Good morning.
I'm going to try to do two things
in this talk, which I hope don't come across as
contradictory.
If they do, please let me know afterwards
and I'll try not to do it.
Or I'll try to avoid the apparent contradiction in
the future.
So the two things first are.
To argue that doing risk benefit
assessment is obviously just central
to the ethics of clinical research,
but it's not done very often.
It's not done systematically.
I don't think it's done very well.
And so the first thing I want to do
is just
present a framework for doing systematic
assessments of research risks,
and then make the argument that it's important to do this
systematically and come.
This is anecdotal, but people I can just I'm
convinced now
that's how I measure where I got here.
So I've been doing this for 30 years.
People just aren't doing this and
they're not doing it in a systematic way.
So that's the first part of it.
Sorry, we're not doing systematically.
We need to do it systematically.
So for people just care about the basics
of this, that framework is enough.
You can just not pay attention to the rest of the talk.
What the second part of the talk is going to be, which I hope
doesn't
seem in tension with the first part,
is to think about
some of the different challenges complexities
that arise when you start thinking about
how to do risk benefit assessment.
Right?
So I think the basics are pretty straightforward,
but I think there are also a lot of really interesting
questions
that arise once you start thinking about this
carefully.
And these are questions a lot of which haven't been
addressed.
So people now know the Belmont Reports of
Belmont
Report is one of, if not the sort of founding
document
for research ethics, particularly research
ethics, in the United States.
And here are some things that they have to say with
respect
to risk benefit assessment.
They say things like, we should try to do it as
systematically
as we can, as clearly as we can.
I like this last one, which is just never done.
Is that you shouldn't just do it based on intuition,
but risk benefit assessments, they're empirical questions,
right?
They're questions of if I take a certain drug,
what are the chances it's going to help me?
What are the chances it's going to hurt me?
Well, those are very empirical questions.
And presumably in order to address them, it's
valuable to have as much of the relevant
data as you can.
Belmont to
emphasize that 50 years ago now, and basically nobody does it.
And so one of the things we've been trying to argue is
trying
to convince people of the value and the importance of doing
that.
So I'm just going to give.
So when I give talks, I usually say I get to tangents,
but what I do, what I realize now is
if I put some of them on the slides,
they're not actually tangents. And then I get more.
So I'm sneaking in more tangents here.
So this is to leading into this talk.
So this is a paper you can see we did 20 years ago
now.
And CMA was actually a post back fellow here.
Her first. She came through the department
three times.
This was her first time.
And this was a survey of IRB chairpersons
who had worked in pediatric research,
reviewed pediatric research studies.
We gave them a list of common research interventions, like a
blood draw
like an allergy, skin testing like a bone marrow
biopsy.
And we just asked them, how risky are
these?
Are they minimal risk?
Are they a minor increase or a mask?
Are they more than a minor increase
over a minimal risk?
And when you hear this talk,
you'll learn more about those categories
and why they're the relevant ones.
But that's the assessment that we did.
And the data that everybody focuses on
from that paper are two things.
One, that IRB chairs tend to be really cautious.
There is a big chunk of them who said that even a single
blood
draw was more than minimum risk, which it just isn't.
That was the first thing.
The second thing was that
there was this dispersal, just an amazing amount of
disagreement.
So for instance, allergy skin testing,
almost a third thought it was minimal
risk, almost a third
thought it was a minor increase over amount of risk, and almost
a third
thought it was more than a minor increase
over a minimal risk.
So why are we getting that much dispersion?
Well, the other aspect of this study,
which we couldn't publish
because we didn't have tests of it,
but I thought was especially interesting.
These were interviews.
We called up 188 IRB chairpersons.
We gave them this list of interventions.
And we said, tell us, is this minimal
risk, minor increase our risk
or more than my increase heart intervals.
So you multiply 188 times 8 or 9,
whatever the heck that turns out to be.
Right?
It's almost 50, 100, 2000 specific cases
in which you're asking somebody to categorize the risks
of a procedure intervention, and not, in one case, zero
cases.
Then any of the chairperson say, I don't know.
And that was really striking
because a lot of these things they didn't know about,
they didn't have data on, and they should have said, I
don't know,
because I can't do what Belmont tells me to do.
I can't do this systematic assessment
of a rail of the relevant data
because I don't have it and I don't know it.
I'd have to look it up and get back to you.
Nobody did that.
So what's going on?
I think what people are doing is and part of the problem is, and
this is
my previous career was spent studying evolutionary biology
and I think one of the things we learned
from evolution is that the reason
why we're here right now
is because our predecessors were very
pretty good assessors of risks,
and they made those assessments really, really fast.
So you imagine
two populations
saber tooth, their sounds easily.
It could be a saber tooth tiger.
The one, the one group of our ancestors
were very thoughtful about risks.
And they started discussing it and thinking, could this be a
saber
tooth tiger?
I don't know, maybe we should think about it for a
while.
Maybe we should investigate it.
The other group ran away when they heard the sound.
Well, guess which one survived and reproduced the second one,
right.
And so what we've got is we've inherited this response
where we just make risks assessments
really fast.
That can be valuable in certain cases,
but in other cases it leads us astray,
particularly in research where we're often
dealing with unfamiliar things,
where the heuristics that we rely on
aren't necessarily applicable.
So that's IRB chairpersons.
We're doing this just give you one other example.
I started doing this for a long time.
So people sometimes call me up and say,
could you help us decide whether
this procedure, that procedure is of
minimal risk or greater than minimal risk?
These are some investigators
who study
metabolism, and they often do IVF glucose
tolerance tests.
And the head of the national consortium
called me up once and said,
Dave, you got to help us.
All these IRBs think that IV glucose
tolerance test is more than minimal risk.
And you have to help us convince them
that they're actually minimal risk
because we're confident that they are minimal risk.
And I say, great.
So when you submit these protocols
arguing that you glucose tolerance
tests are minimum risk, what data do you present to the
IRB
to convince them that these are minimal risk?
You know what the guy said to me?
What are you talking about?
Why would we present data?
This is like a nationally renowned
clinical researcher who spends his career collecting
data.
But then when it comes to ethics, he's
like, what's data got to do with it, Dave?
They're just minimal risk.
So nobody's doing this.
IRBs aren't doing it. Investigators aren't doing this.
And I should point out, you can notice that paper
30 years after
Belmont, this example, almost 45 years
after Belmont, it's been a long time
and people still aren't doing it.
So that's the set up for trying to justify
or give grounds for why we need a framework.
And then I'm going to propose one and talk through it some.
So just a really background.
So this is just for the philosophers
in the crowd.
Don't get too worked up about this.
So I'm gonna talk about things like risks and benefits.
And then people get confused.
These are the terms that the regulations use.
But then people will say, well, a risk is a chance of a
harm.
So if I'm going to stick a kid with a needle, that's going to
hurt,
that must not count as a risk, right?
Because risks are some probability
of a harm where it's basically guaranteed.
If I stick a kid with a needle, it's
going to hurt.
So that must not be a risk. That must be something else.
Now, these are actually just terms of our risks.
Apply to things where you're not sure
if a bad thing is going to happen,
and it's also where you're sure some bad things are going to
happen.
And benefits aren't just things that are good that definitely
will happen.
It's also potential benefits, speculative benefits, chances of
benefit.
So think about these terms very broadly okay.
So here's the framework that I'm going to go through.
And we're talking about this talk about our wonderful alums.
The first author.
And this was Annette a read who was
a fellow post-doc fellow at the time.
Right. So she's now a member of the
department.
So what I'm going to do is basically just go through
each of the steps in this framework, describe
basically how I think it works or should work.
And then as I mentioned at the top,
I'm also going to
go into some of the more complexities
that get raised here.
I think try to chemistry.
I think they're still really interesting
questions
to try to address here for anybody
interested in doing research ethics.
Okay.
So to start, one thing to notice is that
basically every clinical trial
is a composite or an aggregate of lots
of different things.
You bring people in, you weigh them, you
measure them, you ask them some questions.
You take two blood draws, you give them a drug, you do an
MRI scan.
So there's all these different components
that make up a given study.
And so the point of this then when it comes to risk
benefit assessment is well what are you doing.
The risk benefit assessment on if there's all these different
components,
the answer is what you should do is you do first.
So if we're talking from the perspective of an IRB,
first
they should look at the risk benefit of each individual
component.
Assess each one of those,
make sure they're acceptable from a risk benefit perspective.
And then look at the aggregate or the study as a whole.
So do both a component and the study as a whole.
Now not only there are lots
of interventions, but they're often
different kinds of interventions.
A lot of studies will include clinical
interventions and research interventions.
So this is what standard they call an add on trial
where you give everybody a standard of care.
And then for the one arm you add something experimental on
top to see if that improves standard of care.
And so for the most part,
when I IRBs are doing this component
analysis, they should be evaluating
the components that are research,
not the components that are clinical care.
And this is a statement
basically directing our base to do that from the U.S.
regulations.
So what they should be looking at
is the risks and benefits of the research.
And this is distinguished from things,
for instance, therapies
that subjects would get even if they weren't in the
research.
So the thought here is why do you do that?
Well, the thought here is if it's clinically indicated,
then the risk benefit profile of that
intervention is going to be acceptable.
That's what it means essentially for it
to be clinically indicated.
So for the most part, IRBs don't have
to worry about the clinical interventions.
The caveat here.
So the arrow is going to be different
caveats.
If we go through this talk here, the worry is that
what I at least need to be worried about,
though, is the possibility
that that add on treatment in the one arm of the study
that that might influence the risk
benefit profile of the standard of care,
it might make it more risky, it might make it less
beneficial,
it might make it more beneficial.
Who knows.
But so you can't completely break it off
the clinical interventions.
You have to think about if there are any interactions
between the research interventions
and the clinical interventions okay.
So now here's the framework.
So first step is that.
We have
this is the stick for the department
clinical research.
Is it ethically acceptable unless it's socially valuable.
So the way you apply that here is
you first make sure that for each of the
interventions, the research interventions
that there's a socially valuable reason.
Why is that intervention in the study.
So you ask the investigator you have three MRI's in this
study.
Why are you doing three MRI?
What are you going to learn from?
For instance, the third MRI that you couldn't learn from the
second MRI.
And you want a good explanation for that.
What are the things I'm going to emphasize
is that
I sit on IRBs
when I talk to people in IRBs, I talk about how important
this stuff actually is in practice.
I rattle on about this in IRB meetings
and then what the IRB on actually says,
okay, here's the protocol, Dave.
You tell us whether the risks and
the benefits of this study are acceptable.
And my answer is almost always, I have absolutely no idea,
because to make these assessments
like whether or not you really need
the third MRI, whether testing this new kind of
treatment
for leukemia, whether those things
are socially valuable,
being able to make those judgments,
you have to know a lot.
You have to know a lot of stuff
that I just don't know about, for instance, leukemia.
So whenever you do this, you're really need people
who have content expertise to make these judgments.
You can't you can rely on philosophers
for the framework,
but you can't rely on philosophers
to implement the framework.
So why don't you do the individual interventions
that,
as I mentioned, you put them all together
as the package that they are
and you evaluate the overall study
to see if it has social value.
Typically, people will point out
you hope to gather important data
from every given trial.
But presumably,
or at least it's rare that any given trial
is going to give you definitive data.
Typically, you have to combine the data
from one trial with data that you collect
from other trials.
And again, this requires a lot of expertise
okay.
So let's just talk about some of the challenges at this
step.
So one of the things that I think
is always interesting
and my experience of being on IRB
for 25 years is IRBs differ on the answer
to this question, which is imagine that the
investigator
submits a protocol to you.
So that's how I IRBs work.
The investigator submits a protocol,
the IRB reviews it.
So here's a question.
What's the job of the IRB?
It's the job of the IRB.
Just to look at that study that got submitted to them,
and just see whether or not that study
is ethically appropriate,
or should somebody do something like,
yeah, you want to do X with this patient population.
But here's this other thing
you could do with the same patient
population that would be more valuable,
that would be more interesting, that would give us more valuable
data.
Is it the job of the IRB or somebody else
to make these comparative judgments
across studies, right to this study,
rather than that study
that at least I've sat on almost never do?
Maybe they should be doing it or somebody should be doing it.
And the other one is just comparative
judgments within a trial.
Well, you designed it with just two MRI's.
But if you added this third MRI, look at all this other
information
you could get that would make your trial
even more valuable.
What should it be do in that case?
They think that the study
that was proposed is okay, but here's a much better study.
Should they say you then do the advisory?
You can't do this one because you could do this better
one
or does it just have to meet some sufficiency standard?
And then there's the question as you go
through this about who should make
these judgments.
We could talk about enhancements.
So this Annette and I used to have
this debate when we wrote that paper.
So typically most clinical research
focuses on diseases and illnesses
like cancers and things like that.
But what about
trials to do what people call
enhancements,
which is not to treat an illness or a disease,
but to make people who are healthy
even better off, to make them happier,
to make them healthier, to make them live
longer, is that is equally valuable
as doing something like treating cancer?
And again, should people be making those
assessments,
and if so, who should be making them?
So this is just some suggestions.
When I gave that previous slide, I get a lot of pushback from IRB
folks like that.
You shouldn't be making these comparative
judgments.
That's not what I IRB are supposed to be doing.
So what I started doing
is trying to collect at least some evidence to
suggest this.
In some cases, I think IRBs should be doing
that.
And these are cases now, interestingly,
they're from pandemics or epidemics.
So the first one was in West Africa
during a Ebola
where the problem was there's only so many patients, and all
of a sudden
you've got all of this money and resources
going into the area to study Ebola.
And what these guys point was, is that
there's a limited number of patients
for all the studies that we want to do.
And so if we really want to get the most social value
out of this situation, we're going to need
to prioritize which of the studies
we allow act to access these patients
and which ones we don't.
And then a similar thing this is arguments.
More recently during Covid where there's arguments early in the
pandemic,
we wanted to learn as much as we could
about Covid, at least initially.
Obviously, in the long run there was lots and lots of
patients,
but initially there weren't that many.
And who should be?
Should somebody be prioritizing and deciding which studies to do
and which studies not to do these?
This group argue that at least within
institutions.
So, for instance, the NIH Clinical Center
should be prioritizing.
Johns Hopkins should be prioritizing,
at least within institutions.
There should be some prioritization.
Okay. So that's potential benefits.
Social value.
So the next thing to look at is the risks.
So the idea is to identify the risks
of each of the individual interventions
and then try to reduce them to the extent
that you can.
Now I'll come back to this in a second.
One of the things that everybody agrees
at this step is that you should
look at all of the risks in a very,
very broad way.
So obviously that means health risks, like
is this drug potentially going to knock out
my kidneys,
and I'm going to have to be on dialysis
for the rest of my life.
That's obviously a risk of being in a clinical
trial.
But when people emphasize there's other sorts of risks,
there's,
for instance, economic risks.
What if knocking out my kidneys, I have a copay of $1,000,
and then I'm going to have to start paying
thousands of dollars out of pocket
to get that kidney problem fixed,
or at least treated.
What about the psychological harms
of being on dialysis
and how that makes me feel?
What about stigma if I'm on dialysis
and how I'm stigmatized?
The argument here with respect to risks
is that all of those should be considered.
I'm going to come back to that then
when we talk about potential benefits,
I mean, it seems right, right.
People care about more than just their
bodies.
They care about their bodies, but they care about their bank
accounts.
Some people care about their bank
accounts.
More than they do their bodies,
so that we should be worried about all the different types of
risks.
So there's a couple challenges with this.
And the basic idea is that what this step says is
that
in order to decide whether a study is acceptable
before it can begin, the IRB has to
make sure that the risks are acceptable.
It's obviously a conundrum.
There is that that's typically the point
of clinical trials, right?
At least early phase clinical trials are designed to
figure out
what the risks are of a given intervention are.
So if the goal of the trial is to find out what the risks
are,
how can you prospectively evaluate
whether those risks are acceptable?
It seems like you got to do the trial
before you can decide
whether the trial is ethical to do.
And so in this, basically what IRBs need to do is they
need
to be creative, they need to expand
beyond the given intervention.
If there's no data on it. Sometimes there is.
Sometimes this intervention has been used before in other
populations.
See how you can extrapolate to this population.
Sometimes this specific drug hasn't been given
to this population, but drugs of a similar
same class have been given.
And you can try to extrapolate from those data.
Or maybe it's just the mechanism of action
between this drug and other drugs.
So basically they're trying to look
for relevantly similar cases for which there are data
and then seeing how they can apply them
to this one, with the thought
that the further you're extrapolating,
the greater the uncertainty.
And then you should factor that uncertainty into the risk
assessment
with a thought that the more uncertain you are,
the riskier the intervention or the study
is.
So here's another challenge.
The first one was that you got to know
something about the intervention here.
The point is that you don't have to know
about the intervention.
You have to know who the intervention
is going to be put into.
Right.
So the risks of a certain drug can vary
dramatically depending upon
who's going to receive it.
But you don't know who's going to receive it until the trial
starts.
So the example I have here is drugs
that are cleared by the kidneys.
They might be very safe for people,
have good kidney function
who can excrete the drug properly,
but they might be really dangerous.
The drug might build up in the systems of
people who have impaired kidney function.
So another thing that I need to be aware
about is the extent to which risks
and potential benefits depend upon who enrolls in the
study.
And so the way IRBs try to handle this is with
inclusion exclusion criteria.
You try to figure out who's going to face
excessive risks and then you try to exclude
them.
The other thing I mention here is the
importance.
Sometimes you just don't know how a given
drug is going to affect people.
And so there's this importance of monitoring the trial as you
go along.
And this goes back to Holly's talk.
If you start monitoring it in the third
or fourth participant,
you start seeing a trend of significant
harms.
Then you need to stop the study and reevaluate and see whether
or not it's
okay to keep going from.
All right, so this is going to be
even more into the weeds.
So this is the end.
Another I did this one yesterday just
to make sure I really drive you guys nuts.
And so for the most part when I've been thinking about
risks,
I've been talking about risks to the individuals who are in
the study.
And that's the standard risk benefit assessment.
And clinical research is done.
More recently, people have started
to worry about the risks
to what are just generally called
third parties.
So here's an obvious example.
Challenge trials are trials where you take an
infectious agent,
like a virus, and you inject it into a couple of research
participants
and you see what happens.
Maybe you're going to test different treatments,
or are you going to try to study the natural history of the
disorder?
So obviously, injecting viruses into the participants poses
risks to them.
That's what we've been talking about.
But the point here is that that can also pose risks to
other people.
If you're infecting the participants
and then the participants work
with the nurses, the participants go on
weekend passes, the participants go home.
They could transmit that infection
to lots of other people.
So what do you think?
So that seems, that may I won't force people to
think,
but that seems like I should take that into account.
Right? Does everybody agree?
Yeah, of course you should take that into
account.
So it seems like you should take risks
to third parties into account.
But now the question is, how broad
do you think that principle should be.
So these are actually some real examples.
And we did an ethics grand rounds on this
about ten years ago where
there was a participant
who was a perfect candidate for the study,
and the study offered the participant
the chance, a potential benefit.
But it turned out that the participant
was the caretaker for two people
who were really sick, and the participant
could leave those people behind.
So what the potential participant
was going to do was, was going to drive
from the west to Bethesda with those two people in the
car,
and the home physician was really worried
that that drive could be dangerous
to the other two people,
not the participant, but to the other two people.
Another example we just heard about
in a recent ethics grant rounds was,
participant who was undergoing bone
marrow transplant that required them
to stay in the clinical center here
for about three months.
It turned out they had little kids
who were very attached to them,
and people were worried that the participant
being away from these little kids for three
months
would be traumatic to their little kids
who couldn't
come here and had to stay at home
and be watched by the grandparents.
It turns out, in that case, as we heard the story, that it
did
turn out to be really they did come.
They participate in this study,
and it was really traumatic for their two little kids.
And this is a couple years later,
and apparently the kids are still
experiencing the effects of it.
So in the second bullet, we said, yeah,
of course
you should take into account risks
to third parties.
But now what do we do with these?
Do we say, well, we're gonna have
inclusion exclusion criteria.
If you have lots of little kids, you can't enroll in our trial.
If you have to drive more than 50 miles to get here.
You can't be at our trial.
Then we have to look at all this information on people
about how many kids they have
and where they live, and whether they fly or they
drive.
Not only does that seem intrusive,
but it starts to seem like
that's just too much.
RBC can't be doing that, right.
So I think this is a really interesting
challenge.
Both.
It seems like something we should be doing on the one
hand,
but then when you generalize
the principle, it seems like it's
something that's almost impossible to do.
And I have absolutely no idea what the answer to this
puzzle is.
And if anybody knows the answer, I'd be very interested
in hearing it afterwards or during a question answer
okay,
now a little bit of philosophy
or philosophy of science that is,
I think, practically relevant.
So I think it's worth at least thinking about a little
bit.
So we've been talking about risks
and benefits of an intervention,
like giving somebody a drug as if this a straightforward
figuring out.
Right.
Inject somebody with a drug.
What are the risks?
Well, it's the potential that bad things or harms
are going to be experienced as a result of that. Right.
That seems pretty easy to imagine.
What's important to notice, though
in some cases it doesn't matter.
It's implicit.
But in some cases it's really important,
which is that whenever we do
risk benefit assessment, we're appealing to some baseline
or some comparison against which we're making those
judgments.
So here's an example to try to show you that
there's a phase two study of a treatment
that has been shown safe.
So we know it's safe let's say,
and offers a small chance of helping participants
medically.
So they have a serious illness.
And there's a small chance this will help them.
Is that a potential benefit study.
So we think about the risks and benefits of that trial okay.
So it's supposed to be a trick question right?
Anybody want to take a shot and fall
into the trick or address the trick?
Or should I just click to the next slide.
It depends. Right.
So this is supposed to be what the comparison is.
It depends on what.
Well it depends on
what's going to happen to these people
if they don't enroll in my trial.
Right.
If outside of my trial
they wouldn't get anything, they would just sit at home and
watch TV.
Then arguably, being in the trial
is potentially beneficial for them
because there's this small chance
of benefit, a small chance, a benefit
is better than no chance of benefit
from watching TV.
So overall, the comparison suggests
potential benefit.
But in contrast, imagine there's some
drug out there that cures this disease.
You take it once and it cures you.
If the
choice is between taking that drug
and being in my trial, then it looks like
not only isn't the trial beneficial,
but it looks really risky, right?
Being in my trial is that nine people
the opportunity of being cured.
That seems like a very bad thing.
So now it looks like a really risky trial.
So the point here is that it depends on what you're comparing
to.
Okay. So that's important.
That's clear
now why this is practically relevant,
not just philosophically relevant.
Is this that
what the comparison is or what is available to people
otherwise varies dramatically in populations.
It varies dramatically in the U.S and
it varies dramatically across the world.
And so what that means in the end is that
whether a study is potentially beneficial
or risky is going to depend upon where you do it.
And if you do it in certain populations,
it might be potentially beneficial.
If you do in other populations, it might be really risky.
That's complicated.
And it also drives people absolutely nuts.
So to evaluate the risks of research,
it's important to know
if these people were in our trial,
what would happen to them instead,
and how does what we're proposing
to do to them in the trial compare
to what would happen to them instead of a trial
may be risky in some places, but potentially beneficial in
others.
So where does this get?
Is there going to be a session or an international?
Okay, so somebody's probably gonna talk about this becomes a
big deal,
especially when you talk about research
in international settings.
And this was a big deal in some HIV studies about 30
years ago.
They turn into huge fights where
still is true.
Not as much as it used to be, unfortunately, but 20 years, 15,
20 years ago, if you lived in certain places,
there were very, very good treatments
for HIV in other parts of the world
where there was lots of HIV, those treatments weren't
available.
So now imagine you have this idea of this
super cheap drug that's not as good
as the fancy ones you can get in Bethesda,
Maryland, but it's better than nothing.
Now, notice if you ran that trial in
Bethesda,
Maryland, it's going to be like the example I
gave before
where there's a chance of a cure outside,
not a cure, but a really good treatment.
Whereas if you do it in Bethesda,
it looks like a risky and maybe an unethical study.
But if you do it in sub-Saharan Africa,
where they don't have access
to those treatments, it looks like
this is potentially beneficial.
So that's what risk benefit suggests, right.
But now what you're saying is that a trial
this unethical
to do here because the risk is okay
to do in sub-Saharan Africa.
And that made people really nervous
to say that.
And some people thought that
that made them so nervous that it must be wrong somehow.
And we have to figure out how. And there were huge fights about
this.
So it sounds like maybe you guys will talk
more about this.
So the important point now here is just
the comparison
can have really practical implications
for some of these trials.
Okay.
There's an end of the philosophy for a
second.
So now we've identified the risks of the interventions.
We want to minimize them
I just I just do these because this is personal trauma
for me.
I still get very freaked out by blood draws.
And they used to I really had an incredibly hard
time
when I was a little kid.
I still do, actually.
And then
I got tons of blood draws in my life.
And then at one point, some nurse
says to me, look out the window.
I looked out the window, she did it.
And she and I said, okay, you're going to do it.
She says, no, we're done.
And it turns out for at least a lot of people
like me, it's not the physical sensation
that's bad.
It's the seeing this thing getting stuck
inside your body that's horrible.
And it just completely went away.
And so the point of this is just
we need people who know what they're doing,
and we need people to be creative
when we're doing this stuff.
This just shows a way in which,
in which we talked about, I don't people
know the jiggling of the, the cheek one.
My dentist taught me this one.
All right.
So so I know these are my tangent. Sorry.
If I'm late, if I go over to my phone,
holly holic, yell at me.
This one is really interesting.
It turns out that that nerve pathways. Right.
So so if you stick somebody down here,
the reason why it hurts, right?
They're all these neuro pathways that goes up into your brain
and your brain does some complicated things and and it
says ouch.
Right. Well,
these pathways come together at various points,
and they can't transmit more than one signal at a time.
And what's really interesting and evolutionarily seems weird
is that those are some of those nerve pathways prioritize
sort of dull, pounding sensations
over sharp, sticky ones.
And so what that does is, and what my dentist
used to do
when we did this was sticking that big needle into my cheek.
At the same time, what you do is jiggle my cheek,
and that sort of dull sensation would get prioritized by the
nerve
pathways over there getting stuck,
and I wouldn't feel that getting stuck.
It was just fantastic.
I think I was 40 before somebody before somebody did that to me.
So I was like, people should be doing this
more often, okay,
second point is that
sometimes the way you minimize risk is do
less tools, fewer blood draws, do fewer
MRI's.
That can reduce risk,
but it also can potentially undermine
the social value of the study.
So then that's the point.
We're going to have to judgment and figure out how to balance
these competing considerations.
The fairness issue is also this is one
that we've had huge debates in our department over years,
which is here's another way to minimize
the aggregate risks of a study,
exclude anybody who faces really high risks from
it.
If you do that, you're going to really minimize the aggregate
risk,
but at the same time, you're excluding people from
that study.
And if that study has a chance for a potential benefit,
then there's a way in which it's bad
for the people you're excluding.
And some people argue that that's unfair.
Okay. So here's some examples.
Experimental drug for serious conditions
with very few effective options.
This is this is a console we had a couple of years ago.
The drug poses a risk of significant
bleeding that requires transfusion.
So the investigator comes to us and says
I think this is a really important trial.
This is a really important drug to test,
but I'm terrified that I'm going
to kill some of my participants because of this risk of
bleeding.
And what I want to do is I want to exclude
anybody who faces a really high risk for
bleeding.
And so who is that?
That's people are bleeding disorders.
It's people have low platelets for whatever reason.
And interestingly, it's also people who declined
blood transfusions for religious reasons.
And so the question was if you want
to minimize the risks of this study,
what you should do is absolutely exclude
all of those people, right?
So is that the right thing to do or is that unfair,
particularly given serious condition,
no other treatments.
These are the kind of, I think, really important
debates
and questions that arise when we start
thinking about risk benefit assessment
and balancing risk benefit assessment
with other values like fairness okay.
So potential benefits.
So we've done social value. We've done risks.
Now we look at the potential benefits
of the interventions.
And again as with risk we look at the potential
benefits
above and beyond what people would get
if they were outside of the trial.
So now I want to come back to the point
I was harping on earlier.
Remember when I talked about financial risks,
psychological risk,
stigma, risks,
and we said you should count all of them?
Seems obvious. You should count all of them.
So here's the question is does the analogous point apply
to potential benefits?
Should you include not just the potential benefits
of clinically getting better, but the potential benefits
of other sorts of things that could happen to you?
So before we talked about economic risk,
if there's a chance that it's good
choice, had to knock out my kidneys
and I'm going to need treatment, I got to pay out of pocket for
that.
That's an economic risk of being in this study.
Well, now I'll take a different study that
offers everybody who participates $500.
Is that a benefit of being in the study?
Should that count as a benefit?
If there's economic risks over here,
are economic benefits over there.
And I'll just say that the almost consensus view
in the literature is, yeah, the economic risks
count.
If everybody's got to take those into
consideration.
The economic benefits don't they shouldn't
consider them or basically shouldn't
consider them at all.
And so obviously there's this question of
does that make sense?
Should we treat them differently,
and if so,
why should we treat them differently.
So I was just one of the other questions.
I'm going to leave us with for now.
So what's really identified?
The risks, identify the risks, minimize them, potential
benefits,
identify them and try to make them larger
to the extent you can.
And this puts us back into this worry about fairness again.
When I was on the infectious disease IRB,
they were just starting to test protease inhibitors,
which people might know turned out
to be like the initial blockbuster
for treatment of HIV.
It really revolutionized the treatment
of HIV, where HIV went from a disease
that basically killed everybody to a disease that people lived
with.
Now, we didn't know all that at the time,
but we know these drugs looked really good.
And the question was, who do we do
the early phase trials of protease
inhibitors in
do we do it in people who are relatively healthy,
with the assumption it will be less risk to them
because they're healthier, they're in better shape?
Or do we do it in people who are really, really sick,
who probably face greater risks from an
experimental drug?
But if it helps them, if it actually works,
they're going to gain a lot more than
the people who are relatively healthy.
So we actually debated that.
And it's interesting.
So I think about it now, I always say that
IRBs tend to be on the cautious side
where they want us to reduce risk,
but in this case, we didn't.
In this case,
we actually mandated that the studies
be done in people who were sick.
So we we did people who had low CD4
counts, I think under 50 to be to potentially do that trial.
So we focused on the potential benefit
rather than minimizing the risks.
Okay. So now we know what the risks are.
We know what the benefits are. We know what the social value
is.
So we're supposed to put it all together.
And here this step is just basically
to the potential benefits
to the participants plus the social value.
Put those together to those.
Justify the risks and the burdens
and the costs of doing the study.
If the answer is yes, then at least
from a risk benefit perspective, you got
you have an acceptable study.
You could basically stop doing a risk
benefit assessment at this point.
If no, then you need a couple more steps.
So I'll talk about those additional steps
in a minute.
The first thing is so people agonize over this all the time.
And this is a case.
So there's this there's this big debate
that always goes on in research ethics
about the way in which you should treat clinical
research
like clinical care, and the ways in which
you should treat them differently.
I think it depends, obviously, on the
context.
This is a case
where I think people get too confused
when it comes to research ethics.
So when I say this, the IAB should look at whether the
potential
benefits of the drug to the participants
justify the risks.
So like, how do you do that outweigh
risks?
What does outweigh mean?
That's a matter for confidence say for justified.
What do any of these things mean.
And my response to that is, yeah, I know this is hard, but
notice
that's what doctors and clinicians
do every day, right?
That's the job of clinicians, is to ask themselves for this
patient
to the potential benefits of this drug
outweigh the risks to this patient.
Now, it's harder often to do it in
research
because we don't have as much data.
But at least I think that the approach,
the sort of basic idea,
is exactly the same.
And so I've termed this the informed clinician test.
And basically what the should do is either
pretend
they're an informed clinician or call one up and actually ask
them.
Here's the situation.
Here are the patients. Here's the drug
we want to give them.
Here's what we know about it.
Do you think that the potential benefits
for these patients justify the risks?
If they say yes, it's a prospect of benefit study.
If they say no, it's a net, what I call a net risk study.
A little bit more philosophy here as so.
Well, I've been saying as you look
at the individual interventions.
Right.
And then you look at the whole package.
What are you supposed to do?
And I sort of highlighted this.
What do you suppose are we looking
at the individual interventions.
And here's the question
to the potential benefits of each intervention
have to justify the risks.
So imagine a study like this.
Imagine a study where I'm going to give people
a potential treatment for kidney disease
or say, liver disease
really could help them.
Looks really promising.
Lots of potential benefits.
But in order to do the study scientifically, I have to do a
biopsy
like a liver biopsy or a kidney biopsy
to pose a significant risks.
And I'm only doing it for research purposes.
If you look at the
liver biopsy or the kidney biopsy on its own,
it looks like that's very risky, not potential benefit
intervention.
And at least saying children, you wouldn't be allowed to do it
and people would probably be really worried about doing
it.
Adults. So is that the way you're supposed
to do it?
Each individual intervention potential benefits have to
justify for us?
Or do you put the whole thing together
and say, yeah, no, I know that.
But look, the potential benefits of getting the drug,
they justify the risks of not only just the drug,
but also the risks of the liver or kidney biopsy.
So that being in the study overall is prospect
a benefit for the people who are going to get into it.
So that's a question of whether or not
you have to look at each one
just individually.
Or you can have the potential benefits
of one intervention
in a study justifying the risks of another.
So hopefully that's clear.
If it's not, we can definitely come.
We can definitely come back to it.
I think that's a really interesting challenge.
Here's a practical example of this.
We wrote a paper on this.
Imagine a trial in kids placebo controlled trial.
Some people get this
potentially beneficial intervention
and some people get a placebo.
What's the risk benefit assessment
of or the profile of that overall study?
What some people will say as well
at the outset before randomization.
There's a chance you'll get the drug.
And if you get the drug and it's beneficial, then that
justifies
even being randomized to the placebo arm.
So the overall study is prospective
a benefit.
What we argued is actually that's a mistake.
And you need to individually look at the
risk benefit of the two arms of the trial.
And that means that the risks of the placebo arm have to be
acceptable.
And you can't justify them by the fact that there was a
chance
the person could have gotten into,
but didn't.
The active treatment are.
So that's just what exist.
Specific example of that.
Okay.
So we're just talking about
whether or not the potential benefits
justify the risks.
If they do, you have a prospect of benefit
study.
If they don't, you have what I just call a net risk
trial.
So then the next last challenge here
is thinking about net risks.
When are net risks acceptable?
How much net risk is acceptable.
So when it comes to individuals who can't consent like kids,
there are some people who think it's just
unethical.
Sister, it's been going on for 6100 years.
Some people say it's just unacceptable.
It's unethical to pose any risks to kids
for the benefit of other people.
It's always exploitation if they can't
give information, we can talk about that.
If people are interested.
Well, lots of people have that worry.
In fact, if you look at almost all
guidelines,
I know around the world they allow some risks.
And kids particularly, and this is the
buzzword you'll hear more about from CMO,
whether or not the risks are minimal
or greater than minimal.
And I'll just go through this.
I think you guys are going to hear
a lot more about this.
So what's the minimal risk?
The standard based on the U.S.
regulations
that a lot of people have adopted, is
what our bodies are supposed to do.
It's supposed to cover the risks of the intervention of the study
to the risks that are ordinarily encountered in daily life,
and if the risks of the intervention
are equal to or lower, then
the risks or the other encountered in
daily life,
then it counts as a minimal risk intervention
or study.
And if they're greater,
it counts as a more than a medium
risk intervention study.
That's the standard that when I was the study I
talked
about before that we did with CMO,
we're talking to the IRB chairs.
That's the question we were asking them.
And you notice how if I just say to you, allergy
skin testing
or the risk of, I was your skin testing,
11 year olds less than or greater
than the risk of daily life, I hope
all of you guys would say, I have no idea.
You're going to tell me what the risk of hours of skin
testing are.
You got to tell me what the risks of daily
life are before I can compare them.
And that's the thing.
I was pointing out that none of those IRB chairs did,
even ones.
Okay, so
this is a this is a study we finished
a couple of years ago with Will Shukman,
who was now in sociology program at UCLA,
who was a post back here, and we wanted to get data.
There's been this debate for a long time,
but there haven't been any data on
what the public thinks about exposing kids
to risk or benefit of others.
So we did a survey of this, a nationally representative
sample.
And what we found is really strong support
amongst the public, including parents,
for exposing kids to research risks
for the benefit of others,
including things like a bone marrow biopsy, which poses more
risks,
I think, than most IRBs would be willing to countenance.
Okay, that's kids.
You're going to get more of that.
So that leaves the question about what about competent
adults?
Should there be limits on the extent
to which researchers can expose
competent adults to net risks, to risks
for the benefit of others?
And how do we decide that?
And does it depend on things
like whether the participants are healthy
or the participants are sick?
People debate that a lot.
Here are some common answers.
You'll see that it runs the whole range.
So if you're sort of libertarian minded,
you might say, look,
you said competent adults, competent
adults get to decide what they do
with their lives, including facing risks
for the benefit of other.
So if they're competent, it's just up to them.
Other people have really strict standards.
They think, no, actually, I think IRBs sometimes do this.
The net risks to which we expose cannot
or shouldn't be much more,
if any, greater than the risks of which we're willing to expose
children.
Some people are really strict about this,
and then other people have argued.
So Frank Miller used to be in this department,
and he argues that, well, let's look at research.
Participation is kind of an altruistic
endeavor.
So let's look at what risks we think are acceptable
in other altruistic endeavors like organ donation.
Now notice that looks like it would allow
pretty high risks.
It's interesting at the end of this paper,
although these guys
suggest that standard, they're kind of
like maybe that's too high.
We're not sure.
Alex Lundin, who's at Carnegie Mellon,
suggests similar thing, but usually like
a public service, like firefighters.
And what do we think are risks acceptable
and that those are going to set the limits
for research risks
with competent adults. Okay.
So just a very brief summary.
Doing a systematic risk
benefit assessment is right by the Belmont
Report was right.
It's important to do this right.
I don't think people are doing it right.
And I think that if we did it more
systematically,
it would make the research better,
it would make the research more ethical.
And then the second part of it, which I hope didn't get too
confusing
and detract
from understanding the framework, is that
I think there's really interesting,
challenging questions here for us, for anybody who's
interested
in research ethics, to figure out.