maximum tolerated dose.
In phase two,
you start to look at the biologic effect
and look at adverse
events in in phase one they can be relatively short.
Phase two, they might start to get
a little longer in order to document
some possible biologic effects in adverse
events.
Phase three we're now getting larger up
to, you know, more than 3000.
As you can recall from the Covid trials,
tens of thousands of people in some cases,
the intervention is the intervention
that's going to be moved forward is based on the results in phase
two.
And this is where you start looking at
effectiveness, continue to look at safety
and then risk benefit in terms of adopting
into clinical practice.
Phase four are
what are often referred to as post-marketing
trials.
And in those we're looking at long term safety.
Not every
intervention goes into a post-marketing
trial.
In those cases where there is maybe
a higher expectation of risk or serious
events,
there's an interest in following the long term
safety.
So in terms of oversight, we spent, a whole lecture
on institutional review boards.
Those are the generally local groups
affiliated with the institution that the investigator is at,
and they're doing the initial review
of their research
to see whether or not it is appropriate to move
forward.
Another thing that is often in place is a data monitoring
committee.
So in general, a data monitoring committee there,
there are at least two primary types.
One type of data monitoring committee
is assigned to a particular study.
So again let's use Covid as an example.
The Covid vaccine trials had
each one had a data monitoring committee.
The other type is a data monitoring committee
that is a hub for a range of trials
or a group of trials or,
you know,
the trials don't necessarily have to be related,
but they're all looking at a particular
condition and interventions.
For example, I sat on the Nhlbi National Heart, lung and Blood
Institute
Pediatric Heart Network, Dsm-v, or DMC,
and we reviewed all the trials
that were conducted under that umbrella.
So the the goal
or the role of the data monitoring committee
is to monitor the trial once it's in the
field.
So if you remember, the IRB
does their review and approves or not the protocol,
they generally don't have much to do
with the trial.
Once it's launched, the investigators
are asked to provide an annual review,
but the IRB is not looking
at the data coming out from the trial,
except for adverse events, which they may be reported to.
But the data monitoring committee
picks up once the trial, starts.
And one really important, component of that monitoring
is to protect subjects from previously unknown adverse
events.
So imagine there's been some animal
studies.
There's been some phase one studies.
There's been some phase two studies.
Not a lot of time has passed.
So maybe there's going to be an adverse
event that comes up a year
into the trial that it would be really important to, to measure.
So for example, with the Covid vaccine trials,
there was this unlikely but potential adverse
event that those so
this is known from other vaccine products
that if you get the vaccine and are then exposed to the
disease,
you might have an even worse response to the virus.
Again, highly unlikely.
It wasn't expected, nor did it happen in the Covid vaccine
trials.
But one of the jobs of the data monitoring committees
was to monitor the data to make sure that
didn't happen.
Now, if that happens,
it's their job to decide whether or not
that signal means that the study has to stop.
So one reason it might have to stop
is because the adverse event is so severe
and so unanticipated that
you need to stop the trial for a while
and look into what happened.
Maybe it had nothing to do with the intervention
that the person received, but you don't
want to move forward with the trial.
If there's a possibility that others
who are receiving
the treatment are going to have that same adverse event.
So they're monitoring for adverse events,
they're also looking at how is the trial doing.
So again, let's imagine you're doing your trial.
And you need in order to answer your question,
a sample of 200 in order to find out
whether the new treatment is better than the old
treatment.
And you're planning to do that over a year.
The data monitoring committee is also looking at your
progress.
So if halfway through,
you only have 50 participants and your anticipated enrollment
was 100,
the data monitoring committee might say, okay, what's going
on?
Why aren't you at where you want to be?
And what are you doing to address that?
Because it's important for a trial to move forward,
and it's funded for a certain period.
That's important for the data monitoring committee to be
aware.
We've got what happens if we can't fully enroll the
trial.
So they might say, tell us what you're doing to increase
enrollment.
So they might add a new site, or they might,
develop relationships with local providers or
something.
But it's, again, just a way to remind the
investigators
from an independent external body
how things are going.
And I should have made that more clear
at the beginning.
The data monitoring committee is made up
of individuals who are independent
from the study, generally independent
from the PiS local institution.
If, for example,
let's say you have a data
monitoring committee and you add a new site, and that
site
PII is, you know, works with the head of the DMC,
the the chair might have to recuse him
or herself,
but it's independent in the sense
that they don't have any
expectation,
that they're decisions,
are somehow influenced by outside,
concerns or bias.
So the data monitoring committee also has
what are referred to as in the end,
stop being rules or interim analysis.
The last so
one thing that you want to make sure is true
is that you are not exposing subjects
to something that is overly risky,
or withholding something of benefit
from a group who could benefit so
interim analysis is the data
monitoring committee receives data
that is sometimes blinded in the sense
that it doesn't say the name of the drug A
and the name of the drug B.
It literally says A versus B, and they're looking to see
whether or not
it looks like the new drug drug B or sorry,
one of the drugs is doing much better
than the other.
And in terms of an interim analysis,
it looks like it's
statistically doing better than the A in this
case.
And so they could decide, according to the interim
analysis
and the stopping rules that we have
a statistically significant difference
at this point earlier that we thought
and we think the trials should be stopped
and the intervention is unblinded.
Now when you unblind them, it could be either that people
getting
the new drug are better or the people
getting the old drug are better.
You don't know, but you know that something's
going on
and there's a statistically significant difference.
And so that would be an
example of shortening a trial
because it's doing even better than you imagined.
And in this case, it could be that it's the new or the old.
But it's a reason to stop
because withholding the new,
the treatment B puts the participants
in a worse off position
and not giving the B to those who are getting A,
you know, they should all now get the
intervention.
And the other thing to think about is that
bad things could happen as well.
So stopping rules are in place.
Sometimes stopping rules refer to like
we know more than one death on the end,
you know.
So stopping rules are laid out in advance
that say,
if this happens, we are going to stop the trial
and you may reopen the trial
or stop and close the trial. So,
that's all to say that
especially for phase three trials
and sometimes trials
that are sort of high profile or there's
a, concerns about severe adverse events.
Phase two studies have data monitoring committees
in order to make sure that the data
is coming out
in a reasonable, timely fashion and that nothing especially bad
or especially good is happening in order to stop it as needed.
So, with that, I am going to just remind you
that good study design is key to conduct of an ethical study.