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.
Well, welcome everybody back to In The Interim.
I'm your host, Scott Berry.
All right.
We cover a number of different topics on In The Interim, uh, w- some
recent sports editions and all that.
Today I'm gonna go back a little bit, and I'm gonna go back to a really interesting
effort of multiple clinical trials coming together for questions during COVID.
This effort, uh, was given a name called the Multi-Platform
Randomized Clinical Trial.
Now, I'm gonna come back to the name.
It's a, it's a really important part of the story actually.
But let me set up the story, and I think it will make a little bit more
sense as to why that name is, is such an important part, uh, of the story.
Okay.
A great deal of people, time, and effort went into this, so I'm gonna
give some, some explanation of this.
I'm gonna walk through a number of people from here at Berry Consultants,
Roger Lewis, Lindsey Berry, uh, Liz Lorenzi, Mark Fitzgerald, Michelle Detry,
Anna McLaughlin, Christina Saunders.
We have huge numbers of people globally working on this.
I can't possibly mention everyone, um, uh, in this, but a, a huge effort and
I, and I think you'll, you'll hear why.
Okay.
So there were...
A long time ago in a place far, far away, there were, there were three
different trials, and I'll, I'll, I'll try to set them up briefly and then
describe the intersection of them.
So plat...
Yeah, you, you, you're probably aware that platform trials played this enormous
role during COVID for finding therapies that did and did not work for the
treatment of patients with COVID-19.
Vaccines were a whole different issue, but for therapeutics to treat COVID,
the Recovery trial, the Principal trial, the REMAP-CAP trial, the, the
active programs, the US NIH, the US, um, uh, Operation Warp Speed efforts.
There was ICE by COVID, the Together trial, Solidarity, DNDi, ATTACK.
So multiple platform trials made huge impacts during it.
So I'm gonna describe three of them somewhat briefly.
REMAP-CAP, which you're gonna hear if you tune into this, uh, uh,
podcast for, for more interims.
You're gonna hear much more about this effort.
Amazingly, we are on episode sixty-two Two or three, and I've
not talked about Remap-Cap yet.
It's, it's an incredible effort.
In part, it's just such a, um, uh, enor-- it was such an, an
enormous and very cool effort.
I, I haven't quite figured out how to do podcasts about it.
So this is, uh, Remap-Cap.
Remap stands for Randomized Embedded Multifactorial Adaptive Platform.
It was built in 2015.
It was built for a potential pandemic.
Yes, 2015, and you know COVID-19 was largely a 2020, uh, uh, disease.
So Remap-Cap was built in 2015 and started enrolling patients in multiple countries
that had community-acquired pneumonia.
The idea was that if there's a pandemic, it's very likely it's going
to go through intensive care units.
It's going to be something that looks like community-acquired pneumonia,
and the trial started enrolling with multiple therapies that are very
interesting in and of themselves in non-pandemic community-acquired pneumonia.
And a master protocol was built, and it built something called a sleeping
strata that was-- We called it the pandemic strata, and it could adopt
and say, "We now have a pandemic.
We are enrolling that pandemic, and we already have therapies.
We already have a database.
We already have sites enrolling.
This is a platform trial ready to go."
And in fact, that's exactly what happened.
So Remap-Cap adopted the sleeping pandemic strata in February of 2020, very,
very early, and one of the domains...
Well, uh, uh, uh, let me go backwards a little bit.
Uh, Remap-Cap from the beginning adopted two stratifications.
Now, stratifications in that trial are really, really, uh, different
than what you m- might be used to.
That, that implies that therapies are modeled f- prospectively,
that they may have differential efficacy in those groups, and it
was severe state and moderate state.
All of these patients are hospitalized.
Moderate state are hospitalized but are con-- are not considered severe.
It's sort of the complement of severe.
Severe state is that you're hospitalized, and you have ICU-level
Organ support, cardiovascular or respiratory organ support.
Largely ventilator, vasopressors, uh, ECMO, uh, uh, ICU-level organ support.
You are severe state.
Moderate state, you're hospitalized in the ward, but you don't have
severe-level, um, um, organ support.
The primary endpoint is for the pandemic strata is organ support free
days, and that's through 21 days, and it actually had a neat aspect of it.
So this is an ordinal endpoint where mortality is the worst outcome.
We, we refer to that as a minus one, and that was actually 90-day mortality.
If a patient died through 90 days, they are considered a minus one.
Actually, I, I wanna be...
I wanna, uh, make sure I'm clear on that.
It's in-hospital mortality, and that, that extends through 90 days of exposure.
So if the patient dies in their hospital visit, they are considered a minus one.
And then it's ordinally the days they are free of organ support, given
they survive and leave the hospital.
So zero is the second-worst outcome.
It means you survive and left the hospital, but for 21 days you
were not free of organ support.
And then one, two, three, all the way up to 21 days, you were
free of organ support, which can't happen in the severe state.
You start on organ support.
There, there were many domains that were started in this, and
this is a multifactorial platform.
They were enrolling antibiotics, steroids, uh, from the beginning in, in CAP.
As soon as the pandemic came, they started investigating steroids, macrolide,
antivirals, immune modulation, uh, interferon, Anakinra, tocilizumab,
sarilumab, convalescent plasma.
And then they adopted very early in March a domain that was looking
at therapeutic anticoagulation.
And the notion here is that the, the cytokine storm at the time was
discussed as the, the, the coagulation was part of that within the body
that was causing the severe disease of COVID Now I'm a statistician, so
please don't take this as medical, but that's largely in my statistical mind
what was going on, and the idea is that would therapeutic anticoagulation
heparin be beneficial for patients?
Would it improve their survival in getting off of organ support?
So a domain was adopted with two arms, standard dose thromboprophylaxis,
so, uh, uh, the, uh...
Consider this low dose, which is given standardly or therapeutic
dose, a high dose of anticoagulation.
Is that beneficial?
And so it started randomizing those two options.
Patients and doctors were not blinded to which arm they were in, uh, uh,
within this setting, but that was adopted by the, the REMAP-CAP trial.
It started enrolling, sorry, in April of twenty-twenty.
ATTACK is a, uh, Canadian-funded, and at the time, a Canadian-funded platform.
The initial goal of this was to investigate exactly the same question,
therapeutic anticoagulation versus prophylactic dose anticoagulation.
Enrolling moderate and severe in Canada, actually, this extended a
little bit into the US, South America.
So this was a platform for investigating the exact same question.
They also, interestingly, in moderate disease, they stratified by D-dimer level.
D-dimer is a biomarker that i-it has some indication of high levels of fibrin
in the blood, es-essentially suggesting coagulation is going on, intravascular
coagulation, and the notion is this thought that this might measure,
again, as a statistician, levels of coagulation, the cyto-cytokine storm.
These may be patients that particularly have differential effect due to
therapeutic dose anticoagulation.
So their design stratified severe disease and then within
moderate, high and low D-dimer.
So really three different groups the trial could come out with a conclusion.
Originally, their endpoint was a twenty-eight-day, are you
alive and free of organ support?
As we're gonna see, they start to work together, and they adopt
the same endpoint as REMAP-CAP.
ACTIV-4-A was one of the...
A third platform now, was a, is an NIH-funded trial investigating
It ended up investigating multiple things, but the first thing to
investigate is therapeutic dose heparin versus prophylactic dose heparin.
There are multiple investigators involved in these trials that are
involved in, in, in the same trials.
Uh, me personally, I was a statistician for each of these three trials.
So I had been working on REMAP-CAP since two thousand and fifteen.
I still work on REMAP-CAP today.
Uh, ATTACK, we had worked with the Canadian investigators, uh, uh,
Patrick Lawler, Ryan Zarichansky, and, um, and many others in that.
And we were working with them on their design.
And then when ACTIVE4 came around, we worked with multiple other
statisticians on the design of this.
And that was the first question for them, therapeutic
anticoagulation versus prophylactic.
Again, hospitalized patients.
Organ support-free days is the primary endpoint.
And now this is somewhat of the third to be adopted, and at this point, there's
already discussions about these three p- platforms doing very similar things.
So they adopt organ support-free days as the primary endpoint.
They are going to stratify differential conclusions by
moderate and severe, and also within moderate by the two D-dimer levels.
They adopt...
By the way, all three of these trials adopt a Bayesian approach.
And maybe that was correlated to me being involved in these, but they
all three adopt a Bayesian approach.
So the que-- the, the, the landscape here is this is globally considered a very
important question, and there are these three trials that are all separately
addressing the exact same question.
So what happens when one of them reaches a conclusion, maybe the other
one doesn't reach that same conclusion, but maybe it has po- similar data and
papers come out at different times?
It was thought to be, uh, somewhat messy.
Okay, we can do a meta-analysis of the three trials.
They don't carry the same weight.
Their perception of them is somewhat different.
And so the three trials are now talking and figuring out, "What
do we do in this scenario?"
The most important part of it is each of the three trials working separately
are going to take longer to come up with an answer to this question
in the middle of a raging pandemic.
Twenty twenty Where the pandemic is globally raging, and this is
an important question, all three are gonna take longer separately.
So the investigators, the funding groups, everybody comes together and
say, "We're gonna work together, and we're gonna actually do it in a way.
We're gonna create something brand new, and we're gonna create something
new, and it's gotta be differentiated from a meta-analysis because it is
different than a meta-analysis."
Statistically, it has components of that, but that this is...
We're gonna call it a multi-platform randomized clinical trial.
It's a randomized clinical trial.
The three efforts all decide to pool their data together into a single analysis.
They're gonna create a joint analysis plan where they all
sign on board prospectively.
They will not read out separately, so it's not that you're gonna see a publication
from REMAP-CAP and then the MPRCT.
They all agree they'll publish together, they'll com- they'll
do their adaptations together, they'll combine their data together.
This is a randomized clinical trial.
It's just that these three trials all take their data from all of their global sites.
They put it together prospectively in a, in a combined analysis.
Hence, we called it the multi-platform, rather obviously
the three platforms coming together.
It's a randomized clinical trial, and we wanted to make sure that message got
across that this is not a meta-analysis.
This is the prospective primary analysis of all three trials.
There was quite an operational effort to this, as you can imagine, that all
three of these trials combine their outcome data together into a, an, an
unblinded statistical analysis committee.
That committee carries out the primary analyses.
They create a efficacy report, and then they talk to the three different DSMBs.
Each of these platforms have a DSMB, and they did that simultaneously.
So these three DSMBs all come together and meet together because they're
hearing the same analyses of the combined data of the three trials.
Okay, so an incredible effort.
There is a statistical analysis plan agreed to by the three platforms.
Uh, uh, that's finalized on August 29th, 2020.
And, uh, ACTIVE starts enrolling in, I, I, I think it was largely, uh...
Do I have that written down?
ACTIVE, uh, starts enrolling patients, I believe summer, June-ish Within that.
So all the trials I have started enrolling, no analyses have been done.
August twenty-ninth, the analysis plan is finalized.
They're gonna do monthly a-adaptive analyses.
The adaptive analyses which could trigger in severe disease is its own group.
And then in moderate, there's low and high D-dimer levels.
So there are three inferential groups where we could reach differential
occlusions, uh, conclusions or the same.
What happened was early on in the trial, they ended up with a
large amount of missing D-dimer data for moderate patients.
So we had moderate patients with low, high, and there
was a group that was missing.
We created our own group and said it's missing D-dimer.
But it's really awkward to come up with a conclusion, so we, we reported
on that strata, and it was part of the modeling, but it couldn't trigger
separately because that would be really awkward to say, "Well, if you're
missing D-dimer, here's what we think."
So those three other groups could have adaptive conclusions.
Could conclude superiority if the probability that the odds ratio
for proportional odds ratio model, Bayesian model for organ support-free
days, this combination of mortality and days free of organ support, if
the probability of superiority is greater than ninety-nine percent.
We're gonna do monthly analyses.
If it's above ninety-nine percent, we're gonna call that a trigger.
We're gonna say it's futile if the probability is greater than
ninety-five percent that the odds ratio is less than one point two.
That was considered a reasonable clinical threshold where we're gonna
say the effect is a-at best small.
And harm is a ninety-nine percent chance that it has an odds ratio less than one.
So that's the design.
Single Bayesian analysis combining all these patients together.
A critical part of the primary analysis is a Bayesian hierarchical model that
allows dynamic borrowing, and the model was really a model that borrows moderate
patients, low and high, and missing D-dimer are part of a hierarchical model.
The mean of that model is in a hierarchical model with the severe effect.
So it's this two-tiered hierarchical model where the moderate D-dimer levels can
borrow from each other because they're all within the same disease state of
moderate And then the moderate and severe effects can borrow if they're similar.
Okay.
Covariates adjusted in the model, regions, sites, age, critically important, time.
Uh, there are no non-concurrent controls here.
Uh, all randomized concurrent controls within them, but we adjust
for time because it's such an important thing within the pandemic.
It-it-- Both for disease variations, but also because of, of, uh, times of
surge where maybe outcomes are different.
Okay.
In the first adaptive analysis, November 20th, twenty-twenty, no...
I-I'm blinded to all of this.
Uh, no triggers are met.
We're told to continue.
Um, the DSMBs all meet, no triggers are met.
On December nineteenth, six days before Christmas, twenty-twenty,
adaptive analysis number two occurs, and a severe state triggers hit.
Within the hierarchical model, there are twelve hundred and seven patients are
met, and therapeutic anticoagulation meets futility, meaning ninety-five percent
chance or higher that the effect has an odds ratio less than one point two.
Those results are publicly disclosed, but the-- no paper comes out.
Uh, randomization to severe is stopped in all three platforms, and publicly
disclose this, this futility trigger.
So patients immediately in all three of these global platform
trials stop randomizing to therapeutic anticoagulation.
Now, the, the, the message is continue in the moderate state.
So all three platforms are still enrolling in the moderate state.
We don't know the answer yet.
Adaptive analysis number three happens January 22 2021
At that analysis, superiority for therapeutic anticoagulation
in the moderate state is met for both high and low D-dimer groups.
2,200 patients, just over 2,200 patients go into that analysis, and superiority
greater than a 99 chance therapeutic anticoagulation is beneficial in
patients in the moderate state And it's the same conclusion for D-dimer.
vary by D-dimer.
Randomization is stopped in each of the three platforms.
Those, uh, results are announced.
The, the data aren't disclosed to the level of typical publications.
That comes out.
So I, I, I, I'll, I'll come back to that.
So two papers are published in the New England Journal of Medicine, and they
are published side by side, and they come out in the summer of twenty twenty-one.
And you can, uh...
easy to find.
Therapeutic anticoagulation with heparin in critically ill, that's
what they refer to the steer-- severe state, patients with COVID-19.
And then therapeutic anticoagulation with heparin in non-critically
ill patients with COVID-19.
Back-to-back papers in the New England Journal of Medicine report out on
this, and the fascinating thing about that is the data are the same.
One model is run, and the results are presented in two different papers.
And, um, the result of each is done separately, yet the Bayesian
hierarchical model is shrinking.
Now, they're shrinking within the moderate state, potentially,
and across moderate and severe.
By the differential conclusion, you can guess, of course, that one was
futility and one was superiority.
The model, uh, learned that the effect was differential and didn't borrow much.
But yet the component that borrowed in the moderate state between D-dimer
levels did shrink those values together, enabling the conclusion to happen that,
uh, uh, therapeutic anticoagulation is beneficial in the moderate state.
Now, these conclusions, th-this effort coming together, the whole idea of this,
if they would not have come together, it's very likely these conclusions
would have been months later and may have been differential because they
didn't combine them together, leading to even more conclusi-- more confusion.
So they all come together.
They read out.
The patients are combined together.
It's done faster and more effectively by the three groups doing this together.
Now, it was an incredibly incredible operational exercise
to have this done all together.
Huge amount of time and effort done to have this happen, but it made
a huge impact in the disease Now, in the moderate state, the final
primary analysis comes out with a ninety-eight point six probability
that therapeutic dose anticoagulation is superior in the overall group, and
it's ninety-seven and, uh, ninety-three and ninety-seven in the three groups.
In the Bayesian hierarchical model, the estimate o-overall is about a one
point two seven, one point three one, one point two two, one point three two.
So one point two seven is the, uh, uh, moderate state estimate
with the bottom of the c-credible interval being one point zero three.
So statistically significant with that ninety-eight point six
probability, it's beneficial for patients in the moderate state.
Now, in the severe state, the probability is ninety-five percent that it is harmful.
That-- And the odds ratio is point eight three.
We set up the odds ratio, so an odds ratio less than one were negative outcomes.
Increased mortality and organ support through-throughout the scale.
Ninety-nine point nine percent probability it was futile, less than one point two.
The top of the ninety-five percent credible interval is one point zero three,
exactly where the moderate state bottom of the interval was, one point zero three.
Ninety-nine point nine probability of futility, ninety-five
percent probability of harm of therapeutic dose anticoagulation.
So this effort was unbelievable in coming together, the impact of this.
The result is an amazing thing of these trials together.
And let me sort of...
The, the, the role of borrowing within these analyses and the
role of prospectively identifying, these were not post hoc subgroups.
These were the analysis plan started in 2015 actually, in the Remap-Cap trial
and adopted by all for this domain.
If you would have pooled the data and said, "We want one conclusion
in COVID-19," the odds ratio, pooling those groups together,
adjusting for all of the states and covariates, would have been 1.03
Essentially 1 with the bottom of the interval 0.85
the top 1.22
It would have reached a conclusion of futility, uh, a-and and no difference.
Therapeutic anticoagulation doesn't matter If you would have pooled them
all together in these two groups.
Remember the conclusion through the Bayesian hierarchical
model was harm, likely harm, in severe, benefit in moderate.
Guidelines adopted these results.
There's a lot of communication to guideline results that these weren't post
hoc, prospectively set up in the SAP.
Because of that, therapeutic anticoagulation is given to
patients in moderate state.
It is not in severe.
Unclear what happens if they don't do this differential
analysis, hierarchical modeling, uh, of these results within this.
Okay.
Uh, I, I-- and again, go to these papers and look them up.
Uh, uh, it was amazing effort within that.
The, the, the, the take-home of this is a brand new entity, multi-platform
randomized clinical trials.
We are talking about this in a number of other efforts.
So this continues to go on that there's multiple separate funded platforms that
are investigating the same question.
Rather than them, the two of them compete, be the first one out, let's combine our
data together and have one conclusion.
It's a more heterogeneous patient population, bigger data, faster results.
During this effort in COVID, it was an amazing effort, amazing effort.
Differential HTE, the Bayesian modeling completely changes the result around.
Benefit and harm rather than it doesn't matter, which had been the answer
if that was not done in the trials.
All right.
I hope you enjoyed this look back into COVID.
Um, we-- lots of people jumper- jumping in in effort.
The, the scientific, uh, aspects of this are very much worth revisiting,
even though COVID is, is not nearly the, the, um, uh, medical
challenge that it was at the time.
Many scientific lessons, platform trials, amazingly successful.
And here is a new entity called the multi-platform randomized clinical trial.
You heard about those interim analyses.
We stay here in the interim.
Thanks for joining.
Until next time