Hello. I’m Melanie Quintana, a

senior statistical scientist at Berry Consultants. At Berry Consultants,

we design a wide range of clinical trials,

most of which involve an individual

subject being randomized to a single treatment

or placebo and being followed over a period of time. However, there are

situations in clinical trials where we want to allow

individual subjects to receive multiple treatments. And that’s what we’re

going to talk about today in this short video– why we do this, when we do this,

and some key considerations that we want to think

about when we’re doing it. In particular, I’m going to

talk about two types of designs. The first is the

one you probably think about most when you

think about multiple treatments within the same subject. And that’s a randomized

controlled crossover design. The second is often

called treatment switching to differentiate it from a

randomized controlled crossover design. And it’s when an

individual is allowed to switch, or crossover, based

on a period of progression or non-response. But let’s start with randomized

controlled crossover, a very simple what’s

often called AB/BA design. In these types of designs,

each individual patient receives each treatment

that’s being studied. And the order in which they

receive those treatments is normally randomized. Why we do this is so that

every participant can serve as their own control, we

can reduce variability, and we can increase power. Within these types

of studies, a period in which you receive the

treatment is called a period. And each period tends to be

prespecified in length, so, say, six months

for each treatment. And those periods tend to be

the same for each treatment. There could be two

periods, or 20, 30, up to 100 periods of

different types of treatments given to a single individual. Between periods, we tend to have

a washout period so that we can mitigate any carryover effects. These are effects of

the first treatment that was given carrying

over into the second period. Some key considerations

that we want to think about in the randomized

controlled crossover design are, in fact, this

carryover effect. We want to make sure that

the washout period itself is substantial enough

to limit any effect that that treatment might

have in the second period. Another thing that we want

to think critically about is, is there a period

or an order effect? So if you give a

treatment in period one, would we expect the effect

of that to be different if it was given in period two? Does it matter the order that

the treatments were given? Oftentimes what we do to

mitigate issues with this is we want to ensure that

we have a disease that’s chronic and stable so

that the disease itself is constant over the entire trial. The final thing that

we want to think about is the timing of

the outcome itself. Oftentimes in a randomized

controlled crossover study, we’re thinking about short-term

outcomes that are completely contained to each period. So, for instance,

change from baseline over the six-month period. Under these ideal situations, so

no carryover effect, no period effect, a nice short

outcome that we can observe at each period, we

can see a substantial increase in power between

a crossover design and, say, a parallel design. Here in this plot, I’m

showing the sample size needed for 80% power on the y-axis. And on the x-axis, I have

the treatment effect itself that we’re assuming. Here, these are standardized

so that a 0.5 means 0.5 standardized unit different

between treatment and control. If you look at the 0.5

standardized unit difference, you can see that we

need a little bit less than 50 subjects for

a crossover design compared to over 125 subjects

for a parallel design. So crossover designs

in this ideal situation allow us to get away

with less than half of the same participants. Now, there can be some

negatives to a crossover design. One is that every patient

receives every treatment. So that means if we’re thinking

about a placebo controlled trial, that every

patient will be receiving a placebo for

some period of time. And this may really not be ideal

to subjects entering the trial and may limit the subjects

who want to enter. Another issue is that

these types of trials could take longer in terms of

the duration of the full trial, because each treatment

period is sequential instead of run in parallel. These are done many

times in practice. This is just one example

of a press release from Axsome Therapeutics. This was released in December. They ran a crossover design. They had two two-week

periods, and in between that, a

one-week washout. And this was a design to

look at their efficacy of their drug in narcolepsy. And what they saw was a

statistically significant reduction in cataplexy

events, or attacks, in the treatment period

compared to the placebo period. So now that we’ve talked about

the randomized controlled crossover design, I want to

switch gears a little bit to talk about another type

of design often called treatment switching. And these designs come up

when an individual switches, or crosses over, to another

treatment after a period of non-response or progression. Oftentimes, these are one-way

switching, so a switching from, say, placebo

to active treatment. And these are done to

address ethical issues and increase study participation

so that everybody knows that if there is a

period of non-response or they’re performing

badly on placebo, they will receive

an active therapy. Oftentimes, these are also

done to actually learn what looks best both in

first-line and second-line therapy. So instead of there just

being a one-way switch, where a subject goes from

placebo to active therapy, there could be randomization

post-progression to learn what all possible second-line

therapies work best. Within treatment

switching, you can start to see that things get

a little bit more complicated than the nice randomized

controlled crossover design. For one, the time of the

periods could be very different. And they depend on when

a subject progresses. So for each subject, they

could be in period one, say, for one month, or another

subject may come in and never go into period two– stay in

period one the entire time. Another issue is that

a subject switches based on an intermediate

outcome, not the final outcome that we’re most interested in. So we are not able to

observe the final outcome and have it be contained

within each treatment period. So all of the same

considerations from randomized controlled

crossover studies still hold with

treatment switching. But we also want to account

for when we switched and what treatments we switched

to in the primary analysis, because switching on

this intermediate outcome before we observe the

final outcome may actually dilute the overall

treatment effect that we are able to observe. Imagine if everyone on

placebo switches over to the active treatment

after a month, then those two

groups are probably going to end up

looking pretty similar. So we need to be very

careful and adjust for different effects

of the switching. We need to be able to adjust

for any potential carryover effect from the first-line

treatment to the second-line. We need to be able to

understand if a treatment is different in the first line

versus the second line. Is that effect different? And then we need to

take into consideration the amount of time that each

patient is in each period. Now, treatment switching is

done many times in oncology. This is just one example of

when it’s done in practice. This is the Precision Promise,

Pancreatic Cancer Platform Trial. And this is a trial that’s

in– a perpetual trial that’s interested in looking

at many therapies over the course

of time and which ones help pancreatic cancer. In this trial, we have

adaptive randomization to receive a first-line therapy. And then if an

individual progresses, there’s adaptive

re-randomization to the best performing

second-line therapy. This trial is

meant to understand which therapies work

best in first line, as well as which work

best in second line, and what are the sequence

of therapies that work best. And the statistical

analyses themselves allow differential effects

in the first and second-line therapies. So now that we’ve talked about

two main types of designs with multiple

treatments, I just want to acknowledge that there

are many more designs that involve multiple treatments. One that’s gaining popularity

is a dynamic treatment regimen, or what people are

calling a smart design. And this also involves

re-randomization to subsequent treatments based

on intermediate outcomes. And also, we’re

interested in learning what is the best and most

ideal sequence of therapies. And finally, where

this is coming up is also in perpetual,

or platform, trials. So platform trials

themselves are meant to be these freestanding

arenas where drugs come in and are

tested, and then they come out to see which ones

are the most effective within a certain disease. And what we’re seeing happen in

these platform trials is that after your initial

period of enrollment and serving to test

for a single regimen, a subject may

still after they’re finished with that period be

able to re-enroll in the trial and have a repeat randomization. And we need to take into

consideration this repeat randomization. In summary, I hope

you’ve seen today that there are many valid

reasons for allowing an individual subject to

receive multiple treatments over the course of

a clinical trial– for increase in power,

for ethical reasons, and also to purely understand

what sequence of treatments work best. Across the board

when we’re dealing with a trial of

this nature, we need to make sure we clearly

document and report the multiple treatments

that each subject receives so that the statistical analyses

themselves can carefully consider the impact that

these multiple treatments have on the final endpoint. Thank you all for watching

this short video today. I hope you learned a

little bit something more about multiple treatments

within the same subject. And please go to

our website to watch some more of these videos.