In this episode of EHA Unplugged, we will revisit a
spotlight talk from EHA 2024. Doctor Yurian Firstlaus from the
Erasmus Medical Center in Rotterdam discusses the need to
use innovative clinical trial designs in the era of
personalized medicine in hematology.
My name is Yurian Firstlaus. I'm a hematologist at the
Erasmus MCA in Rotterdam and I'm also highly involved in trial
design and within the whole form and.
Related to this topic, do some research so for those of you who
are. Aware of clinical trial design.
There are a couple of stages to consider.
In the clinical trial development and the 1st.
Part of trial. Development.
Is the idea. Everything starts with an idea
and a drug and. Opportunity to investigate.
An expert do an experiment and usually use a control and when
there is a design you need to be.
Aware of the effect size of your.
Drug and discuss. That with your statistician.
And as a result of that. There usually.
Comes a protocol and. Eventually you'll start.
Recruiting patients and when you recruit patients, there might be
room for interim analysis. When you.
Have the time and the equipment to do that and at the end you do
a final analysis which. Is usually set out at the
beginning. At the design of a trial and at
final analysis. You execute everything you.
You thought of when you designed the trial?
This is quite a. Fixed and sometimes.
Rigid process. Where there's no room for
changes during the trial and these.
Are actually exactly. Those things that might be
innovative. Which I will talk about today.
So why do we need to innovate trial designs?
We are all aware. Especially in hematology.
Of the. ERA of personalized medicine
there are multiple drugs becoming.
Available specific to. Subtypes of patients who have.
A target. Mutation, for instance, for whom
those drugs are available but in the clinical trial.
It's usually difficult to. Enroll.
Large subsets of. Patients when there's only a
few. Patients who have that.
Actual mutation. So in that era, we need to think
of other approaches to clinical trial design and also since
outcomes of our patients are getting so much better, studies
take a long time to enroll patients and as a result we need
to wait a long time for the final analysis and studies
become expensive because of that long follow up time.
So these are, I believe. Reasons to innovate or think of
innovation in clinical trial designs and there's also.
So I talked about the targeted. Treatments which are also
biomarker driven approaches and on the other hand, when talking
about time and costs. And we have.
The early endpoints which is a different.
Topic I believe. But surrogate endpoints who
might serve, for instance, for overall survival, which MRD is
for instance being used, those early endpoints might.
Actually facilitate a. Sooner end of your trial, but
that's a different topic to discuss and I'm not going to
focus on that today. There's also another methods
which is more important I believe is early intervention.
For a very efficacious or a futile trial.
And which? Is getting very.
Hot in the last. Years is the.
Use of external control data for your clinical trial, which I
will. Also address.
So when we talk about personal, personalized.
Medicine in clinical trial designs there.
Are three key types of trial. Design one should consider and
the first. Is the basket.
Trial which? Is a type.
Of trial which? Usually.
Enrolls multiple diseases where there's one targeted
intervention. Specific for usually.
Mutation and that common target is is investigated in such a
trial but. Such a trial could.
Include solid oncology. Patients with also hematology
patients. And it's really a mixed bag of.
Different types of diseases. On the other hand, we have also
umbrella trials. Which is usually for a single
disease, but within. That single disease when there
are multiple targeted interventions available, those
targeted interventions are investigated in such a trial and
in in these. Types of trials.
There could even be a randomization.
Both of these trials basket. Trials and umbrella trials.
Are early phase trials usually? Phase one or phase two clinical
trials? Mostly phase two clinical trials
during the COVID-19 pandemic, platform trials have become
very. Popular where there's.
A continuously running standard of care arm where there.
Are always. Patients receiving that standard
of care and when there, when there are becoming
interventional treatments, alternative treatments
available, patients are at that point could be randomized to
that intervention and there are in the platform trial.
Usually a couple. Of interim analysis where at
that time an intervention is reassessed and when there's a
clear sign of. Efficacy or futility?
That drug, that intervention is dropped.
These trials are attractive because if you.
If you design it properly, the the standard of care arm could
run for. Multiple years.
And when available, different types of.
Drugs or. Experiments could be.
Added. During that trial, and this
serves as a sort of master. Protocol.
Which you could. Always rely.
On when adding the new treatments.
These types of trials are a bit more.
Difficult to design when you talk to your statistician, but I
really encourage you if you. Have these type of.
Treatments within your disease. Type.
To consider these. Trials because the.
Standard of continuously running, standard of care arm is
very attractive. The next thing I want to address
is, like I mentioned, the early intervention for and very.
Efficacious or futile trial? No one wants to do a.
Trial who? At the end.
Is is either. Very negative.
Or. Very positive and always comes
the question if you could have seen that coming and these type
of. Trials are called.
Adaptive trial designs and the example I want to highlight here
is based on the whole phone data and this is an example in
multiple myeloma. Where the trial?
Randomized 2 two treatment arms, a control treatment arm versus
an intervention arm, and at the design of the.
Study it was. Estimated that 668 patients
needed. To be enrolled.
And at final analysis. The result?
Was quite disappointing because there was no difference between
the experimental and the control arm.
We questioned whether we could have seen this coming and
whether. If we could.
Introduce interim analysis. Maybe have?
Changed the course in this. Trial retrospectively I.
Will show you 2 methods now. The first one is the group
sequential design. Which actually allows you.
To introduce interim analysis and AT interim analysis.
You could. You could.
Decide the based on the the boundaries.
I will show you whether a trial. Should be continued.
And in this example, we introduced 2 interim analysis.
The first interim analysis was when there were still patients
being enrolled and at the second interim analysis.
We observed. That all patients were already
included. But at 2:00.
Thirds of events we had still some follow up time to monitor
and if you for instance could consider at the first interim
analysis to stop the trial. You would actually.
Save patients from a potential futile or.
Efficacious treatment and. At the second interim analysis,
you could primarily say follow up time.
So if we. Again retrospectively.
Apply this to the. To this hopeful multiple myeloma
trial, you'll see interim analysis one, interim analysis
two, we were not able. To firmly say that this.
Trial was going to be a negative trial and we should stop the
trial. And a key concept of the group
sequential design is that when more data are being observed,
the bounds of efficacy and futility.
Are getting. Closer, In other words, your
estimate of of the data is getting more precise and
therefore you're more. Precisely to say.
If your trial is going to be still promising or whether your
trial is going to be a negative trial.
At the final analysis, we did not observe any difference.
Another approach we introduced. Here is a sample size. 3
estimation, design and what? Is really attractive here is
that when you. Are conducting your.
Trial. You're reassessing your power,
and the power is important when designing a trial because you
want to be sure you want, you want to have some.
Probability that your. Trial is actually going to be
showing a real effect and when the.
Power is very high. Above 90% usually.
That's a very favourable result and.
That's a scenario where you could consider.
Stopping your trial for efficacy.
On the other hand, when your. Trial is at.
Below 30%. Which is a probability of 30.
Percent that you're going to. Observe a real.
Difference. That's considered a futile
trial. And then usually a trial is
stopped and the promising zone is the interesting zone where
and that's. Usually set at.
Between 30 to 90%. Where the sample.
Size is being re. Estimated.
So during your trial, you. Have the feeling that this trial
might have some interest, but at that time you're not sure
definitely. Not sure whether.
Your trial is going to show that eventually and then you could
enroll additional patients. You could re estimate your
sample size and in this scenario that could have been resolved in
1/3 of additional patients in your trial.
When we calculated the conditional power, and we did
that at the second interim analysis, we found that this.
Study actually had. 17% conditional power, meaning that
this trial was very unlikely. To show efficacy.
And actually was futile and therefore in this scenario
retrospectively again. We could have decided that this
trials should have stopped. Which in this case because it
was after already. 2/3 of. Events, meaning the trial was
already completed for inclusion. But it could have saved.
Follow up time. These are the.
Adaptive trial designs. We also recently.
Used a Bayesian type of analysis to.
Investigate whether we could. Improve decision making in a
clinical trial by using a Bayesian methodology.
The basin methodology. Is has three key concepts
consisting of a prior. Which is the belief what you
have before you start on a. Trial.
And this is, for instance, a complete remission rate you
expect for a leukemia patient. Which is usually.
After induction treatment, about 80 percent, 85%.
And when you observe additional. Data in In a very positive
trial, you might. Observe a very.
High remission rate, for instance.
Updated likelihood. Your guess what you observe is
is better, so your updated. Belief is.
In between, that's the posterior.
And that's a key concept of Bayesian analysis, which.
I will show. You how that performed in our
AML analysis and this is again maybe a disappointing hopeful
example. But it helps to use these.
Examples. To investigate what what could
have changed? And if we looked in this.
Trial this was. The whole from 132 trial we
randomized almost 800 patients between a controlled treatment
and experimental treatments and we observed clearly no
difference, however, at the design of the study.
The expected hazard ratio for benefit.
Was .76. While the observed.
Hazard ratio was .99. And again, we questioned what
we've seen at this coming when we introduced interim analysis.
So that's what we did. We introduced 4 interim analysis
and when using that group, sequential design.
We actually observed that. At the third interim analysis,
the fertility bound was crossed. So at the third interim analysis
after 450 patients we using. This type of.
Adaptive designs might have considered to stop the.
Trial because efficacy. Was never going to be shown.
However, if we. Use a Bayesian analysis and one
of the strengths of Bayesian analysis.
Is that you're also able. To reinforce your control arm by
adding additional patients. And we use the previous.
Hopeful trial also including AML patients.
We actually showed. That using that Bayesian
approach after the first interim analysis.
So after 150 patients there was a 1.2%.
Probability of being. Successful of being able to show
that .76 expected hazard. Ratio we had.
At the beginning of the trial. So using a Bayesian analysis and
reinforcing your control arm, we were.
Able to show that that trial was already.
Very early, going to be a negative trial.
So these are. Examples of methods to introduce
interim analysis which might really.
Help you. During a trial.
To to be sure that you're. Able to demonstrate the beliefs
you had when you designed the trial.
External control data, I mentioned that at the beginning,
are increasingly being used, and we also investigated thinking of
the Bayesian analysis. Where you're able to.
Reinforce your control arm if you're.
Able to. Use external control.
Data whether that helps to compare to.
Reinforce your control arm. But the question is, are
external control? Data really helpful.
Are those really comparable? To your clinical trial.
So we again I used an approach. Where we.
Took the control arm of a hopeful trial and we.
Sought for. External control data to
reinforce the control arm and we identified 2 potential
alternative. Data sources.
The 1st is the. Harmony and Alliance data.
Source. And the other is a.
Truly real world data set. Of patients gathered in the in
the Netherlands by the Dutch Cancer.
Society, who also received the same.
Intensive AML induction treatments and we questioned
whether the control patients of the clinical.
Trial were actually similar to the.
External real worlds control data arms and when we looked for
baseline characteristics, we clearly, and that's not
surprising, observed differences.
Missing data was much more common in the real world data
source, but also within the harmony.
Data source and. When we performed the matching
methods, we. Observed this is overall
survival that. Outcome for the whole phone
control. Arm was actually.
Similar similar compared to the Harmony data.
But I have to acknowledge that the harmony data is basic.
Is basically a data set. Who consists?
Of the majority of clinical trial patients.
And in this case, in our analysis, we found that about 60
to 70% were actually enrolled in a clinical trial.
However, the real. Data from the Dutch Cancer.
Society. Those patients.
Clearly performed worse. 10% worse.
Survival Overall survival at two years.
When compared to hope and controls and we really believe.
That this is due to. To various reasons, but for
instance the missing performance status.
How? Fit is your patient, and is it
really a patient who could also be?
Included in a clinical trial? Is it comorbidity?
Data all all types of. Data that are not available in a
real. World data set so we are really
hesitant to firmly say that real real world data could actually.
Supplement the data in a clinical trial at this.
Stage so I hope to have show. You that innovative trial
design. Is the future.
And with the personalized medicine becoming available now
we need to. Adapt our trials according to
the. Needs to the accelerating needs
within the clinical trial development.
To deliver the right drug. For the right patient at the
right time, adaptive designs I've I've shown you could help
you. For potential and early trial
intervention. External data might reinforce
your control arm and important in that.
Perspective, but. You need to define and identify
the right and the proper external control.
Data set. And with all this, and
especially if you want to adapt your trial during the conduct of
your trial, you need a solid trial infrastructure.
Where you have. Immediate data available and who
can support the complex trial that you might design?
And with that, I would like to conclude.
Thank you.