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Susan S. Ellenberg, PhD, discusses stepped-wedge clinical trials and evaluation by rolling deployment.
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Susan S. Ellenberg, PhD, discusses stepped-wedge clinical trials and evaluation by rolling deployment.
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[ Music ] >> This is Ed Livingston with JAMAevidence, and in the JAMA Guide to Statistics and Methods, there's a chapter entitled The Stepped-Wedge Clinical Trial and it was written by Dr. Susan Ellenberg from the University of Pennsylvania.
And Dr. Ellenberg has joined me today to talk about this type of trial design. Dr. Ellenberg, in your chapter, you describe stepped-wedge trials in the context of cluster randomized trials. So could you start by explaining to us what a cluster randomized trial is? >> Sure. A cluster randomized trial is the kind of trial you do when it's not really feasible, or completely impractical, or it's completely impractical to randomize individual participants.
You really need to randomize in groups. This kind of design was thought of for interventions like something that might be presented in a classroom, maybe presenting it in a group and you want to see which educational intervention might work best. Obviously, you can't randomize individual people. You've got to randomize classrooms. But there are many circumstances in medical research where a cluster randomized design is useful. For example, a particular intervention that you might put into place in a clinic to, say, control infections, control nosocomial infections.
There are other kinds of things where it would be difficult to do it individually for each individual participant. You're going to count up the individual events. You're going to count up the number of nosocomial infections, but it wouldn't really be practical to randomize individual patients to that intervention. It would have to be a clinic or a unit on a floor or something like that. So cluster randomized designs are fairly new. I think the first paper written about these, to put some kind of formal structure in it, was not until the late 1970s, but they're been particularly appealing in the growing interest in pragmatic trials because it facilitates doing many kinds of pragmatic trials.
>> So what's the difference between a cluster trial and a stepped-wedge trial? >> A stepped-wedge trial is a type of cluster trial. So you still have clusters. You're including clusters as your unit of analysis, but the difference is in a standard cluster trial, clusters would be randomized to an intervention versus a control. In a stepped-wedge trial, everybody is ultimately going to get the intervention and the randomization is when the cluster gets the randomization. So if you have, say, 12 clusters, you start off, first observation period everybody is getting the control intervention.
And then after some period of time, depending on what it is that you're studying, will depend on how long that observation time needs to be. Then some subset of the clusters will then move to the intervention and they'll get the intervention for the rest of the time of the study. And then after another period of time another set of clusters will move from the control to the randomization. So by the end of the study, everybody will have been exposed to the intervention, and the comparison will be within clusters, the before and after.
What happened to this cluster while they were on the control intervention compared to what happened to the cluster when they were on the experimental intervention? So in that sense, it's a little bit like a crossover trial, so that the clusters are crossing on. >> In your chapter, you point out that there are four things to consider when designing a cluster trial. There are the number of clusters, the number of steps, the treatment duration, and the balance of prognostic factors. Could you walk me through those four steps and what they are and how you consider them when designing these trials?
>> Well, they all factor into determining how big the study needs to be. With any kind of a cluster design, whether it's a normal cluster design or a stepped-wedge trial, you lose efficiency because people within a cluster are more similar to each other than they are to people outside the cluster, so you don't get as much bang for the buck from each individual person getting the intervention or the control as you would. So that has to be factored in. You don't just look at the number of people.
You have to adjust for the fact that there's going to be this correlation within the cluster. But the size of the clusters does matter to some extent, so that has to be considered in figuring out what sample size there has to be. The number of steps is important. The more steps you have, the more power you get because you have more people changing from time to time. Think about it. If you just had one step, like half the people got the control and then you changed and then everybody got the intervention, it would be like a crossover trial with only two periods.
You wouldn't have as much information as you have with multiple steps, so you have that. Duration? Of course, that's going to depend on what it is that you're implementing. If you're studying something that's a chronic condition where you have to observe people for a certain period of time in order to see whether the intervention is doing anything, whether it's reducing the number of episodes or reducing the symptoms, you will have a certain amount of time that you will have to observe each cluster for and maybe you don't need so much time if you're looking to see if you're preventing some kind of an acute event.
So that has to be factored into the study. >> The fourth was balance the prognostic factors, and admittedly, I didn't understand that one. >> Yeah. So balance of prognostic factors. So when we do an individual randomized trial, we like to think that the randomization is going to make everything balanced, right? You'll have the same number of men and women on each arm. You'll have comparable age distribution. And of course, the beauty of randomization is that you could expect it to balance even the things that we are not measuring or that we don't even know we should measure with regard to prognosis.
And that same concept is with a cluster randomized trial, except that the number of clusters in a cluster randomized trial, or a stepped-wedge trial, is going to be smaller than the number of individuals in an individually randomized trial. So whereas if you have an individually randomized trial of several hundred people, you can be pretty sure things are going to be pretty well balanced. But in a cluster randomized trial, you're very unlikely to have 200 or 300 clusters.
You may have 15 or 20 clusters, and so you'd be more worried. Just like in an individually randomized trial, if you only had 20 people randomized 10 to each arm, you wouldn't be all that sure that everything was all that well balanced. So we do have to worry about that in cluster randomized trials.
One thing that people may do is that they will try to match clusters and then, say, take two clusters that will have patients generally from the same geographic area or are likely to have similar socioeconomic status or some other factor that is likely to affect prognosis, and you will -- put those two as pairs and then you'll basically flip a coin and have one randomized to one group and one randomized to the other.
That's a little more complicated in a stepped-wedged trial because you're not just randomizing to one group or the other. You're randomizing over time. But you can do the same kind of thing, trying to figure out who's getting the intervention at different times. It's a little less problematic, actually, in a stepped-wedge trial because, since you're looking at the same cluster before and after for your main analysis, you're controlling through that to some extent because whatever is prognostically important for this particular cluster, you know, that's where the comparison is going to be within that cluster.
But you still have to worry about it to some extent because the people that you're observing in the intervention part of that cluster might not be similar to the people who were treated and the kind they were getting on control. And so you do have to keep track of the prognostic characteristics of the people of the study and try and adjust through that. >> Just to clarify, the unit of analysis is the cluster and not the individual patient. Is that correct? >> That's right. That's right.
That's really important. >> Yeah, because that comes up all the time in manuscripts and people try to analyze individual patients when they've done these cluster-type trials, and that just doesn't work. The other -- also to clarify, when you talk about the number of steps, if there's, say, 12 clusters and at one point of time you provide the intervention to six of them and then another point of time it's six of them. That's two steps. If you have the 12 clusters and let's say every month you provide the intervention to one of the clusters over 12 months, that would be 12 steps.
Is that correct? >> That's right. And the more steps you have, the more power you have, the more efficient it is because you are controlling to some extent for this variability. But it's often more complex for the people running the trial to have lots and lots of steps. So it's more typical, for example, if you had 12 clusters to have maybe two at a time go or even three at a time. That would be a more common design.
The other really important thing with stepped-wedge designs is the time factor. As I explained, people are getting exposed to the intervention at different times along the way, so there are some clusters that will only have one period of time where they're getting the control and for all the rest of the time they're getting the intervention and there will be some clusters that are getting the control for most of the time and maybe only at the very end they're getting the intervention. Now, what happens if things change over time with regard to the outcome you're looking for?
Maybe some new background supportive care is introduced and suddenly everybody is using that halfway through the trial. That is going to confound things with people who get the intervention later and also the people in the control may be doing better than those people who are getting it earlier. And so in analyzing these, that's not something we can really control. If there's something that's going to improve people's health, we can't say you can't introduce it.
But there are ways to adjust for that time factor in the analysis. So the analyses of these trials is very complex and much too complex to go into on this kind of a podcast. But one has to not only account for the cluster effect, the fact that each cluster may be more similar, the people in it may be more similar to each other than to other clusters, but you also have to adjust for the time effect. >> In the original article published in JAMA on this topic, there was a summary of an article that used a stepped-wedge design.
And one of the interesting things that came up is problems in obtaining informed consent of the participants in such a trial. If you're randomizing an entire cluster, you have to somehow get informed consent or wave consent. And if you do have to get consent and a few patients choose not to do so, it can cause selection bias and that's what happened. Are there other little caveats like that that investigators and readers should know about that are unique or peculiar to cluster randomized trials? >> Well, the informed consent issue is an important factor and sometimes we see people proposing to do some kind of a cluster design, I think, feeling like it's going to be easier to avoid the informed consent process.
And there are a number of cluster designs where it's probably reasonable not to do an individual consent. So this is often attention in designing these trials. Another issue and one reason why stepped-wedge designs might be attractive in some circumstances is when the intervention really can't be available to everybody, you know, right at the outset. If it's a behavioral intervention, you may have trained individuals, trained researchers who need to present and explain the intervention to the people carrying out the trail at the different sites, and they may not be able to do that all at once.
So that's a reason why you would have to spread it out. It also might be that if you're going to be studying a drug or a vaccine where the supplies are somewhat limited and it's going to take a while to get enough available, that could be another reason for doing a stepped-wedge trial. You can roll out the intervention bit by bit as you get more of the supplies of the intervention. >> I think we've covered quite a lot about stepped-wedged designs. Is there anything else you think that we should talk about?
>> One thing that is often appealing to researchers about a stepped-wedge design is that they can tell participants that eventually they'll get the intervention. It's not like a regular randomized trial or even a regular cluster randomized trial where you're either assigned to the treatment or you're assigned to the control. Everybody will ultimately get access to the treatment and that can be appealing, both to the people conducting the trial and the people who are going to be in the trial.
And so in that light, one might say, well, who's going to get it first? The fairest way is to do it by a random process. I think these kinds of designs probably have been used in the past very ad hoc where the material wasn't available to everyone right away. They said, OK, we'll get it to this person's group first and this group second and kind of ad hoc but without the statistical structure that allows you to be able to be more likely to draw a reliable conclusion.
And there's been a fair amount of statistical work on the design and analysis of stepped-wedged trials over, I would say, the last 20 years. So now we can do these trials when they make sense, when the problem at hand is such that a stepped-wedge design is really the best way to approach the trial. We know how to design and analyze it. We know how to figure out how many people we would need, how many clusters we would need. And when we get the results, we understand better how to analyze the data, accounting for the clustering, accounting for the time factor, and so that's one reason why these designs have become more popular because they can be done in a reliable and reproducible way.
>> I guess the message is don't do this at home. Make sure that before you try to do a cluster randomized trial, you get a statistician who knows how to do one of these complex analyses on board on your study from the very beginning. >> That's exactly right! And I'm glad you went on after the word statistician because this is not an area that probably most statisticians are familiar with. You really need to get somebody who's had experience with these designs. There's actually been a recent consort extension for stepped-wedge designs that could be very helpful for people thinking about doing one of these trials.
It was published in VMC, the Journal Trials in -- last year in 2019. So I would say that would be a good place to look for anybody trying to design these trials. Of course, the consort really tells you how to report them. But it does give you, you know, knowing what you're going to have to report can help you figure out what you need to do in the first place. >> And you can find the reference to that consort extension at the EQUATOR Network, and that's EQUATOR-Network.org. >> Yes.
>> So that's a very helpful place to find these kinds of summary documents about how to design, interpret, and write about trials. >> That's right. And that paper does have a number of references to some of the specifics of the design and analysis of stepped-wedged designs, which should also be useful. I think, you know, my own point of view is that the most efficient way to address a particular medical question is in an individually randomized clinical trial.
But as I said in the beginning, there are some kinds of questions that simply can't be studied that way, and so it's really good that we have these alternative approaches that we can use when a cluster has to be the unit of randomization or when you can't roll everything out at the same time or whether it's important to assure people that sooner or later they will get the intervention. There are all these considerations. And we can now have these designs that allow us to meet the demands of these kinds of situations.
But you know, they shouldn't be used just because people think it's going to be easier or it's going to help them avoid having to do informed consent, because they're much less efficient, so that's the downside. >> I'd like to thank Dr. Ellenberg from the Department of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine at the University of Pennsylvania in Philadelphia for explaining stepped-wedge trials for JAMAevidence.
This is Ed Livingston for JAMAevidence. Go to JAMAevidence.com to find a lot of information about the JAMA Guide to Statistics and Methods, the Users' Guide to the Medical Literature, The Rational Clinical Exam, and other content. You'll also find an array of podcasts explaining concepts in medical research that clinicians should know about. [ Music ]
Segment:0 .
[ Music ] >> This is Ed Livingston with JAMAevidence, and in the JAMA Guide to Statistics and Methods, there's a chapter entitled The Stepped-Wedge Clinical Trial and it was written by Dr. Susan Ellenberg from the University of Pennsylvania.
And Dr. Ellenberg has joined me today to talk about this type of trial design. Dr. Ellenberg, in your chapter, you describe stepped-wedge trials in the context of cluster randomized trials. So could you start by explaining to us what a cluster randomized trial is? >> Sure. A cluster randomized trial is the kind of trial you do when it's not really feasible, or completely impractical, or it's completely impractical to randomize individual participants.
You really need to randomize in groups. This kind of design was thought of for interventions like something that might be presented in a classroom, maybe presenting it in a group and you want to see which educational intervention might work best. Obviously, you can't randomize individual people. You've got to randomize classrooms. But there are many circumstances in medical research where a cluster randomized design is useful. For example, a particular intervention that you might put into place in a clinic to, say, control infections, control nosocomial infections.
There are other kinds of things where it would be difficult to do it individually for each individual participant. You're going to count up the individual events. You're going to count up the number of nosocomial infections, but it wouldn't really be practical to randomize individual patients to that intervention. It would have to be a clinic or a unit on a floor or something like that. So cluster randomized designs are fairly new. I think the first paper written about these, to put some kind of formal structure in it, was not until the late 1970s, but they're been particularly appealing in the growing interest in pragmatic trials because it facilitates doing many kinds of pragmatic trials.
>> So what's the difference between a cluster trial and a stepped-wedge trial? >> A stepped-wedge trial is a type of cluster trial. So you still have clusters. You're including clusters as your unit of analysis, but the difference is in a standard cluster trial, clusters would be randomized to an intervention versus a control. In a stepped-wedge trial, everybody is ultimately going to get the intervention and the randomization is when the cluster gets the randomization. So if you have, say, 12 clusters, you start off, first observation period everybody is getting the control intervention.
And then after some period of time, depending on what it is that you're studying, will depend on how long that observation time needs to be. Then some subset of the clusters will then move to the intervention and they'll get the intervention for the rest of the time of the study. And then after another period of time another set of clusters will move from the control to the randomization. So by the end of the study, everybody will have been exposed to the intervention, and the comparison will be within clusters, the before and after.
What happened to this cluster while they were on the control intervention compared to what happened to the cluster when they were on the experimental intervention? So in that sense, it's a little bit like a crossover trial, so that the clusters are crossing on. >> In your chapter, you point out that there are four things to consider when designing a cluster trial. There are the number of clusters, the number of steps, the treatment duration, and the balance of prognostic factors. Could you walk me through those four steps and what they are and how you consider them when designing these trials?
>> Well, they all factor into determining how big the study needs to be. With any kind of a cluster design, whether it's a normal cluster design or a stepped-wedge trial, you lose efficiency because people within a cluster are more similar to each other than they are to people outside the cluster, so you don't get as much bang for the buck from each individual person getting the intervention or the control as you would. So that has to be factored in. You don't just look at the number of people.
You have to adjust for the fact that there's going to be this correlation within the cluster. But the size of the clusters does matter to some extent, so that has to be considered in figuring out what sample size there has to be. The number of steps is important. The more steps you have, the more power you get because you have more people changing from time to time. Think about it. If you just had one step, like half the people got the control and then you changed and then everybody got the intervention, it would be like a crossover trial with only two periods.
You wouldn't have as much information as you have with multiple steps, so you have that. Duration? Of course, that's going to depend on what it is that you're implementing. If you're studying something that's a chronic condition where you have to observe people for a certain period of time in order to see whether the intervention is doing anything, whether it's reducing the number of episodes or reducing the symptoms, you will have a certain amount of time that you will have to observe each cluster for and maybe you don't need so much time if you're looking to see if you're preventing some kind of an acute event.
So that has to be factored into the study. >> The fourth was balance the prognostic factors, and admittedly, I didn't understand that one. >> Yeah. So balance of prognostic factors. So when we do an individual randomized trial, we like to think that the randomization is going to make everything balanced, right? You'll have the same number of men and women on each arm. You'll have comparable age distribution. And of course, the beauty of randomization is that you could expect it to balance even the things that we are not measuring or that we don't even know we should measure with regard to prognosis.
And that same concept is with a cluster randomized trial, except that the number of clusters in a cluster randomized trial, or a stepped-wedge trial, is going to be smaller than the number of individuals in an individually randomized trial. So whereas if you have an individually randomized trial of several hundred people, you can be pretty sure things are going to be pretty well balanced. But in a cluster randomized trial, you're very unlikely to have 200 or 300 clusters.
You may have 15 or 20 clusters, and so you'd be more worried. Just like in an individually randomized trial, if you only had 20 people randomized 10 to each arm, you wouldn't be all that sure that everything was all that well balanced. So we do have to worry about that in cluster randomized trials.
One thing that people may do is that they will try to match clusters and then, say, take two clusters that will have patients generally from the same geographic area or are likely to have similar socioeconomic status or some other factor that is likely to affect prognosis, and you will -- put those two as pairs and then you'll basically flip a coin and have one randomized to one group and one randomized to the other.
That's a little more complicated in a stepped-wedged trial because you're not just randomizing to one group or the other. You're randomizing over time. But you can do the same kind of thing, trying to figure out who's getting the intervention at different times. It's a little less problematic, actually, in a stepped-wedge trial because, since you're looking at the same cluster before and after for your main analysis, you're controlling through that to some extent because whatever is prognostically important for this particular cluster, you know, that's where the comparison is going to be within that cluster.
But you still have to worry about it to some extent because the people that you're observing in the intervention part of that cluster might not be similar to the people who were treated and the kind they were getting on control. And so you do have to keep track of the prognostic characteristics of the people of the study and try and adjust through that. >> Just to clarify, the unit of analysis is the cluster and not the individual patient. Is that correct? >> That's right. That's right.
That's really important. >> Yeah, because that comes up all the time in manuscripts and people try to analyze individual patients when they've done these cluster-type trials, and that just doesn't work. The other -- also to clarify, when you talk about the number of steps, if there's, say, 12 clusters and at one point of time you provide the intervention to six of them and then another point of time it's six of them. That's two steps. If you have the 12 clusters and let's say every month you provide the intervention to one of the clusters over 12 months, that would be 12 steps.
Is that correct? >> That's right. And the more steps you have, the more power you have, the more efficient it is because you are controlling to some extent for this variability. But it's often more complex for the people running the trial to have lots and lots of steps. So it's more typical, for example, if you had 12 clusters to have maybe two at a time go or even three at a time. That would be a more common design.
The other really important thing with stepped-wedge designs is the time factor. As I explained, people are getting exposed to the intervention at different times along the way, so there are some clusters that will only have one period of time where they're getting the control and for all the rest of the time they're getting the intervention and there will be some clusters that are getting the control for most of the time and maybe only at the very end they're getting the intervention. Now, what happens if things change over time with regard to the outcome you're looking for?
Maybe some new background supportive care is introduced and suddenly everybody is using that halfway through the trial. That is going to confound things with people who get the intervention later and also the people in the control may be doing better than those people who are getting it earlier. And so in analyzing these, that's not something we can really control. If there's something that's going to improve people's health, we can't say you can't introduce it.
But there are ways to adjust for that time factor in the analysis. So the analyses of these trials is very complex and much too complex to go into on this kind of a podcast. But one has to not only account for the cluster effect, the fact that each cluster may be more similar, the people in it may be more similar to each other than to other clusters, but you also have to adjust for the time effect. >> In the original article published in JAMA on this topic, there was a summary of an article that used a stepped-wedge design.
And one of the interesting things that came up is problems in obtaining informed consent of the participants in such a trial. If you're randomizing an entire cluster, you have to somehow get informed consent or wave consent. And if you do have to get consent and a few patients choose not to do so, it can cause selection bias and that's what happened. Are there other little caveats like that that investigators and readers should know about that are unique or peculiar to cluster randomized trials? >> Well, the informed consent issue is an important factor and sometimes we see people proposing to do some kind of a cluster design, I think, feeling like it's going to be easier to avoid the informed consent process.
And there are a number of cluster designs where it's probably reasonable not to do an individual consent. So this is often attention in designing these trials. Another issue and one reason why stepped-wedge designs might be attractive in some circumstances is when the intervention really can't be available to everybody, you know, right at the outset. If it's a behavioral intervention, you may have trained individuals, trained researchers who need to present and explain the intervention to the people carrying out the trail at the different sites, and they may not be able to do that all at once.
So that's a reason why you would have to spread it out. It also might be that if you're going to be studying a drug or a vaccine where the supplies are somewhat limited and it's going to take a while to get enough available, that could be another reason for doing a stepped-wedge trial. You can roll out the intervention bit by bit as you get more of the supplies of the intervention. >> I think we've covered quite a lot about stepped-wedged designs. Is there anything else you think that we should talk about?
>> One thing that is often appealing to researchers about a stepped-wedge design is that they can tell participants that eventually they'll get the intervention. It's not like a regular randomized trial or even a regular cluster randomized trial where you're either assigned to the treatment or you're assigned to the control. Everybody will ultimately get access to the treatment and that can be appealing, both to the people conducting the trial and the people who are going to be in the trial.
And so in that light, one might say, well, who's going to get it first? The fairest way is to do it by a random process. I think these kinds of designs probably have been used in the past very ad hoc where the material wasn't available to everyone right away. They said, OK, we'll get it to this person's group first and this group second and kind of ad hoc but without the statistical structure that allows you to be able to be more likely to draw a reliable conclusion.
And there's been a fair amount of statistical work on the design and analysis of stepped-wedged trials over, I would say, the last 20 years. So now we can do these trials when they make sense, when the problem at hand is such that a stepped-wedge design is really the best way to approach the trial. We know how to design and analyze it. We know how to figure out how many people we would need, how many clusters we would need. And when we get the results, we understand better how to analyze the data, accounting for the clustering, accounting for the time factor, and so that's one reason why these designs have become more popular because they can be done in a reliable and reproducible way.
>> I guess the message is don't do this at home. Make sure that before you try to do a cluster randomized trial, you get a statistician who knows how to do one of these complex analyses on board on your study from the very beginning. >> That's exactly right! And I'm glad you went on after the word statistician because this is not an area that probably most statisticians are familiar with. You really need to get somebody who's had experience with these designs. There's actually been a recent consort extension for stepped-wedge designs that could be very helpful for people thinking about doing one of these trials.
It was published in VMC, the Journal Trials in -- last year in 2019. So I would say that would be a good place to look for anybody trying to design these trials. Of course, the consort really tells you how to report them. But it does give you, you know, knowing what you're going to have to report can help you figure out what you need to do in the first place. >> And you can find the reference to that consort extension at the EQUATOR Network, and that's EQUATOR-Network.org. >> Yes.
>> So that's a very helpful place to find these kinds of summary documents about how to design, interpret, and write about trials. >> That's right. And that paper does have a number of references to some of the specifics of the design and analysis of stepped-wedged designs, which should also be useful. I think, you know, my own point of view is that the most efficient way to address a particular medical question is in an individually randomized clinical trial.
But as I said in the beginning, there are some kinds of questions that simply can't be studied that way, and so it's really good that we have these alternative approaches that we can use when a cluster has to be the unit of randomization or when you can't roll everything out at the same time or whether it's important to assure people that sooner or later they will get the intervention. There are all these considerations. And we can now have these designs that allow us to meet the demands of these kinds of situations.
But you know, they shouldn't be used just because people think it's going to be easier or it's going to help them avoid having to do informed consent, because they're much less efficient, so that's the downside. >> I'd like to thank Dr. Ellenberg from the Department of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine at the University of Pennsylvania in Philadelphia for explaining stepped-wedge trials for JAMAevidence.
This is Ed Livingston for JAMAevidence. Go to JAMAevidence.com to find a lot of information about the JAMA Guide to Statistics and Methods, the Users' Guide to the Medical Literature, The Rational Clinical Exam, and other content. You'll also find an array of podcasts explaining concepts in medical research that clinicians should know about. [ Music ]
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S: Segments Tab
F: Forward 15,
R: Rewind 15
L: Languages,
P: Playspeed
M: Mute Toggle,
V: Volume Bar
N: Video Only Toggle,
B: Full Screen Toggle
C: Captions Toggle
Escape: Closes dialogs and menus
Z: Next Transcript Chapter
X: Previous Transcript Chapter
Q: Next Transcript Paragraph
W: Previous Transcript Paragraph
Form Title Embed Media
Entire Media
Segment(s)
Custom Clip
Start At:
End At:
Embed Size
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You Size - Responsive
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We Size - Responsive
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Picture Overlay Popup
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400 x 225
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512 x 288
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400 x 700
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768 x 1024
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1024 x 512
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1280 x 608
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Accessibility
Using forms mode, you can utilize keyboard shortcuts to make usability more efficient.
Space bar will play and pause the media.
You can press A to open the Accessibility Dialog.
This describes many other keyboard shortcuts for media controls.
Exit Clip Mode
The video is currently set-up to show just a clip from 0:00 to video end. You have requested to see a portion of the media outside of the clip.
Click OK to switch to the full media or click if you would like to stay inside the clip. You can always go back to the clip with Tools and Clip Mode