The Peter Attia Drive - #188 - AMA #30： How to Read and Understand Scientific Studies

00:00:00.000 Hey everyone, welcome to a sneak peek, ask me anything or AMA episode of the drive podcast.

00:00:16.500 I'm your host, Peter Atiyah. At the end of this short episode, I'll explain how you can

00:00:20.460 access the AMA episodes in full, along with a ton of other membership benefits we've created,

00:00:25.440 or you can learn more now by going to peteratiyahmd.com forward slash subscribe.

00:00:31.140 So without further delay, here's today's sneak peek of the ask me anything episode.

00:00:39.280 Welcome to another ask me anything episode, AMA number 30. I'm once again joined by Bob Kaplan.

00:00:44.840 In today's episode, we discuss all things around studying studies. If you've listened to this

00:00:50.180 podcast or read any of my weekly emails, you know that I place a large emphasis on being able to

00:00:54.280 sift through the noise and find the signal when it comes to various studies and papers that are

00:00:58.280 printed, both as the studies themselves. And unfortunately, a lot of the times with the

00:01:01.900 media reports on them. So if you follow the news, you know, there's no shortage of articles that

00:01:05.520 either contradict each other or seem too good to be true, don't make any sense. It can be hard to

00:01:09.480 understand this. So what do you do? Well, we've, as some of you may know, written a series on this

00:01:14.240 called studying studies, but we've also tried to tackle some of the bigger things in this AMA.

00:01:18.640 And we combed through a lot of the questions that many of you have been asking over the previous

00:01:21.640 months and years that relate to this topic. And I think we have enough of them here that we were

00:01:24.980 able to put together a solid episode. This episode's a bit longer than normal. We contemplated breaking

00:01:29.740 it into two because it's so long, but I think our audience can handle this. And so at least now you

00:01:35.200 have in one place, the all singing, all dancing discussion of how to study studies. So in this

00:01:40.580 discussion, we talk about a bunch of things. What are they? What is the process for a study to go from

00:01:43.660 an idea to a design to execution? What are the different types of studies out there? And what do they

00:01:48.140 mean? What are the strengths and limitations of each of them? How do clinical trials work

00:01:51.980 specifically for drugs? What are the common pitfalls of observational studies that you

00:01:55.880 should be looking for? What questions should you be asking about a study to figure out how rigorous it

00:02:00.500 was? What does it really mean when a study is statistically significant? Why do some studies

00:02:05.380 never get published? What is my process for reading scientific papers? So if you're a subscriber and you

00:02:11.220 want to watch the video of this podcast, you can see it on the show notes page. I think that's

00:02:15.180 valuable for this episode because I do refer to some tables, though this is still certainly

00:02:20.060 amenable to audio only. If you're not a subscriber, you can watch the sneak peek of this on our YouTube

00:02:25.200 page. So without further delay, I hope you enjoy AMA number 30. Hey, Bob, how are you, man? Looking

00:02:35.720 pretty studious there in the library today. Hey, Peter. Thanks very much. Yeah, just getting some

00:02:40.760 reading in before the podcast. This is going to be a pretty good one because as you may recall about,

00:02:46.600 I don't know, four or five months ago, maybe longer, I was on a podcast with Tim Ferriss and

00:02:50.600 I don't know how it came up, but I do remember somehow it came up that we had spent a lot of

00:02:55.260 time writing this series, Studying Studies. And God, that's been four years ago, I think. But we didn't

00:03:02.560 really have something more digestible for folks on how to make sense of the ever-changing landscape of

00:03:09.240 scientific literature and how to kind of distinguish between the signal and the noise of the research

00:03:14.760 news cycle. And I remember after that, Tim and I went out for dinner and he kept pressing me on,

00:03:19.500 well, what can I do to get better at this process? Are there newsletters I used to be subscribing to

00:03:24.920 and things like that? And while I'm sure that there are, I didn't know what they were off the top of my

00:03:28.180 head. And so I think what we've done here, when I say we, I mean you, what you have done here is

00:03:33.360 aggregate all the questions that have come in over the past year, basically that pertain to

00:03:40.400 understanding the structure of science. I looked through the questions last week and I was pretty

00:03:46.000 excited. I think it's going to be a sweet discussion and I hope this serves as an amazing

00:03:49.960 primer for people to really understand the process of scientific experiments and everything from how

00:03:57.160 studies are published and obviously what some of the limitations are. So anything else you want to add

00:04:01.200 to that, Bob, before we jump in? I agree. I think it's a fun topic. We get so many of these questions

00:04:05.640 that we end up, or at least I do, or to the website where we'll point readers to one of the parts of the

00:04:11.940 studying studies. But I think sometimes just talking about it and explaining it can help a lot. So I think

00:04:18.060 this will be really useful as far as like a question and answer session rather than just reading a

00:04:23.280 blog. I don't think this displaces that other stuff. I think we go into probably more detail on some

00:04:28.340 things there, but I also think we're going to cover things here that aren't covered there. So

00:04:31.540 depending on how you like to get your info, this could be fun. So where do you want to start?

00:04:36.620 We have, again, a lot of questions, but I think this question gets to the core of, I think,

00:04:40.860 what we're trying to do here, which is how can a user or a person who has no scientific background

00:04:46.640 better understand studies that they read in the news or in the publications to know if the findings

00:04:52.080 are solid or not, especially in today's age where you can easily see two studies that contradict

00:04:56.780 each other. Coffee's good. Coffee's bad. Eggs are good. Eggs are bad. So I thought we could run

00:05:02.660 through a bunch of questions with the first one that we got here is what is the process for a study

00:05:08.980 to go from an idea to design and execution? This is a great question. In theory, it should start

00:05:16.480 with a hypothesis. Good science is generally hypothesis driven. I think the cleanest way to think

00:05:24.420 about that is to take the position that there is no relationship between two phenomena. We would call

00:05:33.500 this sort of a null hypothesis. So my hypothesis might be that drinking coffee makes your eyes turn

00:05:43.160 darker. So I would have to state that hypothesis and then I would have to frame it in a way that says

00:05:49.320 my null hypothesis is that when you drink coffee, your eyes do not change in color in any way, shape,

00:05:57.520 or form. And that would imply that the alternative hypothesis is that when you drink coffee, your eyes

00:06:04.980 do change color. You can already see, by the way, that there's nuance to this because am I specifying

00:06:12.220 what color it changes to? Does it get darker? Does it get lighter? Does it change to blue, green? Does it just

00:06:17.720 get the darker shade of whatever it is? But let's put that aside for a moment and just say that you

00:06:22.140 will have this null hypothesis and you will have this alternative hypothesis. And to be able to

00:06:27.200 formulate that cleanly is sort of the first step here. The second thing, of course, is to conduct

00:06:32.600 an experimental design. How are you going to test that hypothesis? As we're going to talk about,

00:06:38.380 a really, really elegant way to test this is using a randomized controlled experiment. If it's possible

00:06:44.640 to blind it, we'll talk about what that means. You'll have to decide, well, how long should we

00:06:49.620 make people drink coffee? How frequently should they drink coffee? How are we going to measure eye

00:06:53.880 color? These are the questions that come down to experimental design. You then have to determine

00:06:59.060 a very important variable, which is how many subjects will you have? And of course, that will depend on a

00:07:05.600 number of things, including how many arms you will have in this study. But it comes down to doing

00:07:10.280 something that's called a power analysis. And this is so important that we're going to spend some time

00:07:14.220 talking about it today, although I won't talk about it right now. If this study involves human

00:07:19.640 subjects or animal subjects, you will have to get something called an institutional review board to

00:07:25.320 approve the ethics of the study. So you'll have to get that IRB approval. You'll have to determine what

00:07:31.380 your primary and secondary outcomes are, get the protocol approved, develop a plan for statistics,

00:07:36.620 and then pre-register the study. All of these things happen before you do the study. And of

00:07:42.260 course, in parallel to this, you have to have funding. So those are kind of the steps that go

00:07:47.440 into doing an experimental study. And what we're going to talk about, I think in a minute, is that

00:07:53.440 there are some studies that are not experimental, where some of these steps are obviously skipped.

00:07:58.740 Yeah. One of the questions we got was, what are the different types of studies out there? And what do

00:08:02.460 they mean, for example, observational study versus a randomized controlled study? What are the

00:08:07.740 different types of studies? I think broadly speaking, you can break studies into three

00:08:15.760 categories. One would be observational studies. We'll bifurcate those or trifurcate those in a minute.

00:08:22.380 Then you can have experimental studies. And then you can have basically summations of and or reviews of

00:08:32.800 and or analyses of studies of any type. Let's kind of start at the bottom of that pyramid.

00:08:39.200 I think you actually have a figure that I don't like very much, but-

00:08:42.940 I was going to say, I thought it was one of your favorites.

00:08:45.040 Yeah, I can't stand it. I'll tell you what I like about the figure. I like the color schema because

00:08:49.200 my boys are so obsessed with rainbows that if I show them this figure, they're going to be really

00:08:56.060 happy. So let's pull up said rainbow figure. Okay. Got it.

00:09:01.340 Okay. So you can see these buckets here. And again, at the level of talking about them,

00:09:06.560 I think this makes sense. What I don't agree with the pyramid for Bob is that it puts a hierarchy in

00:09:12.400 place that suggests a meta-analysis is better than a randomized control trial, which is not necessarily

00:09:18.220 true, but let's just kind of go through what each of these things mean. So looking at the

00:09:22.280 observational studies, an individual case report is first or second paper I ever wrote in my life

00:09:27.500 when I was in medical school, it was an individual case report. There was a patient who had come into

00:09:32.820 clinic when I was at the NIH. This was a patient with metastatic melanoma and their calcium was sky high,

00:09:41.620 dangerously high, in fact. And obviously our first assumption was that this patient had metastatic

00:09:48.120 disease to their bone and that they were lysing bone and calcium was leaching into their bloodstream.

00:09:53.860 Turned out that wasn't the case at all. It turned out they had something that had not been previously

00:09:58.260 reported in patients with melanoma, which was they had developed this parathyroid hormone related like

00:10:04.800 hormone in response to their melanoma. This is a hormone that exists normally, but it doesn't exist

00:10:11.080 in this format. And so their cancer was causing them to have more of this hormone that was causing

00:10:17.740 them to raise their calcium level. It was interesting because it had never been reported before in the

00:10:21.820 literature. And so I wrote this up. This was an individual case report. Is there any value in that?

00:10:29.200 Sure. There's some value in that. The next time a patient with melanoma shows up to clinic

00:10:33.260 and their calcium is sky high and someone goes to the literature to search for it, they'll see that

00:10:37.860 report and it will hopefully save them time in getting to the diagnosis.

00:10:43.180 Your mentor and friend, Steve Rosenberg, I think of him when I think of individual case reports.

00:10:48.920 I think if you listen to the podcast, he talks about this, but a lot of what motivated him early

00:10:53.560 on, I think were just a couple of cases. I think it gets back to that first question too,

00:10:57.440 about the process for a study to go to an idea to design to execution, which is to have a hypothesis,

00:11:02.660 you need to make an observation. And so you make an observation, you say, hmm, that's strange.

00:11:07.680 And I think that that's what individual case reports can represent sometimes is this is an

00:11:11.300 interesting observation. It's hypothesis generating for the most part, but it really

00:11:15.360 might kickstart a larger trial or it might kickstart a career. You never know.

00:11:20.940 Exactly. Now, of course, it's not going to be generalizable. I can't make any statement about

00:11:25.900 the frequency of this in the broader subset of patients. And obviously I can't make any comment

00:11:31.620 about any intervention that may or may not change the outcome of this. So that gets us to kind of

00:11:36.660 our next thing, which is like a case series or set of studies. So here you're basically doing the same

00:11:45.620 thing, but in plural effectively. You wouldn't just look at one patient. You would say, well,

00:11:52.100 I've now been looking back at my clinical practice and I've had 27 patients over the last 40 years

00:12:02.260 that have demonstrated this very unusual finding. And another example of this, going back to the

00:12:08.480 Steve Rosenberg case, would be one could write a paper that looks at all spontaneous regressions of

00:12:13.920 cancer. Obviously, spontaneous regressions of cancer are incredibly rare, but there are certainly

00:12:19.140 enough of them that one could write a case series. So now let's consider cohort studies. So cohort

00:12:25.000 studies are larger studies and they can be retrospective or they can be prospective. So I'll

00:12:29.960 give you an example of both. So a retrospective observational cohort study would be, let's go back

00:12:37.960 and look at all the people who have used saunas for the last 10 years and look at how they're doing today

00:12:48.000 relative to people who didn't use saunas over the last 10 years. So it's retrospective. We're looking

00:12:54.580 backwards. It's observational. We're not doing anything, right? We're not telling these people

00:12:59.540 to do this or telling those people to do that. And the hope when you do this is that you're going to

00:13:03.860 see some sort of pattern. Undoubtedly, you will see a pattern. Of course, the question is,

00:13:07.700 will you be able to establish causality in that pattern? Cohort studies can just as easily,

00:13:13.120 although more time-consumingly, be prospective. So you could say, I want to follow people over the

00:13:21.400 next five years, 10 years who use saunas and compare them to a similar number of people who

00:13:28.340 don't. And now in a forward-looking fashion, we're going to be examining the other behaviors

00:13:35.660 of these people and ultimately what their outcomes are. Do they have different rates of death,

00:13:39.140 heart disease, cancer, Alzheimer's disease, other metrics of health that we might be interested in?

00:13:43.740 Again, we're not intervening. There's not an experiment per se. We're just observing,

00:13:49.360 but now we're doing it as we march forward through time. So this brings us to the kind of the next

00:13:54.400 layer of this pyramid, which are the experimental studies. Divide these into randomized versus non-randomized.

00:14:02.200 And of course, this idea of randomization is going to be a very important one as we go through this.

00:14:07.300 So a non-randomized trial sometimes gets referred to as an open label trial where you take two groups

00:14:15.980 of people and you give one of them a treatment and you give the other one either a placebo or a

00:14:20.360 different treatment, but you don't randomize them. There's a reason that they're in that group.

00:14:25.260 So you might say, we want to study the effect of a certain antibiotic on a person that comes in

00:14:34.720 and we're going to take all the people that come in who look a certain way. Maybe they have a fever of

00:14:44.160 a certain level or a white blood cell count of a certain level. We're going to give them the

00:14:48.180 antibiotic and the people who come in, but they don't have those exact signs or symptoms. We're

00:14:54.600 going to not give an antibiotic to, and we're going to follow them. That's kind of a lame example.

00:14:59.020 You could do the same sort of thing with surgical interventions. We're going to try to ask the

00:15:04.280 question, is surgery better than antibiotics for appendicitis or suspected appendicitis?

00:15:10.660 But we don't randomize the people to the choice. There's some other factor that is going to determine

00:15:17.480 whether or not we do that. As you can see, that's going to have a lot of limitations because

00:15:21.580 presumably there's a reason you're making that decision and that reason will undoubtedly introduce

00:15:26.800 bias. So of course, the gold standard that we always talk about is a randomized control trial

00:15:32.720 where whatever question you want to study, you study it, but you attempt to take all bias out of

00:15:40.880 it by randomly assigning people into the treatment groups, the two or more treatment groups. We'll talk

00:15:46.820 about things like blinding later because you can obviously get into more and more rigor when you do

00:15:52.140 this. But before we leave the kind of experimental side, anything you want to add to that, Bob?

00:15:56.800 I would add, so non-randomized controlled trials, maybe another example, illustrative example,

00:16:01.640 I think with non-randomized controlled trials might be you have patients maybe making a decision

00:16:06.680 beforehand, which we'll get into selection bias, but they might want to go on a statin, let's say,

00:16:11.120 and then you give them a choice and the other ones might want to go on some other drug like

00:16:14.440 azetimibe. They're basically selecting themselves into two groups, but you could compare those two

00:16:19.860 groups and see how they do, but it hasn't been randomized. There's a lot of bias that can go into that.

00:16:24.800 There could be a lot of reasons why one group is selecting a particular treatment over the other.

00:16:29.820 And so that's why I think when we get to randomized trials that shows the power of randomization.

00:16:35.300 Yeah, exactly. We don't need to go back to the figure, but people might recall that at the top

00:16:39.220 of that pyramid was systemic reviews and meta-analyses. Let's just talk about meta-analyses

00:16:43.460 since they are probably the most powerful. So this is a statistical technique where you can combine

00:16:47.980 data from multiple studies that are attempting to look at the same question, basically. So each

00:16:54.340 study gets a relative weighting and the weighting of a study is sort of a function of its precision.

00:17:00.060 It depends a little bit on sample size, other events in the study, larger studies, which have

00:17:04.900 smaller standard errors are given more weight than smaller studies with larger standard errors,

00:17:08.700 for example. You'll know you're looking at a meta-analysis. We should have had a figure for

00:17:12.640 this, but I'll describe it the best I can. They usually have a figure somewhere in there that will

00:17:18.640 show across rows all of the studies. So let's say there's 10 studies included in the meta-analysis,

00:17:24.860 and then they'll have the hazard ratios for each of the studies. So they'll represent them usually as

00:17:31.780 little triangles. The triangle will represent the 95% confidence interval of what the hazard ratio is,

00:17:39.800 which we'll talk about a hazard ratio, but it's basically a marker of the risk. And you'll see all 10

00:17:44.740 studies, and then they'll show you the final summation of them at the bottom, which of course you wouldn't

00:17:49.360 be able to deduce looking at the figure, but it takes into account that mathematical weighting. So on the

00:17:54.020 surface, meta-analyses seem really, really great, because if one trial, one randomized trial is good,

00:18:02.040 10 must be better. I know I've said this before probably three or four times over the past few years on the

00:18:07.100 podcast, but as James Yang, one of the smartest people I ever met when I was both a student and

00:18:12.500 fellow at NCI, once said during a journal club about a meta-analysis that was being presented,

00:18:17.580 he said something to the effect of, a thousand sows ears makes not a pearl necklace. And that's just an

00:18:24.080 eloquent way to say that garbage in, garbage out. So if you do a meta-analysis of a bunch of garbage

00:18:29.800 studies, you get a garbage meta-analysis. It can't clean garbage. It simply can aggregate it. So a

00:18:38.180 meta-analysis of great randomized control trials will produce a great meta-analysis.

00:18:44.380 They try to control for garbage, the researchers and the investigators. But I think to your point

00:18:48.440 with the pearl necklace, imagine if you had say 10 trials and nine of them are garbage. One of them

00:18:54.860 is really good, really rigorous, randomized control trial. And you're looking at the top of the

00:18:59.560 pyramid and you're saying, well, meta-analysis is the best. We should be looking at this meta-analysis.

00:19:03.760 Meanwhile, you've got that one randomized control trial that actually is worth its salt. It's

00:19:08.920 rigorous, et cetera, that I would say, if you had the option, I think you probably would rely more

00:19:13.040 on that one randomized control trial, which is lower on the pyramid. So I think that's probably,

00:19:18.400 I think you've told me one of your hangups with the pyramid, because it's not necessarily

00:19:21.800 top of the pyramid is going to be some meta-analysis of randomized control trials.

00:19:26.780 That's right. Yeah. I don't want to suggest meta-analyses are not

00:19:29.460 great. What I want to suggest is you can't just take a meta-analysis as gospel without actually

00:19:36.060 looking at each study. You don't get a pass at examining each of the constitutive studies within

00:19:41.460 a meta-analysis is really the point I think we want to make here. There's one thing in here that

00:19:45.880 isn't represented, but we had a few questions about it. I think a couple. People were asking about

00:19:51.260 what's the difference between a phase three and a phase two or a phase one clinical trial?

00:19:55.980 Do you know what's going on there?

00:19:57.940 Yes. So here we're talking about human clinical trials. This phraseology is used by the FDA here

00:20:05.640 in the United States. And typically the world does tend to follow in lockstep, but not always with

00:20:11.380 kind of the FDA's process. So if you go way, way, way back, you have an interesting idea. You have a

00:20:17.920 drug that you think is, or a molecule that you think will have some benefit. Think of it as a cancer

00:20:25.300 therapeutic. You've done some interesting experiments in animals, maybe started with some

00:20:31.400 mice and you went up to some rats and maybe even you've done something in primates. And now you're

00:20:37.100 really committed to this as the success of this and the safety of this in animals looks good. So it's

00:20:42.720 both safe and efficacious in animals. And you now decide you want to foray into the human space. Well,

00:20:48.860 the first thing you have to do is file for something called an IND, an investigational new

00:20:53.960 drug application. So after you do all of this preclinical work, you have to file this IND with the

00:21:00.260 FDA. And that basically sets your intention of testing this as a drug in humans. And the first

00:21:07.520 phase of that, which is called phase one is geared specifically to dose escalate this drug from a

00:21:15.660 very, very low level to determine what the toxicity is across a range of doses that will hopefully have

00:21:23.740 efficacy. These are typically very small studies, usually less than a hundred people. They're typically

00:21:30.600 done in cohorts. So you might say, well, the first 12 people are going to be at 0.1 milligrams

00:21:37.260 per kilogram. And assuming we see no adverse effects there, we'll go up to 0.15 milligrams

00:21:44.020 per kilogram for the next 12 people. And if we have no issues there, we'll escalate it to 0.25.

00:21:50.240 You'll notice, Bob, I said nothing in there about does the drug work. These are going to be patients

00:21:55.660 with cancer. If this is a drug that's being sought as a treatment for colon cancer, these are going to be

00:22:01.020 patients that all have colon cancer. They're often going to be patients who have metastatic colon cancer.

00:22:06.200 So these are going to be patients who have progressed through all other standard treatments

00:22:12.860 and who are basically saying, look, sign me up for this clinical trial. I realized that this first phase

00:22:19.520 is not going to be necessarily giving me a high enough dose that I could experience a benefit

00:22:25.200 and that you're really only looking to make sure that this drug doesn't hurt me. But nevertheless,

00:22:30.700 I want to participate in this trial. If the drug gets through phase one safely, then it goes to phase

00:22:37.520 two. And the goal of phase two is to continue to evaluate for safety, but also to start to look for

00:22:44.820 efficacy. But this is done in an open label fashion. What that means is they're not randomizing

00:22:52.080 patients to one drug versus the other typically. They can, but usually it's now we think we know

00:22:59.820 one or two doses that are going to produce efficacy. They were deemed safe in the phase one.

00:23:06.300 We're now going to take patients and give them this drug and look for an effect. And a lot of times,

00:23:12.460 if there's no control arm in the study, you're going to compare to the natural history. So let's assume

00:23:18.220 that we know that patients with metastatic colon cancer have on standard of care have immediate

00:23:24.980 survival of X months. Well, we're going to give these patients this drug and see if that extends

00:23:29.700 it anymore. And of course you could do this with a control arm, but now it adds the number of patients

00:23:33.380 to the study. So again, typically very small studies can be in the 20, 30, 40, 50 range, maybe up to a few

00:23:41.460 hundred people. And that one, Peter, I think is probably a good example of if you have the

00:23:46.620 non-randomization, this might be a case where say it's an immunotherapy and people know about the

00:23:51.580 immunotherapy and it's been really effective. It's approved for a particular cancer, let's say.

00:23:55.800 And there are a lot of people that know about it and there are cancer patients that know about it and

00:23:59.600 they want to get that treatment, but it's not approved. They're talking to their doctor,

00:24:03.280 maybe they're online. They might enroll in one of these trials because they really want to try the

00:24:07.660 drug and maybe they might believe in it more than some other treatment.

00:24:11.420 Yep. There are lots of things that can introduce bias to a phase two if it does not have randomization.

00:24:17.220 Again, the goal would be to still randomize in phase two because you really do want to tease out

00:24:22.240 efficacy. So if a compound succeeds in phase two, which means it continues to show no significant

00:24:31.240 adverse safety effects, which by the way, doesn't mean it doesn't have side effects. Every treatment has

00:24:35.940 side effects. It's just that it doesn't have side effects that are deemed unacceptable for the risk

00:24:42.120 profile of the patient and it shows efficacy. So really you have to have these two things.

00:24:47.020 You then proceed to phase three. Here a phase three is a really rigorous trial. This is a huge step up.

00:24:53.760 It's typically a log step up in the number of patients. You're talking potentially thousands of

00:24:59.340 patients here. And this is absolutely a placebo controlled trial or not necessarily placebo, but it

00:25:08.340 can be standard of care versus standard of care plus this new agent. But it is randomized whenever

00:25:14.440 possible. It is blinded. And with drugs, that's always possible. And these are typically longer

00:25:19.760 studies because you have so much more sample size. You're going to potentially pick up side effects

00:25:25.900 that weren't there in the first place. And of course, now you really have that gold standard for

00:25:30.940 measuring efficacy. And it's on the basis of the phase one, phase two, and mostly phase three data

00:25:36.880 that a drug will get approved or not approved for broad use, which leads to a fourth phase, which is a

00:25:45.020 post-marketing study. So phase four studies take place after the drug has been approved and they're used

00:25:53.180 to basically get additional information because once a drug is approved, you now have more people

00:25:58.780 taking it. And they may also be using this to look at other indications for the drug. We talked about

00:26:04.640 this recently, right? A phase four trial with semaglutide being used to look at obesity versus

00:26:12.100 its original phase three trials, which were looking at diabetes. The drug's already been approved. This

00:26:17.700 study isn't being done to ask the question, should semaglutide be on the market? No, it's on the market.

00:26:22.720 It's basically expanding the indication for semaglutide in this case so that insurance

00:26:27.120 companies would actually pay for it for a new indication. But given the size and the number

00:26:31.700 of these studies, you're also looking for, hey, is there another side effect here that we missed

00:26:36.860 in the phase three? Right. And it might be the particular population that might have a different

00:26:42.180 risk profile. You might have a different threshold. That's right. Because you're not doing this in

00:26:46.200 patients with type two diabetes. You're doing this in patients who explicitly don't have diabetes,

00:26:49.760 but have obesity, different patients. Are we going to see something different here? So yeah. So

00:26:54.260 anyway, that's the long and short of phases one, two, three, and four.

00:26:59.120 Okay. So going back to observational studies, are there any things that you look for in particular

00:27:04.640 that will increase or decrease your confidence in it, whether that's a pearl necklace or garbage?

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The Peter Attia Drive - December 20, 2021

#188 - AMA #30： How to Read and Understand Scientific Studies

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