DSMBs in Adaptive Trials with Roger Lewis

Judith: Welcome to Berry's In the
Interim podcast, where we explore the

cutting edge of innovative clinical
trial design for the pharmaceutical and

medical industries, and so much more.

Let's dive in.

Scott Berry: All right.

Welcome everybody.

Welcome back to, in the Interim, we
investigate all things statistical,

scientific, um, um, medicine as we,
we may talk a little bit about today

in the world of clinical trial design,
innovative clinical trial design.

I'm your host, Scott Berry, and I
have a, a wonderful guest today,

a good friend of mine, and, uh.

And, and has been working with, with
and at Barry for a number of years.

Dr.

Roger Lewis, uh, sort of a doctor because
he's also, he's a PhD in biophysics.

He's also an md.

Uh, he's a professor at the the Geffen
School of Medicine at UCLA, a member

of the National Academy of Medicine,
and also a fellow of the a SA.

Uh, so Roger, welcome to in the Interim.

Roger Lewis: Great.

Well, it's a pleasure to be.

Scott Berry: Yeah.

So, uh, uh, uh, uh, an interesting topic
today and you're, you're, uh, incredibly

well experienced and, and passionate
about data safety monitoring boards.

Um, and you have a long history of
serving on data safety monitoring boards.

You've been a member of statistical
analysis committees that have presented

to them, uh uh, been all around the
world of data safety monitoring boards.

So we're gonna talk about them,
particularly the role in new trial

designs, innovative trial designs,
adaptive trials, platform trials.

Roger Lewis: Role of the data safety

Scott Berry: Board.

So it, it'd be

Roger Lewis: would be wonderful.

Scott Berry: uh, so my, my wife always
reminds me that a lot of people watch

this and listen to this, that may
not know all of the details of this.

So

Roger Lewis: let's, start

at the what is the role of a Data

Scott Berry: safety monitoring
board in a clinical trial?

Roger Lewis: So the Data Safety
Monitoring Board, which is sometimes

called a data monitoring committee,
has goes by a number of different

names, but they're all generally
groups of people that are assembled.

To keep an eye on things as a
trial is being conducted and to

protect the participants or the
volunteers who participate in

the trial from avoidable risk.

So there are, there's a step
before you start a trial where the

investigators, um, and all of their
collaborators and maybe patient

representatives think very clearly about.

How do we design a clinical trial?

So we learn and improve the care
of future patients, but at the

same time, how are we gonna protect
the patients within the trial?

But once the trial begins, those
investigators are generally blinded to

the accumulating results within the trial.

So they don't have insights
into what's going on.

Once the the process is actually started,
the data monitoring committee or the data

safety monitoring board fills that gap.

And keeps an eye on things when the
investigators are not supposed to be

looking to avoid biasing or otherwise
manipulating the trial results.

Scott Berry: So what, up the
data safety monitoring board?

Who, who, what, what, who might be
the members of a, a typical DSMB.

Roger Lewis: So a typical DSMB uh,
has, uh, members who are experts

in the clinical medicine or the
science of what is being done.

They may know a lot about
the type of therapy or the

usual treatment of patients.

There are usually statistical
or clinical research design

experts who know a lot about.

The interpretation of accumulating data.

'cause there are some specific
challenges to looking at data multiple

times as a trial is being conducted.

And then sometimes, depending on
the context, there may be patient

representatives or specialists in the
ethical considerations and in trials

that that span, um, the enrollment of
or include the enrollment of patients

across multiple geographic areas.

Or, or settings in which the patients
are particularly vulnerable, say

they're incapacitated by their illness
and can't consent on their own.

There may be additional people
brought in to be, to bring in the

perspectives of those locations.

Scott Berry: Oh, interesting.

So I, uh, we,

Roger Lewis: We.

Scott Berry: back to waiver of informed
consent and if it's DSMB different

in a, in a situation like that.

So during the trial, largely the
Data Safety Monitoring Board are

those that are seeing the data.

Uh, and generally people may not
understand that the investigators,

the outside world don't see
the data, but the data Safety

Monitoring Board is watching it.

didn't say anything about the role
of the Data Safety Monitoring Board,

or your view on the role of the Data
Safety Monitoring Board about the

scientific credibility of the trial.

So as the trial's going, presumably
we're running this trial to answer.

question, hopefully
more than one question.

What do

Roger Lewis: What?

Scott Berry: the role of the DSMB
is in making sure that that the

credibility of the trial, that it's
answering the scientific question.

Roger Lewis: Yeah,
that's a great question.

So the, when I'm usually
asked about the role of DSMB.

I talk about three different
levels of responsibility.

So the first is to the
participants in the trial.

So it's to protect those participants
from avoidable risk, including risks

that could not have been foreseen
at the time the trial was designed.

The second is to provide an assurance that
the scientific integrity, the validity,

um, the credibility of the trial is to
maintained to the extent that is possible.

While protecting the
participants in the trial.

So for example, in the setting of
an adaptive trial, which I hope

we'll talk about, there are very
specific rules that are in place.

That must be followed if the design
is in, is going to have the operating

characteristics, the protection from error
rates and bias that we want it to have.

The DSMB is one of their responsibilities
is to make sure that that trial

design is followed, as long as that's
still consistent with protecting

the participants from risk.

The third, uh, uh.

Responsibility of the DSMB and
the hierarchy, in my view, is to

operationalize the sponsor or the
investigator's goals with respect

to the, the, the trial itself.

So there are decisions that
sometimes have to be made as

the trial is being conducted.

So, for example, stopping
a trial for futility.

That should take into account all of
the different goals, uh, scientifically,

um, and otherwise, with respect to
the trial and balance those that can

only happen if the DSMB understands
in some depth what those goals were

Scott Berry: Yeah,

Roger Lewis: you,

Scott Berry: on adaptive trials,
and I want to talk about the

difference of A-D-S-M-B in an
adaptive trial and maybe set up that.

If you are running a trial and the trial
design is roll a hun, enroll a hundred

patients, and then we'll look at the data
and, and, and do an analysis, the DSMB

can follow the data, but you know, they
would have to, they would have to stop

it, otherwise it runs out to the end.

But otherwise.

The role of them is monitoring
data largely and, and looking

for, for safety signals.

Roger Lewis: In

an adaptive trial

may be

set up.

Now there's multiple.

Scott Berry: analyses, it has
adaptive triggers that can happen.

Patient populations could be stopped.

Uh, randomization could be
changed in adaptive trial.

So how

Roger Lewis: How is the role of the

Scott Berry: now changed?

Roger Lewis: Yeah, I
think it's a great point.

So in, in the traditional trial, you
designed, say with a fixed sample size.

The DSMB is largely looking for safety
signals or maybe operational or logistical

challenges that weren't foreseen, so
problems that weren't, uh, weren't

anticipated in an adaptive trial.

With its various moving parts, the,
the role of the DSMB expands to

making sure that the adaptive trial
is conducted as it was in intended.

As long as that continues to be cons,
uh, ethically and scientifically

appropriate, and most importantly
to understand the difference.

So it's, it's one thing to understand
how a traditional fixed sample

size trial is supposed to be
conducted, and it's qualitatively

more complicated to understand how
a modern, adaptive, or say, platform

trial is intended to be conducted.

Scott Berry: So now, um, and, and I, I'm
typically involved on the design side.

I've, I've, I have been on dsbs and
I, I've, I've been a part of them,

but I'm typically on a design side.

And what we're

Roger Lewis: We're trying.

Scott Berry: is a really efficient
design that's going to answer

the questions efficiently.

It may have stopping rules.

We define detailed stopping rules,
algorithms, models, and something

that we've simulated a great deal.

The design, the characteristics
of it, you could

Roger Lewis: You could
almost say, now this could be

Scott Berry: automated.

It within the setting and the,
the, so the question is, the role

of A-D-S-M-B there, and, and by no

Roger Lewis: no means.

Scott Berry: am, am I saying that this
should be run without A-D-S-M-B, but

the role of the humans watching this
automated machine go seems entirely

Roger Lewis: Different.

Scott Berry: maybe A-D-S-M-B
of 15, 20 years ago.

That was kind of a fixed trial design.

Roger Lewis: So I think it's,
it's, the role has expanded.

I'm not sure it's different in its
intent, so, so let's take the, the.

The type of situation that that
you and I are commonly involved

in, it's adaptive design.

It has planned interim analyses.

There are decision rules based on
statistical triggers, so the DSMB should

be paying attention to whether the design
is being implemented as it was intended.

The DSMB should be looking at the
characteristics of the incoming data.

That the design is by design blind to.

So most of the adaptive trials that
that, uh, that we see we work on,

we see designed by others, um, are
generally have the adaptations driven

by the primary endpoint of the trial.

Now, occasionally we have some
that it combine, um, efficacy and

safety endpoints, for example,
in a utility function, but there

is a restricted set of data.

That the adaptive design is responding to.

So one of the things that humans should
do is pay attention to the other parts

of the data stream that the algorithm may
be blind to and make sure that everything

still makes sense and is appropriate.

Secondly, um, there are settings in
which the, what happens in real life

unfortunately falls outside the,
the range of what was simulated.

So, for example, we may simulate a design
that, uh, under certain assumptions

regarding the accrual rate and just remind
everybody the operating characteristics

of an adaptive design that uses par,
uh, incomplete data from patients is

that the operating characteristics
actually depend on the accrual rate.

So let's suppose we have a
trial in which the accrual rate

is much faster than expected.

The DSMB may want to verify that
the operating characteristics

are still still acceptable.

So that's, that's an example.

So I think there's a general class
of situations, which is, uh, those

unanticipated scenarios in which
the operating characteristics of the

design may not actually be as well
understood as the regions of, of what

might occur that we did simulate well.

Scott Berry: So

Roger Lewis: So I.

Scott Berry: being in that
situation and, and, and sitting

on A-D-S-M-B and wondering, um.

The, the algorithm is set up and it might
do a predictive probability of success.

Should we stop enrollment?

What's the predictive probability
of success or algorithms

running you as the DSMB?

I assume you have to
know a little bit about.

How that's functioning, what
that probability is, what it

incorporates and what it doesn't.

So you described that you need to look at
the data that the algorithm can't see, the

design didn't incorporate those things.

It sounds like there's
a whole new skillset.

To understanding the design, what's
involved, what's not, a whole bunch

of time spent with the designers
before you go under the, under

the, the, the unblinded part.

And you're now by yourselves and you
can't really ask many of these questions.

Uh, and that's hard.

So a whole new skill set to sit on
A-D-S-M-B of more complicated trials.

Roger Lewis: I, I think that's right, but
I think it's both skillset and process.

So this will do them in that order.

The skillset is you need to have people
on your DSMB who absolutely understand

how the statistics were supposed to
work and how they will work if some of

the assumptions that we, that we tested
carefully turned out not to be true.

So, for example, as a general rule,
if enrollment is slower than expected,

the design's gonna work just fine.

Scott Berry: Yeah.

Roger Lewis: And so you don't
have to worry about that.

Whereas in the other direction,
you might or might not have to

worry depending on the details.

But there's also a process issue which
you, which you alluded to, which is

that before the DSMB sees any data
or certainly unblinded data, the

DSMB can have open conversations with
the people who designed the study.

The sponsor regulatory agencies and
understand, um, the considerations that

were taken into account and how the
design was developed, how it's supposed

to work, and how it was evaluated.

Once the DSMB has seen unblinded data, I.

To avoid various types of bias that
we may or may not exist very often,

but we certainly worry about a lot.

The DSMB needs to not discuss any of these
details with the, uh, investigator team.

Other than through the provision of
specific recommendations that are intended

to improve the safety or validity or,
or other characteristics of the trial.

So all the conversations about how
the design was supposed to work and

why it was designed that way need
to occur front, and it requires much

more preparation before the DSMB, as
you say, goes, goes un uh, into their

cone of silence if, if you will, and
I think this is an extension of the.

Uh, of the expertise that was
required with, say, a traditional

group, sequential design, dsbs with
traditional group sequential designs.

Uh, generally had a statistician
who understood that methodology.

An alpha spending approach, for example,
of an O'Brien Fleming stopping rule.

You needed someone who understood
the design, but now the designs

are much more complicated.

So you need people who not only
understand the general theory, but they

understand the specific application
of the design that you're tasked.

With overseeing

Scott Berry: Yeah, so

Roger Lewis: so.

Scott Berry: we've had, um, won't
mention any names or things, but I've

been involved in a design of a trial
where we have that initial meeting.

And we're, we're talking to DSMB
members who, quite frankly the

Roger Lewis: The

say it.

Scott Berry: like the design and they,
they almost wanna redesign before

they start, before it starts, oh, I
would do this and I would do that.

And we've had circumstances where.

has largely said, I don't think
you should be a member of the

DSMB if you don't like the design.

That the role of the DSMB
isn't to redesign the trial.

So there's some level of acceptance
of the design by being on A-D-S-M-B.

Is

Roger Lewis: Is that fair?

Oh, I think, I think
that's absolutely fair.

First of all, I don't think that as,
as someone who serves on A-D-S-M-B

member, I don't think I should serve on
A-D-S-M-B if I think it's a bad design.

'cause part of my responsibility
is ensuring that design is

conducted as it was intended.

For reasons of scientific validity.

On the other hand, um, none of us like
criticism and you know, we, we work for

weeks or months or many months on the
design of a trial, and the DSMB does

come into the, uh, evaluation of that
design with a fresh perspective and

Scott Berry: Mm.

Roger Lewis: there may actually
be some really good insights and

the sponsor's response should not
be to kick off the dissenters.

But, but to decide to make sure that
they aren't, uh, that they only have good

points that warrant a reevaluation of
some of the characteristics of the design.

And I think that, um, that brings
us to an interesting point, which

has to do with the, the, the
requirement for DSMB members.

To be independent financially and
scientifically from the sponsor

or the product, um, and hopefully
intellectually from folks who

are developing the, the therapy.

So for example, you don't want someone on
your DSMB who has spent their entire life.

Um, uh, developing compounds in Class
X and then put them on A-D-S-M-B of a

trial evaluating compound X because they
will, they will want to see it, it work.

Um, and it's just not saying
that people are dishonest.

It's saying that PE we
all have our own biases.

And, um, it's those, uh, covert biases
that I think are, are the most worrisome

because they're the hardest to account
for in, in what can be quite nuanced

discussions about risk, um, and benefit.

So the, the DSMB members are
generally people who are scientific

experts or medical experts in the
clinical area in general, research

methodology in related therapies.

But they need to have some level of
independence, um, from the actual, uh,

product development, which is why they
may look at a design and there may

be things they don't like, but it may
also because they have broader insights

into likely challenges with the patient
population, outcome assessment, safety

assessment, and those sorts of things.

Scott Berry: Yeah.

Yeah.

Um, and, and, and point very well taken
about, um, uh, there can be huge value.

Uh, brought from the DSMB to that, even
in the, the early design stages of that

uh, over the years, and we won't, we won't
say how many years you've been doing DSMB

work, but o over the years, uh, given the,
the, the, the changes, is there a bit of

a clash that people, uh, who have been
DSMB members in, uh, I'll say somewhat.

Traditional fixed trial designs
jump into an adaptive trial, and

it's, it's a different world.

It's a, it, it, it's a different type of
trial and a bit of clash with maybe the,

i, I won't even say, but the amount of
time spent in the review or the, the, the

expectations for A-D-S-M-B or the, the

Roger Lewis: The role that they play.

Scott Berry: what might be a a 20
interim analysis adaptive trial

that they're trying to follow.

Roger Lewis: So there's lots of growing
pains, moving people who have experience

with traditional approaches into, uh,
the role of oversight of these trials.

And I.

I am sure that when we're done with
this recording, I will regret not

having remembered some of them.

Um, so one has to do with
the level of preparation.

So it is an open secret that many
clinical members of d SMBs are

picked because there have, uh, very,
um, high stature in their fields.

They have a lot of credibility, um,
and they are very, very busy people.

They often show up to DSMB meetings
not having spent sufficient

time reviewing the reports.

And as a sub separate topic, these reports
have just grown in length and complexity.

Um, and, uh, we could spend an entire
podcast talking about how damaging that

is to the overall safety of, of the trial.

So these people are used
to showing up to meetings.

With, uh, insufficient preparation
and still be able to make important,

meaningful contributions based on their
knowledge of the clinical disease and

context and the fact that the trials
were quite simple and they could

pretty much figure it out on the fly.

That's no longer true.

And the amount of preparation
required before the meeting start,

before you see data and for each
meeting is substantially greater.

And people may or may not be,
uh, able to incorporate that into

their other demands and their
professional, professional existence.

There is a, a commonly observed phenomenon
that if you want the best possible

review of a grant or a manuscript,
the best person for that is someone

who's still an assistant professor.

Um, because they will spend the
time and they will care and they

wanna make a good impression.

And there's some of that
truth in DSMB work as well.

Um, that the best people to do
this are not people who, um, are so

well known that they can't devote
the time to what's necessary.

So that's one area of conflict.

A second area of conflict, which I
think we really need to touch on, is the

difference between a rule and a guideline.

So if you look at the, um, what
was written in the literature about

data safety monitoring boards,
for example, as, as originally

envisioned by NIH institutes.

A very common paragraph in those,
in those publications was that the

rules, uh, or the stopping rules
say, you know, group sequential

stopping rules were merely guidelines.

And it was up to the DSMB to decide
when they were, um, appropriate

to apply and when they weren't.

And in fact, there was an implication
that if the DSMB thought it wasn't

good to stop so soon, that that
was just fine and they could just

make that decision unilaterally.

Um.

That view, at least in my opinion, is
completely inconsistent with this idea

that we want adaptive trials including
group sequential trials that have,

um, defined operating characteristics.

If you want a defined operating
characteristic where you've simulated

the trial under a set of rules, they are
rules And just like, um, other rules,

Scott Berry: might say
the rule in the protocol.

It

Roger Lewis: uh.

Scott Berry: It might.

It's in the protocol.

Roger Lewis: Absolutely.

Absolutely.

So these brief specified rules are
rules, which doesn't mean they can't

be broken, but if they're broken, they
need to be broken for very explicit

reasons, not because a member has a
hunch or is curious what would happen

if the trial went a little bit longer

or or the member
disagrees with the design.

It needs to be for a reason.

Like there's a safety signal.

We didn't anticipate.

Um, and then, uh, the reasons for
deviating from the rule need to be, in

most cases, discussed with the sponsor.

Um, so, so one of the big disconnects
between DSMB work a couple of decades

ago and modern DSMB work adaptive trials,
is that adaptive trials have rules.

And they are not merely guidelines.

If we're going to simultaneously
argue that we understand the operating

characteristics of these trials,

Scott Berry: Yeah.

Yeah.

The

Roger Lewis: the.

Scott Berry: uh, within that now.

Uh, with the rules and the trial running.

I, the, my analogy is that
you, this is like a plane

that's flying automatic pilot.

It, it has, it has, uh, uh,
algorithms that fly the plane.

But you have a plane sit, you
have a pilot that's there, and the

DSMB is somewhat like the pilot.

Now, part of this is.

have to have the DSMB there
because they need to evaluate the

appropriateness of those rules.

I could imagine situation where
concerns that the data that's

going into the algorithm is flawed.

There's weird missing data to it.

There's concerns that the data's not
appropriate, and you might think, I

know it's a rule, but I'm concerned that
the data's not appropriate, and I don't

Roger Lewis: I don't,

Scott Berry: rule is
any longer appropriate.

Roger Lewis: that seems very different.

Scott Berry: I just don't like it.

Or it would be nice if the trial ran for
another year, or, or, or as a guideline.

That seems though really
hard for the DSMB.

To, to, to make those judgements,
but seems that that's the

key role now for them.

Roger Lewis: A Absolutely.

And, and let me give you just a couple
of, um, semi hypothetical examples

of those types of considerations.

So let's say you have a.

Um, a futility stopping rule, and yet
your first interim analysis at which

that futility rule can be applied.

Let's, and let's say the
disease in question is a

chronic degenerative disease.

The first patients who are enrolled
in the trial may be from the

reservoir of, of patients with
relatively longstanding disease who

are waiting for the trial to open up.

And therefore their disease may actually
be qualitatively different, more

difficult to to slow the progression
or to intervene on than patients with

newly diagnosed disease, in which case,
it may be that the futility rule is

appropriately interpreting the data,
but that the population we expect to

enroll later in the trial may actually
have a more favorable prognosis.

So we're getting the right
answer on the wrong population.

In which case the rule
might not be appropriate.

A very similar situation occurs in, in
global clinical trials, if there may be

particular geographic areas in which,
for example, a, a surgical procedure may

behave differently than in, in some areas.

Obviously, we'd like to have
the heterogeneity in the disease

process or in the surgical
procedure thought out ahead of time.

So the adaptive design accounts for it.

It, but the DSMB in those cases may
be suspicious that the rule is missing

an important, um, characteristic
of the data stream that need, that

really needs to be accounted for.

Scott Berry: Yeah, so there, there,
within these adaptive trials, there's also

somewhat of a new group and it's, uh, it
there, there always was a statistician

who was unblinded to the data.

That's presenting safety tables,
um, and these, these tombs of

safety tables that they go through.

And there was always that role,
but this individual was kind of a

master of the data and presenting it.

Now, in most of these trials, we
have statistical analysis committees

that are running, the models that
are driving, driving the adaptations.

The interaction between the DSMB and
the statistical analysis committee also

seems to be very much of a new thing.

Roger Lewis: Yeah, absolutely.

So, so as you well know, the, the
Statistical Analysis Committee

is an unblinded group, um,
who actually run the analysis.

So it, they receive data usually
from a data coordinating center.

The data they receive have
usually gone through the, um, the

ongoing quality assurance process.

And what I mean by that is that they
are cleanish, but they are not locked.

Um, and the statistical analysis
committee, then, um, it makes a good

faith effort to implement whatever the
pre-specified analysis were intended, uh,

necessary to drive the decision rules.

And I think it is a rule rather
than an exception that the

statistical analysis committee.

Learns things about where the data are
more or less consistent, where they're

more or less complete, where there may
be issues with internal inconsistency

in the data that, um, are important to
understand, to help, uh, assess the.

Um, the credibility or the level
of assurance we wanna place in the

results of those interim analysis.

One of the things the statistical
analysis committee has a lot of

insight into, just to give a concrete
example, is you're doing analysis.

Three, you can see which of
the patients in analysis three,

we're actually also in analysis.

Two.

But they've had an outcome, or god
forbid even a treatment assignment

changed since the prior, prior analysis.

And in big trials, those things happen.

But it, it helps us on the
statistical analysis committee side

understand the level of assurance
that we should place in the data.

And you can picture a setting
in your interaction with the

DSMB, especially if, if something
is near a statistical trigger.

That the DSMB would appropriately
ask questions about the quality of

the data that yielded that result.

Scott Berry: Yeah, and, and, and so then
that statistical analysis committees.

Trying to diagnose the appropriateness
of the analysis, the data that went

in, uh, and, and, and interacting
with the D-S-M-B-I imagine back

and forth a little bit about that
appropriateness when the time comes

for a, a decision or a trigger is met.

Roger Lewis: Yeah, I mean the, the pattern
that we see is that the Statistical

analysis committee statisticians.

Uh, singular or plural.

Uh, present their interim analysis report,
which as you noted is separate generally

from the safety reports and secondary
endpoints and all those other reports.

They present that, um, interim analysis
report to the DSMB, and then they

stick around to answer any questions.

Usually the DSMB charter allows for
the DSMB to then kick the statistical

analysis committee out of the meeting.

My experience is that usually the
statistical analysis committee has.

Very useful insights into the data,
and they rarely get kicked out.

Scott Berry: Yeah.

Yeah.

Yep.

You know, I, I

Roger Lewis: I, I

Scott Berry: to start this off with,
uh, my layup question for you, which I

think we want to get on the podcasts.

Roger Lewis: forgot.

Scott Berry: DSMB be blinded
to treatment assignment?

Roger Lewis: Okay.

Um, yeah, so the, so the DSMB is.

Inherently tasked with balancing
efficacy and safety at a very fundamental

level, and those considerations are
not, are virtually never symmetric.

So we will, for example, want to continue
a trial that has a good chance of showing

that the new treatment is helpful.

We rarely wanna prove with the
same level of certainty that we can

hurt people with a new treatment.

So given the lack of symmetry.

There's absolutely no value whatsoever
in blinding the DSMB to treatment

assignment at any point in the trial.

And I, just to be clear, my
position on this, which I, I

know, I know you anticipated, is
what I mean is that every table.

Figure comparison presented to the DSMB
should explicitly label the treatment

arms no A versus B, no shuffling of them.

They should be labeled with the
actual name of the treatments so

that the DSMB never is confused.

There are extraordinarily high profile
cases where dsbs thought they knew what

was going on when they were looking at
blinded data, and by blinded, in this

case, I don't mean aggregated, I mean.

Treat treatment assignment separated,
but labeled A versus B, um, with

sometimes with that randomized and
the DSMB made bad decisions 'cause

they assumed they knew what they were
was going on, but they were wrong.

The cardiac arrest, uh, excuse me,
cardiac arrhythmia suppression trial

is the most notable example of that.

One of my favorite publications on this
was an editorial from Curtis Maynard in

the New England Journal many years ago.

Which is entitled Masked Monitoring
and Clinical Trials, blind Stupidity.

Scott Berry: Yeah.

Roger Lewis: Um,

Scott Berry: Okay.

Roger Lewis: as New England Journal 1998.

He was right then.

He's still correct.

Scott Berry: so largely the DSMB
should have access to everything.

in, in this, this notion of risk
benefit, I maybe you could construct

weird cases where there, there there's
some efficacy endpoint that's separate,

but largely it's a question of is the
risk benefit profile for a patient

in the trial still, still good?

And they should have, essentially
have everything at their, that,

that, that they can possibly get
data to make those decisions.

Roger Lewis: I, I think
that's exactly right.

Now, I'll give you an example of
where people get confused about this.

So, picture a trial that's gonna take
a year to get the, to the first plan

to interim for efficacy, where the
first opportunity for early stopping

for efficacy is, but the charter
and, and appropriately, so the, the

committee meets in after six months.

So a common area of confusion is
at that first six month meeting

should the D-S-M-B-C efficacy data.

Um, and the answer is yes,
they should, just to be clear,

um, because they will even be
balancing efficacy and safety at

that six month, um, uh, meeting.

The confusion is that because
there's no opportunity to stop early

for efficacy within the design.

At that first meeting, people sometimes
argue it's a safety only meeting.

The reason this is, um, incorrect is
because the level of safety concern

that the DSMB should appropriately
tolerate depends on the benefit that

patients are receiving from the therapy
or rec or appear to be receiving.

You simply cannot, um, separate them.

Scott Berry: Yeah, I now
do adaptive designs almost

address this more appropriately
because the rules are laid out.

Maybe there's now five interim
analysis and it says very clearly

you cannot stop at this analysis.

Here are the rules here.

They're set up where maybe historically
there was a notion that when the DSMB

meets maybe we'll declare success.

And it was a little bit
more guideline driven.

And there may be thought to be
reasons not to show them efficacy,

maybe adaptive designs help that.

And of course, many of these
adaptive designs, you can't possibly

follow the trial unless you're
completely unblinded to treatment

assignment because the analyses are.

Roger Lewis: You know, I, I think
many of these things come up on

what I'll call soft considerations.

The safety end, the safety outcomes
that are not involved in the primary

efficacy analysis, um, the total
burden, um, those sorts of things.

So I'm not sure the adaptive
design helps those that much.

I think with respect to the, um, the
direct balance of the primary efficacy on

the primary endpoint versus say a pre, um.

Uh, a preconceived primary
safety consideration.

I think the fact that the adaptive
design in general allows you to, to

look in a pre-specified way earlier,
I think it does, it does help that.

But the fact is that each time the DSMB
meets, if patients have been treated,

um, within the trial, they should be
seeing efficacy and safety data so

that they can explicitly balance them.

Scott Berry: Yeah.

Yeah.

Wonderful,

Roger Lewis: Wonderful.

Scott Berry: Uh, and

Roger Lewis: So the name of this talk

Scott Berry: is in the interim, and we
have d SMBs that live their life in the

interim, uh, looking at the interim data.

Roger Lewis: by Roger.

Scott Berry: appreciate it.

That was fabulous.

Um, thank you for joining in the interim.

Roger Lewis: Great.

Thank you for having me.

Scott Berry: Thanks, Roger.