How to Build a Patient-Reported Outcome Strategy for Clinical Trials

Julia Poritz

Senior Research Consultant, Patient-Reported Outcomes

Julia Poritz is a Senior Research Consultant at Cytel, specializing in patient-reported outcomes. She holds a PhD in Counseling Psychology from Texas A&M University and is a Licensed Psychologist with specialties in health psychology and rehabilitation psychology. Prior to joining Cytel, she worked at the University of Texas Medical Branch as a Clinical Assistant Professor in the Department of Obstetrics and Gynecology. Before this, she completed a postdoctoral fellowship at the Brain Injury Research Center at TIRR Memorial Hermann with a focus on the development and implementation of patient-reported outcomes for individuals with traumatic brain injuries. In addition to utilizing patient-reported outcome measures as part of routine clinical practice, she has also conducted research on the validation of existing psychological assessments as well as the creation and evaluation of new tools for special populations.

For links, there is a 6-part webinar series called Best Practices for Integrating Patient-Reported Outcomes in Oncology Clinical Trials, developed by the National Cancer Institute and the International Society for Quality of Life Research, available for free on the National Cancer Institute YouTube page (https://www.youtube.com/@NCIgov). 

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Transcript

00:00
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01:17
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01:29
Welcome to another episode of the Effective Statistician. Today I’m super happy to have a very nice and experienced and awesome to work with colleague here. Julia, how are you doing? I’m good, thank you. Very good and excited to talk about a topic that is very close to my heart, patient-reported outcomes. It’s really important because I think all we do in the end

02:00
is for the patients. If we can’t help the patients, well, what do we do? And so all the endpoints about station reported outcomes are one way to put a patient back into the center of clinical research. But before we dive into this topic, maybe you can introduce yourself and how you got interested in patient reported outcomes. Sure. Sure.

02:28
My name is Julia Poritz. I’m a senior research consultant at Cytel on the evidence value and access team with Alexander. And how I got interested in patient-reported outcomes probably started back in graduate school. I don’t know that we called them PROs at the time, but of course we were very interested in the patient voice, both in a clinical setting and in research setting. And so we need a way to measure the patient voice.

02:56
And so we use measures completed by patients themselves, which of course now we call patient reported outcome measures. And so much of the research I did during graduate school and then in my postdoc fellowship focused on various PIERO measures. And so if I look back on it now, it probably all began then, even if I wasn’t using that term PIEROs back then.

03:21
What has been your experience in terms of working with PROs, researching PROs during your professional career? Yeah, I would say most of my experience has been either developing measures for a special population or validating an existing measure in a special population. So prior to joining Cytel, that would have been in various health-related populations like spinal cord injury or traumatic brain injury.

03:50
And of course now at Cytil we work with a broad array of indications, although I’d say it’s primarily oncology and rare diseases. Yeah, I think that’s the topic that rare diseases, especially where you very often need to develop something new because there’s nothing established for specific needs at the patient population. So if we want to think about developing new PROs, we need to have some framework.

04:19
So could you walk us through how different aspects like actually the disease, symptoms, functioning, PROs, and ultimately also quality of life, which I think very often is also considered a PRO, as they all relate to each other. Yeah, you’re right. We definitely need some sort of framework or model to be able to consider all of these concepts together.

04:45
And one of the most widely used frameworks is the Wilson and Cleary model. This is a model of health-related quality of life. And it considers all those aspects that you just mentioned and lays out a framework for how they’re related to each other. We know that somebody’s disease or the treatment that they receive for it ultimately has an impact on their overall quality of life. But it’s probably not a straight line, right? There are all those other.

05:13
factors you mentioned in between. So the great thing about this Wilson and Cleary model is it shows how something like your disease or your treatment has an impact on symptoms that you experience. The symptoms might then have an impact on your functioning, which affects your overall perceptions of your health and wellbeing, and that ultimately has an impact on your overall quality of life.

05:37
So having a framework to think about PROs is really helpful, not just to conceptualize the relationship between all these different factors, but also to start coming up with a plan for how you can practically use PROs or PRO measures. So let’s go for an example. Yeah. Let’s say we have oncology indication, head and neck. And there is the tumor that’s obviously that area.

06:07
And now the therapy can shrink the tumor in size. Now that is really on the lowest level, directly the disease level. But what are then following the symptoms functioning, pure O’s, how does that build on, for example, this indication? Exactly. And that’s a great example. So like you said, if there is a treatment that causes reduction of the tumor, then that will have an impact on the person’s symptoms.

06:36
And so for example, there might be some localized pain that’s been caused. And so as the tumor reduction occurs, the pain can reduce. that, then you think about how that would affect their physical functioning. And once their physical functioning improves, that then has an effect on their perceptions of their health. Oh, my pain is reduced. I’m functioning better on a daily basis.

07:04
I’m starting to feel more optimistic about my overall health and wellbeing. And so then that leads into this broader concept of overall quality of life. So you could theoretically take any health condition and treatment and walk through in that sense to see the impact starting with disease and treatment all the way through symptoms, functioning, health perceptions, and then ending up with the overall impact on quality of life. so if we think about that reduces the pain,

07:34
And that maybe could have an impact on the ability to swallow, which then has an ability to eat, which then increases the quality of life, so to say. Yes, exactly. And the model also includes other things like individual characteristics, environmental characteristics, so that we keep in mind that this doesn’t all occur within a vacuum. There are other things going on in patients’ lives that will have an impact on their quality of life as well.

08:03
What could be these other factors that have an impact? Yeah, that’s a great question. So in terms of individual characteristics, if somebody maybe has a pre-existing condition prior to this head and neck cancer, that’s something to keep in mind. Is that condition still ongoing or maybe it’s resolved, but there’s some anxiety about recurrence while they’re dealing with the head and neck cancer. That’s an individual characteristic that

08:29
understandably will probably have an impact on their overall quality of life. And then if you think about some of the environmental characteristics, are they living in a situation that’s stressful? Maybe they’ve lost their job or they’re having some sort of financial stress. So that could be an environmental factor that also has an impact on their quality of life. Yeah, very good. So one topic that I found really interesting about is when you create a development plan.

08:59
you need to have something that is called a patient reported outcome strategy. Why do we need a strategy and what is that actually about? Yeah, that’s a great question. I think the strategy could be defined a lot of different ways, but I think an easy way to think about it is it’s an umbrella term and then we can have buckets underneath that umbrella. So

09:24
four of the buckets that I often think about are identifying your PRO measures, implementing your measures, analyzing the data, and then reporting the results. So those four categories could fit under the umbrella of your PRO strategy. And there’ll be different things that fall into each of those buckets as you progress through your strategy. But it’s important to have a plan because you want to make sure from the outset that you’re selecting

09:53
appropriate measures, and then you need also plan to implement them and analyze the data. And of course, you need a plan at the end how you’re going to communicate and disseminate the results. So from start to finish, there are all these aspects that require advanced planning and preparation to ensure that you have a cohesive PRO strategy. So when I think about the first step and selecting the PROs, what would be factors

10:22
that drive my selection. So given that there are actually PROs in that area. Yes, you’re right. First, you need the measures to exist. Yes. And thankfully, there are a lot of measures out there. So you’re wondering what factors into the decision-making process about selecting these measures. Yes. It’s a multifaceted process. Ultimately, it does go back to the patient voice and the patient experience, like you mentioned earlier.

10:50
So you need to examine the qualitative research that is available in your specific indication to get an understanding of what the patient experience is in that indication, what really matters to patients, because you want to make sure you’re measuring what really matters to them. So you have to take into consideration the patient voice. You also probably need to take into consideration any precedent. Are there…

11:18
studies that have already finished or are in progress, what measures are being used in those studies. If you’re planning to compare results later on, you need to have some sort of alignment between the past studies and your studies. So that’s something you have to keep in mind. And then also another thing to think about is how the treatment will have its impact. So like you mentioned earlier with

11:46
the tumor reduction, you need to make sure that what you’re measuring is not just important to the patients in that indication, but is also relevant to the treatment itself. So it’s, it’s a complex process to choose the right measure, but starting early on, you can come up with a plan to make sure you’re selecting the best possible measures. It definitely, you know, should pick up a treatment effect.

12:15
Otherwise, it’s pointless. Of course, it also needs to be in line with what regulators demand from such a patient reported outcome and what payers will demand from such a patient reported outcome. Exactly. You need to make sure you’re using validated measures because if you’re using something that’s not validated, you’re going to encounter issues. So that’s also a really important piece.

12:44
talked about the implementation, the analysis, and then publication reporting factor. In these three areas, is there anything that you would like to tell the listeners that is really important for you where things go wrong? Oh, well, I guess it all goes back to planning, having a plan in place. And that could look different for each of these areas. For implementation, some of the things we think about are when

13:13
The measures will be collected, in what order they’ll be administered, the potential burden to the patient if there are too many measures or the measures are too lengthy. in terms of the implementation, that’s when it gets very logistical in nature, trying to figure out the best way.

13:33
to implement these measures that you’ve selected because at this point you’ve determined, okay, we have the best possible measures for our purposes. And so then you want to make sure that you’re actually implementing them in the best possible way. So that gets pretty logistical in nature. In terms of the analysis, it goes back to, like you said, the treatment effect and being able to demonstrate with your analyses that there is an effect of the treatment on your PRO endpoints. And then in terms of

14:02
reporting, you know, we may not often see this, but ideally if we can see the PRR results in the primary publication with the efficacy analyses, that’s an ideal situation. And there’s all sorts of different reporting guidelines that can be used to ensure that the results are reported in an appropriate way. And so that’s really important as well to make sure that we’re, you know, using

14:32
the best standards possible to communicate these results. Yes, so there’s of course, CONSERT for clinical trials, STROBE and Prisma and many other statements that help you on that. And I think I talked about this in earlier episodes, so you probably need to just search for it in my archives. But this is a really important part. One other part that I was struggling with

15:00
when I was working in a German affiliate of a big company was you need translations. So just having the English version is not sufficient. Probably not even if you run your study just in the US. Yeah. That’s correct. And so having something that is understandable to your local patients is really important.

15:25
And then there’s ways how you can have a validated translation and things like this. But make sure you have enough time planned for doing these things. Yes, exactly. It’s a very lengthy process that involves translation and back translation and validation. And there are some concepts that work really well in one language that don’t work so well in another language. For example, we might have a measure in English that says, I’ve been feeling blue.

15:54
And in some places, there’s no, that just doesn’t work out, right? You can’t just say, I’m feeling blue. So when, when you translate a measure and validate it in another language, it can end up being a pretty complicated process. Yeah. In German, I’m feeling blue means I’m drunk. So that would not work for that measure. Yeah. So when you think about such a strategy, what would be a

16:23
good point to start with it, overall clinical development plan. Do you need to do this already before phase one or what’s the timeline? Let’s say the earlier the better, but there’s not necessarily a bad time to get started. So it doesn’t necessarily have to be earlier than phase one. Phase two is probably a fine starting point so that you can start to implement the measures. So I don’t necessarily think there’s a bad time. You can…

16:51
Given that the peer-reviewed strategy can encompass a variety of different activities, even if somebody comes to us at the very end, just needing guidance or assistance with dissemination, we can always support with that. So there’s no bad time to be thinking about your peer-reviewed strategy, but in an ideal world, earlier on would be better. Yeah, earlier on, at least, said you have some already in phase two. Yes. you said you have data.

17:20
for your phase three study. And I also really want to emphasize again the comparability with other drugs. So I have once worked in an area where the company I worked with wanted to do things better and choose, yeah, a better patient reported outcome, but it was different to all the others.

17:46
So any comparisons, network meta-analysis, all these didn’t work, or at least didn’t work as good as they should. And that created all different problems. So I think there’s sometimes there’s a balance that you need to meet in terms of what you do, when you do, and these different topics. Exactly. Yeah. There are a lot of measures that we think of as legacy measures that are older and have been used.

18:15
extensively, but maybe weren’t developed with the most sophisticated methods. And so now we have newer measures that are much more robust. But like you said, if you’re planning for an indirect treatment comparison, you’re going to run into a roadblock where you won’t be able to compare your new, more sophisticated measure with the older legacy measure. So it’s definitely something to keep in mind. Once we did a specific study,

18:45
It was actually not even a clinical trial where we had sent both browse included so that we can easily match one on the other. And that helped a lot with, with, you know, translating them. Yeah. That must’ve been really interesting. Yes, that was fun. Yeah. Thanks so much, Julia. Of course. was awesome to have you. Is there any final things that you want to tell the listener about?

19:14
Piero’s strategy key takeaway point? Yeah, I would say it all goes back to what you mentioned at the beginning that ultimately what we’re doing is for patients. And so their voice and their experience needs to be incorporated. We’re not just checking a box to say, oh, we included Piero’s or we reported on Piero’s. It’s so much more than that. We’re really doing this for patients. And to that end, if anybody wants to learn more about

19:43
PiroStrategy. There’s a great free resource that was put together by the National Cancer Institute and ISAQUAL. They created a free webinar and it’s available on the National Cancer Institute YouTube page. So for anybody listening who would like more information about PiroStrategy, that’s available. And we will link to this and other resources that we mentioned today on the blog of this episode. Thanks so much, Julia.

20:13
Thank you for having me. And I’m really looking forward to continue working with you. Likewise. Thank you, Alexander.

20:25
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