Aspects of Placebo Response

Have you ever wondered what factors drive the placebo response in clinical trials?

Join me today as we uncover the truth behind placebo responses and explore the fascinating factors driving these responses in medical research.

We’ll also cover some design aspects to mitigate this response. 

Here are the highlights in this episode:

  • Introduction to Placebo Response
  • Common Misconceptions
  • Factors Driving Placebo Response
  • Implications for Clinical Practice
If you’re intrigued by the complexities of placebo responses and want to gain valuable insights into how they influence clinical trial outcomes, listen to this episode. Share it with your colleagues and peers to deepen your understanding of placebo’s role in medical research.


Aspects Of Placebo Response

[00:00:00] Alexander: Welcome to another episode of the effective statistician. Today we want to talk about placebo. There are a lot [00:00:10] of misperceptions actually about placebo. The first misperception is that it is no treatment. [00:00:20] Well, that is in most cases not true because placebo means that you [00:00:30] just have a placebo treatment on top of standard of care.

[00:00:36] Alexander: And so, That usually doesn’t mean no [00:00:40] treatment whatsoever. So that is the first thing that we need to understand. It doesn’t mean no treatment. It just means that [00:00:50] beyond the all the usual things that the patient gives and that the protocol allows, of course, There is also this placebo injection [00:01:00] pill whatsoever.

[00:01:01] Alexander: Now even if there’s very, very little standard of care, there is still usually a [00:01:10] lot of placebo response. Well, of course, it depends on the different indications, but it’s surprisingly big and [00:01:20] widespread. Now, there’s a couple of things that can help. drive placebo response. And one of the important things is the [00:01:30] perception of the patients.

[00:01:32] Alexander: Now, what is driven? What actually drives the perception of the patients? So first, there is [00:01:40] the ratio of the randomization. If you have a one to one randomization, The placebo response [00:01:50] is sometimes actually smaller than if you, let’s say, have a randomization of three to one in [00:02:00] favor of the active group.

[00:02:02] Alexander: That has been studied in depression studies. And so that ratio, so the, the [00:02:10] expectance of whether, how likely it is that patients get placebo actually drives the placebo response. Another thing is of course [00:02:20] the strength of the anticipated treatment. So these can go in various directions. If the strength of the [00:02:30] treatment response is very big, Patients will notify and notice that they are not on treatment.

[00:02:37] Alexander: Yeah, because they [00:02:40] know that, well, if I take that medication, then my symptoms surely will go away. And only if I don’t take it, then they will stay. So the [00:02:50] perception about the strength of the medication will definitely drive it. Actually, also the on the other side. So there’s also the so called [00:03:00] nocebo effect.

[00:03:01] Alexander: So patients will experience side effects. Under placebo if they think these side effects [00:03:10] should occur under the actual treatment. Also, the pre-treatment might drive placebo response. So if they [00:03:20] already had exposure to similar treatments, things like that, that can have an in impact on the strengths of the placebo response.

[00:03:29] Alexander: [00:03:30] So naive patients usually behave. differently to placebo than pre created patients. The other thing is then [00:03:40] of course also the perception from the physicians because there is very often a very close relationship between the physicians and the patients and [00:03:50] so the perception about of, you know, the physician likewise As the patients will drive these kind of different things [00:04:00] and don’t think that patient reported outcomes are an exception from that.

[00:04:06] Alexander: There is a very, very strong bond [00:04:10] between physicians and patients in many cases. And so any expectations on the physician side, but also transition over to the patient [00:04:20] side. Now, these are. Very often, you know, the most commonly mentioned placebo responses. And [00:04:30] when you talk to non statisticians, they say, Oh, yeah, well, placebo response, but, you know, you can also see it in horses or, you know, all kinds of other [00:04:40] animals.

[00:04:40] Alexander: And they don’t have these perceptions and things like that. Well, Yeah, there’s still the observer bias. However, [00:04:50] there’s also the regression to the mean and just the natural variability of the condition. Usually, we [00:05:00] study patients only when their symptoms are very, very strong. And when there is a natural [00:05:10] variation over time, then just by chance, and just by, you know, how patients would usually [00:05:20] evolve.

[00:05:20] Alexander: That is especially true, for example, in depression, anxiety, pain, and other many other areas where [00:05:30] symptoms fluctuate over time. And when we only include patients with a certain level of symptoms, yeah, [00:05:40] certain severity of such symptoms, then naturally they will always kind of improve afterwards. Okay.

[00:05:48] Alexander: There’s another aspect and [00:05:50] that is the intensity of how we kind of study the patients in a sense. [00:06:00] So if we have a lot of visits, if we’ll have a lot of instruments, if we have a [00:06:10] lot of different measurements, all kind of different things, This will drive placebo response, because this kind [00:06:20] of intensity of care of the interactions will drive placebo response.

[00:06:27] Alexander: This is especially true [00:06:30] in mood disorders, for example. If you have more interactions with the physicians, just that by itself. can drive improvement [00:06:40] in terms of the symptoms. The administration wrote might also play a big role. So different [00:06:50] administration routes can lead to different placebo response, just because the different perceptions that it comes with.

[00:06:59] Alexander: [00:07:00] Patients will think about a pill very different to, for example, an injection or multiple injections, many injections, yeah? This [00:07:10] might drive a bigger placebo response. And finally, just different other design aspects within the study [00:07:20] may have an influence on the placebo response. The duration of the treatment or the placebo treatment can have [00:07:30] an influence and also the ability whether patients can switch to another treatment thereafter [00:07:40] once they haven’t responded.

[00:07:42] Alexander: can have an impact on that. So be careful for all these different factors. [00:07:50] There are lots of different design aspects that can help you to, for example, blind these things. Different depression, [00:08:00] anxiety, and other studies have, for example blinded lead in periods. So the patients actually don’t know when they will [00:08:10] switch from placebo to active treatment.

[00:08:13] Alexander: or whether they, when they switch from active to placebo treatment, or they [00:08:20] might, you know, have always a certain time period and only thereafter they can start with the new treatment. [00:08:30] There are many, many different ways you can reduce or manage the placebo response. You can even have a run in period [00:08:40] on placebo and then only randomize.

[00:08:43] Alexander: those that haven’t responded to placebo. Now these are of course all kind of different [00:08:50] things that also potentially have an influence on the, on the estimate and they definitely should be discussed with the different regulators. [00:09:00] Helping your non statistical colleagues to better understand placebo response and all the different [00:09:10] factors that Influences will also be really, really important so that they understand all these [00:09:20] different origins of placebo response and don’t assume that they know everything about placebo response.[00:09:30] 

[00:09:30] Alexander: So this was a shorter episode about a little bit more technical problem. Depending on your therapeutic area you’re working on in it, this [00:09:40] will be a bigger or smaller problem. I know that in certain indications, it’s no problem whatsoever, at least on the efficacy side, [00:09:50] because the treatment effects are that big.

[00:09:53] Alexander: And then of course the study more or less unblinds itself. or side effects are that [00:10:00] big that it also unplugs itself. Well, nevertheless, Think about what happens in your [00:10:10] specific area, and sometimes it even makes sense to invest more into understanding placebo [00:10:20] response in your area. For these cases, it is really helpful to work across different companies across the [00:10:30] industry, so said Esa Hall.

[00:10:33] Alexander: You can understand better placebo response and what drives it. For that, there’s [00:10:40] lots of different data sharing opportunities. And there’s, for example, the clinical study data request consortium that you [00:10:50] can have a look into. And there’s some other consortium. It could also be said, potentially you can work with regulators on that.

[00:10:58] Alexander: Maybe they can [00:11:00] be a help there. Thanks a lot. I don’t know that specifically. So if you found this episode helpful, [00:11:10] then please share it with your colleagues. This is an episode that is beyond the 350 mark [00:11:20] episode of this podcast. And there’s. Lots to learn within the statistics field, not just about technical things like this, but also many, many [00:11:30] other aspects. So please recommend this podcast to your colleagues.

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