188 - [AMA #30 Sneak Peek] How to Read and Understand Scientific Studies

1. 0:00 – 0:10

   Intro

   1. PAPeter Attia0:00

      [upbeat music] Hey, everyone. Welcome to "The Drive" podcast. I'm your host, Peter Attia. Hey, Bob.

2. 0:10 – 2:44

   The ever changing landscape of scientific literature

   1. PAPeter Attia0:10

      How are you, man? Looking pretty studious there in the library today.

   2. BOBob0:13

      Hey, Peter. Thanks very much. Yeah, just getting some reading in before the-

   3. PAPeter Attia0:16

      [chuckles]

   4. BOBob0:17

      ... before the podcast.

   5. PAPeter Attia0:20

      Well, this is, uh, this is gonna be a pretty good one because, um, as you may recall, about, um, I don't know, four or five months ago, maybe longer, I was on a podcast with Tim Ferriss, and [lip smacks] uh, I don't know how it came up, but I do remember somehow it came up that, um, you know, we had spent a lot of time writing this series, Studying Studies, and, uh, God, that's been four years ago, I think. Um-

   6. BOBob0:45

      Yeah

   7. PAPeter Attia0:45

      ... but [clears throat] we didn't really have something more digestible for folks on how to make sense of the ever-changing landscape of scientific literature and, uh, you know, in some ways, how to, how to kinda distinguish between the signal and the noise of, of the research news cycle. Um, and I remember after that, Tim and I went out for dinner, and he, he kept sort of pressing me on, "Well, what can I do to get better at this process?" Um, you know, are there newsletters I should, I should be subscribing to and things like that. And, and while I'm sure that there are, I didn't know what they were off the top of my head. Um, [lip smacks] and so I think what we've done here, when, when I say we, I mean you, um, what, what, what you have done here is kind of aggregate all the questions that have come in over the past year, basically, um, that pertain to understanding the structure of science. And, uh, I really ... I don't know. I looked through the questions, uh, last week, and I was pretty excited. I think it's gonna be a sweet discussion, and I hope this serves as an amazing primer for people, um, to, to really understand the process of scientific experiments and, and everything from how, how, how studies are published and, and obviously what some of the limitations are. So anything else you wanna add to that, Bob, before we jump in?

   8. BOBob2:02

      I agree. I think it's a fun topic. We get so many, we get so many of these questions that we end up, or at least I do, um, or to the website, where we'll point readers to one of the parts of the Studying Studies, but I think sometimes just talking about it and explaining it can help a lot. So I think this will be really useful, um, as far as quest- like, a question-and-answer session rather than just a, just reading a blog.

   9. PAPeter Attia2:28

      Yeah. And, and, and again, I don't think this displaces that other stuff. I think we go into probably more detail on some things there, but I also think we're gonna cover things here that aren't covered there. So, um, depending on how you like to get your info, this, uh, this could be fun. So where, where do you wanna start?

3. 2:44 – 6:39

   The process for a study to progress from idea to design to execution

   1. BOBob2:44

      So I think the ... We have, again, a lot of questions, but I think the, th- this question gets to the core of, of, I think, what we're trying to do here, which is how can a user or a person who has no scientific background better understand studies that they read in the news or in the publications, uh, to know if the findings are solid, solid or not, especially in today's age where you can easily see two studies that contradictory, uh, contradict each other? Coffee's good. Coffee's bad. Eggs are good. Eggs are bad. [chuckles] Um, so I thought we could run through, uh, a bunch of questions with the f- the first one, um, that we got here is, "What is the process for a study to go from an idea to design and execution?"

   2. PAPeter Attia3:28

      [lip smacks] Yeah. Uh, it is a great st- this is a great question. Um, I mean, in theory, [chuckles] it should start with a hypothesis. Uh, good science is generally hypothesis-driven. Um, so [clears throat] I think the cleanest way to, to think about that is to take the position that there is, um, no relationship between, um, two phenomena. So we, we would call this sort of a null hypothesis. So, um, uh, my hypothesis might be that drinking coffee makes your eyes turn darker. Um, so I would have to state that hypothesis, and then I would have to frame it in a way that says, um, "My null hypothesis is that when you drink coffee, your eyes [clears throat] do not change in color in any way, shape, or form," and that would imply that the alternative hypothesis is that when you drink coffee, your eyes do change color. You can already see, by the way, that there's nuance to this because am I specifying what color it changes to? Does it get darker? Does it get lighter? Does it change to blue, green? Does it just get the darker shade of whatever it is? But let's put that aside for a moment and just say that you will have this null hypothesis, and you will have this alternative hypothesis, and to be able to formulate that cleanly is sort of the first step here. The second thing, of course, is to conduct an experimental design. How are you going to test that hypothesis? Um, uh, as we're gonna talk about, a really, really elegant way to test this is using a randomized controlled experiment. Um, if it's possible to blind it, we'll talk about what that means. Uh, but, you know, and you'll have to decide, well, how long should we make people drink coffee? How frequently should they drink coffee? How are we going to measure eye color? You ... These are the questions that come down to experimental design. You then have to determine a very important variable, which is how many subjects will you have, and of course, that will depend on a number of things, including how many arms you will have in this study. But it comes down to doing something, uh, that's called a power analysis, and this is so important that we're gonna spend some time talking about it today, although I won't talk about it right now. Uh, if this study, uh, involves human subjects or animal subjects, you will have to get something called, um, an institutional review board to approve the ethics of the study. So you'll have to get that IRB approval. Um, and- You'll have to determine what your primary and secondary outcomes are, get the protocol approved, uh, develop a plan for statistics, and then pre-register the study. Uh, all of these things happen before you do the study, and of course, in parallel to this, you have to have funding. So, uh, those are kind of the steps that go into doing an experimental study. And what we're gonna talk about, I think in a minute, is that there are some studies that are not experimental, uh, where some of these steps are obviously skipped.

4. 6:39 – 20:22

   The various types of studies and how they differ

   1. BOBob6:39

      Yeah. One of the, one of the questions we got was, what are the different types of studies out there, and what do they mean? For example, you know, an observational study versus a, a randomized controlled study. What are the different types of studies?

   2. PAPeter Attia6:51

      Yeah. So I, I mean, I, I think broadly speaking, you can break studies into three categories. Um, one would be observational studies, um, and we'll, we'll bifurcate those or trifurcate those in a minute. Uh, then you can have experimental studies, and then you can have basically summations of and/or reviews of and/or analyses of studies of any type. So, um, let's kind of start at the bottom of that pyramid. Um, and I, I think you actually have a figure that I don't like very much, but I know it's-

   3. BOBob7:36

      I was gonna say.

   4. PAPeter Attia7:36

      Yeah.

   5. BOBob7:36

      I thought it was one of your favorites.

   6. PAPeter Attia7:37

      Yeah, I can't stand it. I, I'll tell you what I like about the figure. I like the color schema because my boys are so obsessed with rainbows that if I show them this figure, they're going to, they're gonna be really happy. So let's, let's pull up said rainbow figure.

   7. BOBob7:54

      Okay. Got it.

   8. PAPeter Attia7:57

      Okay. So, uh, you can sort of see these, these, these buckets here, and, and again, at the, at the, at the level of kind of talking about them, I think this makes sense. What I don't agree with the pyramid for, Bob, is that it, it puts a hierarchy in place that suggests a meta-analysis is better than a randomized control trial, uh, which is not necessarily true. But let, let's just kind of go through what each of these things mean. So, so looking at the observational studies, an individual case report is, uh, you know, look, I, the, I think w- w- the se- first or second paper I ever wrote in my life when I was in medical school was an individual case report. Um, it was a patient who had come into, um, clinic. This was at, when I was at the NIH, and this was a patient with metastatic melanoma, and their calcium was sky high, dangerously high, in fact. And obviously, our first assumption was that this patient had, uh, metastatic disease to their bone and that they were lysing bone and calcium was, was, you know, leaching into their bloodstream. Turned out that wasn't the case at all. Um, it turned out they had something that had not been previously reported in patients with melanoma, which was they had developed this P- uh, uh, parathyroid hormone-related like hormone, um, in response to their melanoma. So, um, this is a hormone that exists normally, but it doesn't exist in this format. Um, and so their cancer was causing them to have more of this hormone that was causing them to, uh, raise their calcium level. It was interesting because it had never been reported before in the literature, and so I wrote this up. This was an individual case report. Um, is there any value in that? Sure, there's some value in that. The next time a patient with melanoma shows up to clinic and their calcium is sky high and someone goes to the literature to search for it, they'll see that report and, uh, you know, it will hopefully save them time in getting to the diagnosis.

   9. BOBob9:57

      So your mentor and friend, Steve Rosenberg, I think of him when I think of individual case reports where, I think if you listen to the podcast, he talks about this, but a lot of what motivated him early on, I think, were, were just a couple of, a couple of cases, and it, I think it gets back to that first question too about the process for a study to go to an idea to design to execution, which is to, to have a hypothesis, you need to make an observation.

   10. PAPeter Attia10:21

       Right.

   11. BOBob10:22

       So you make an observation, you say, "Hmm, that's strange." And I think that, that, that's what individual case reports can represent sometimes is, is this is a really, this is an interesting observation. It's hypothesis generating for the most part, but it really, it might kickstart a, a larger trial or it might, might kickstart a career. You never know.

   12. PAPeter Attia10:40

       Yeah, e- exactly. Um, now of course, it's not gonna be generalizable, right? It's, I, I can't make any statement about, um, you know, the frequency of this in the, the broader subset of patients, and obviously I can't make any comment about any intervention that may or may not change the outcome of this. So that gets us to kind of our next thing, which is like a, a case series, uh, or, or set of studies. So here you're basically doing the same sort of thing, um, but, but in, in, in plural effectively. Um, so, so you, you wouldn't just look at one patient, you would say, "Well, um, you know, I've now been looking back at my clinical practice and I've had, you know, 27 patients over the last 40 years that have demonstrated this very unusual finding." And, uh, uh, another example of this going back to the Steve Rosenberg case would be, uh, one could write a paper that looks at all spontaneous regressions of cancer. Well, there's obviously been a number of them. They're infrequent. Um, in fact, they're exceedingly rare, but there are certainly enough of them that one could write, uh, a case series. So, s- so now let's consider cohort studies. So, so cohort studies are larger studies, um, and they can be retrospective or they can be prospective. So I'll give you an example of both. So a, a retrospective observational cohort study would be Like, let's go back and look at all the people who have used saunas for the last 10 years and, um, look at how they're doing today relative to people who didn't use saunas over the last 10 years. So it's retrospective. We're looking backwards. It's observational. We're not d- we're not doing anything, right? We're not telling these people to do this or telling those people to do that. [lips smack] Um, and the hope when you do this is that you're going to see some sort of pattern. Undoubtedly, you will see a pattern. Of course, the question is, will you be able to establish causality in that pattern? Um, cohort studies can just as easily, although more time consumingly, be prospective. So you could say, "I want to follow people over the next five years, 10 years, who use saunas and compare them to a similar number of people who don't." And now, in a forward-looking fashion, [clears throat] we're going to be examining the other behaviors of these people and ultimately what their outcomes are. Do they have different rates of death, heart disease, cancer, Alzheimer’s disease, other metrics of health that we might be interested in? Again, we're not intervening. There's not an experiment per se. We're just observing, but now we're doing it as mar- as we march forward through time. [lips smack] Okay, so this brings us to the kind of the next layer of this pyramid, which are the experimental studies. Uh, and again, we sort of divide these into randomized versus non-randomized, and of course, this idea of randomization is going to, um, be a very important one as we go through this. Uh, so a non-randomized trial, uh, sometimes gets referred to as an open-label trial where, uh, you take two groups of people and you give one of them a treatment and you give the other one, uh, either a placebo or a different treatment, but you don't randomize them. There's a reason that they're in that group. So you might say, um, you know, "We want to study the effect of a certain antibiotic on a person that comes in the ER, and we're going to take all the people that come in who look a certain way, maybe they have a certain, a fever of a certain level or a white blood cell count of a certain level, and we're gonna give them the antibiotic. And the people who come in but they don't have those exact signs or symptoms, we're going to not give an antibiotic to, and we're gonna follow them." Uh, that's kind of a lame example. Um, you could do the same sort of thing with surgical interventions. We're gonna try to ask the question, is surgery better than antibiotics for appendicitis or suspected a-appendicitis? But we don't randomize the people to the choice. There's some other factor that i-is going to determine whether or not we do that. And as you can see, that's going to have a lot of limitations because presumably there's a reason you're making that decision, and that reason will undoubtedly introduce bias. So of course, the gold standard that we always talk about is a randomized control trial where whatever question you want to study, you study it, but you attempt to take all bias out of it by randomly assigning people into the treatment groups, the two or more treatment groups. [lips smack] Um, we'll talk about things like blinding later, 'cause you can obviously get into more and more rigor when you do this. But before we leave the kind of experimental side, anything you want to add to that, Bob?

   13. BOBob15:56

       Uh, I would add, so non-randomized controlled trials, maybe another example. I'm trying to think of another, uh, example, which is you have patients maybe, maybe making a decision beforehand, which we'll get into selection bias. But it, you-- people might wanna go, they might want to go on a statin, let's say, and then other, uh, you give them a choice, and the other ones might want to go on some other drug like ezetimibe.

   14. PAPeter Attia16:15

       Mm-hmm.

   15. BOBob16:16

       And they're, they're, they're basically selecting themselves into two groups, but you could, you could compare those two groups and see how they do. Um, but you haven't, uh, it hasn't been randomized. There's a, there's a lot of bias that can go into that. There could be a lot of reasons why one group is selecting a, a particular treatment over the other. And so that's why I think when we get to randomized trials that it shows the power of randomization.

   16. PAPeter Attia16:39

       [lips smack] Yeah. Yeah. Exactly. Um, uh, let's-- we don't need to go back to the figure, but, uh, people might recall that at the top of that pyramid was, you know, systemic reviews and meta-analyses. Let's just talk about meta-analyses since they, um, are, are probably the most powerful. So, so this is a statistical technique where you can combine data from multiple studies, um, that are attempting to look at the same question, basically. So, um, uh, each study gets a relative weighting, um, and the weighting of a study is sort of a function of, um, its precision. It depends a little bit on sample size, uh, other, other events in the study. Larger studies which have smaller standard errors are given more weight than smaller studies with larger standard errors, for example. [lips smack] Um, a very common way that these-- you'll, you'll, you'll know you're looking at a, a meta-analysis. We should have had a figure for this, but, uh, I'll describe it the best I can. They usually have a figure somewhere in there that will show kind of across rows all of the studies. So let's say there's 10 studies included in the meta-analysis, and then, um, they'll have the hazard ratios for each of the studies. So they'll represent them usually as little triangles. So the, the triangle will represent the 95% confidence interval of what the hazard ratio is, which we'll, we'll talk about a hazard ratio, but it's, it's, it's a basically a marker of the risk. [lips smack] And you'll see all 10 studies, and then they'll show you the final summation of them at the bottom, which of course you wouldn't be able to deduce looking at the figure, um, but it takes into account that mathematical weighting. So on the surface, meta-analyses seem really, really great, right? Because if One trial, one randomized trial is good, ten must be better. Um, but again, I, and I've, I know I've said this before probably three or four times over the past few years on the podcast, um, but as, as James Yang, one of the, uh, smartest people I ever met when, when I was, uh, uh, both a student and fellow at NCI, uh, once said during a journal club about a meta-analysis that was being presented, he said something to the effect of, "A thousand sows' ears makes not a pearl necklace." And, you know, that, that's just an eloquent way to say that garbage in, garbage out, right? So if you do a meta-analysis of a bunch of garbage studies, you get a garbage meta-analysis. So it, it can't, uh, it can't clean garbage. Um, it, it simply can aggregate it. So a meta-analysis of great randomized control trials will produce a great meta-analysis.

   17. BOBob19:14

       So I'm sure they, they try to control for garbage, you know, the, the researchers and the investigators. But I think to your point with the pearl necklace, imagine if you had, say, ten, ten trials, and nine of them are garbage. One of them is really good, really rigorous randomized controlled trial, and you're looking at the top of the pyramid and you're saying, "Well, meta-analysis is the best. We should be looking at this meta-analysis." Meanwhile, you've got that one randomized controlled trial that actually is worth its salt. It's, it's rigorous, et cetera, that, that w- I would say if you had the option, I think you probably would rely more on that one randomized controlled trial, which is lower on the pyramid. So I think that's probably, I think, uh, you've told me one of your hang-ups with a, with a pyramid 'cause it's not necessarily top of the pyramid is gonna be some meta-analysis of, of randomized controlled trials.

   18. PAPeter Attia20:00

       [lip smack] That's right. Yeah. A- and, and so I don't want to suggest ra- meta-analyses are not great. What I want to suggest is you can't just take a meta-analysis as gospel without actually looking at each study. You don't get a pass at examining each of the constitutive studies within a meta-analysis, is, is really the, the point I think we want to make here.

5. 20:22 – 31:42

   The different phases of a clinical trial

   1. BOBob20:22

      Yeah. There's, there's one thing in here that, that isn't represented, but we had a few questions about it, I think a couple. People are asking about, you know, you know, what's the difference between a, a phase three and a phase two or a phase one clinical trial? Do you, do you know what's going on there?

   2. PAPeter Attia20:38

      Yes. Uh, and the, these are great questions. So, so here we're talking about human clinical trials, and, and this, this phraseology is, is, is used by the FDA, um, here in the United States. And, and typically, um, the, the world does tend to follow in lockstep, but not always with, with kind of the FDA's process. So, um, [lip smack] if you go way, way, way back, uh, you, you have an interesting idea. You, you have a, a drug that you think is-- or a molecule that you think will have some, you know, benefit. L-let's think of it as a, a cancer therapeutic. Um, and you've, uh, you've done some interesting experiments in animals and, you know, you maybe started with some mice, and you went up to some rats, and maybe even you've done something in primates, and now you're really sort of committed to this as, uh, y- the success of this and the safety of this in animals looks good, so it's both safe and efficacious in animals, and you now decide you want to foray into the, the human space. Well, the first thing you have to do is, um, file for something called an IND, an investigational new drug application. So after you do all of this preclinical work, you have to file this IND with the FDA, and that basically sets your intention of testing this as a drug in humans. And the first phase of that, which is called phase one, is geared specifically to dose escalate this drug from a very, very low level to determine what the toxicity is across a range of doses that will hopefully have efficacy. So these are typically very small studies, uh, usually less than a hundred people, and they're typically done in cohorts. So you might say, well, the first, you know, twelve people are going to be at zero point one milligrams per kilogram, and assuming we see no adverse effects there, we'll go up to point one five milligrams per kilogram for the next twelve people, and if we have no issues there, we'll escalate it to point two five, da, da, da, da, da, da. Now, you, you'll notice, Bob, I said nothing in there about does the drug work, right? So-

   3. BOBob22:54

      Right

   4. PAPeter Attia22:54

      ... these are going to be patients with cancer. If this is a drug that's being, um, sought as a treatment for colon cancer, these are gonna be patients that all have colon cancer. They're often going to be patients who have metastatic colon cancer, right? So these are gonna be patients who, um, have progressed through all other standard treatments, uh, and who are basically saying, "Look, sign me up for this clinical trial. I realize that this first phase is, um, not going to be necessarily giving me a high enough dose that I could experience a benefit, um, and that you're really only looking to make sure that this drug doesn't hurt me. But nevertheless, I want to participate in this trial." [lip smack] So if the, if the drug gets through phase one safely, then it goes to phase two, and the goal of phase two is to continue to evaluate for safety, but also to start to look for efficacy. But this is done in an open label fashion. What that means is they're not randomizing patients to one drug versus the other typically. They can, but usually it's now we think we know one or two doses that are gonna produce efficacy. They were deemed safe in the phase one. We're now going to, um, take patients and give them this drug and look for an effect. And a lot of times, if there's no control arm in the study, you're going to compare to the natural history. So let's assume that we know that patients with metastatic colon cancer have, um, on standard of care have, uh, you know, a, a median survival of X months. Well, we're going to give these patients this drug and see if that extends it anymore. And of course, you could do this with a control arm, but now it adds the number, number of patients to the study. So again, typically very small studies, uh, can be, you know, in the 20, 30, 40, 50 range, maybe up to a few hundred people.

   5. BOBob24:53

      And that one, Peter, I think is a probably a good example of if you have the non-randomization, this might be a case where say it's an immunotherapy and, and people know about the immunotherapy and it's been really effective for a particular, gets approved for a particular cancer, let's say. And there are a lot of people that, that know about it and there are cancer patients that know about it and they, they want to get that treatment, but it's not approved. If they're talking to their doctor or they, they, or maybe they're online, they, they might enroll in one of these trials because they really want to try the, the drug and maybe they, you know, they might believe in it more than, than some other treatment.

   6. PAPeter Attia25:24

      Yep. Yeah. There are lots of things that can introduce bias to a phase two if it does not have randomization. Again, the goal would be to still randomize in phase two, um, because you really do want to tease out efficacy. So if a study, uh, if a, if a compound succeeds in phase two, which means it continues to show no significant adverse safety effects, which by the way doesn't mean it doesn't have side effects, right? It, the, every treatment has side effects. It's just that it doesn't have, um, side effects that are deemed unacceptable for the risk profile of the patient and it shows efficacy. So really you have to have these two things. You then proceed to phase three. And here a phase three is a really rigorous trial. This is a huge step up. It's typically a log step up in the number of patients. So you'd, you know, you're talking potentially thousands of patients here. Um, and this is absolutely a placebo controlled trial or not necessarily placebo, uh, but it, it can be sort of standard of care versus standard of care plus this new agent, but it is randomized whenever possible it is blinded and with drugs that's always possible. Um, and these are typically longer studies. Uh, so, so now because you have so much more sample size, you're going to potentially pick up side effects that weren't there in the first place. And of course now you really have that gold standard for measuring efficacy. And it's on the basis of the phase one, phase two, and mostly phase three data that a drug will get approved or not approved for broad use, which leads to a fourth phase, which is a post-marketing study. So phase four studies take place after the drug has been approved and they're used to basically get additional information because once a drug is approved, you now have more people taking it. Um, and they may also be using this to look at other indications for the drug. So, uh, we talked about this recently, right? A phase four trial, um, with semaglutide being used to look at obesity, uh, versus its original phase three trials, which were looking at diabetes. So kind of that's a, the drug's already been approved. This study isn't being done to ask the question, should semaglutide be on the market? No, it's on the market. It's basically expanding the indication for semaglutide in, in this case so that insurance companies would actually pay for it for a new indication. But given the size and the number of these studies, you're also looking for, hey, is there another side effect here that we missed in the phase three?

   7. BOBob28:08

      Right. And it might be the, the particular population that might have a different risk profile so that, uh, you might, you might have a different threshold.

   8. PAPeter Attia28:15

      That's right, because you're not doing this in patients with type two diabetes. You're doing this in patients who explicitly don't have diabetes but have obesity. Ah, you know, different patients. Can, are we going to see something different here? So yeah. So, um, anyway, that's, that's the, that's the long and short of phases one, two, three, and four.

   9. BOBob28:32

      Okay. So going back to observational studies, are there any things that you look for in particular that, that will increase or decrease your confidence in it, whether the, the, it's a pearl necklace or, or garbage?

   10. PAPeter Attia28:45

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Episode duration: 31:42