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Designing Trials Efficiently: An Introduction Part 1 — Transcript · YTtoTranscript

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Designing Trials Efficiently: An Introduction Part 1

NIH VideoCast · 2,396 words · 12 min read · EN

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Below is the complete, readable transcript of Designing Trials Efficiently: An Introduction Part 1 by NIH VideoCast on YouTube. Read the full text, copy any part you need, or generate a transcript for any video with our free tool.

My name is John Powers. I'm a senior medical scientist. I work here at NIH. I'm an employee of Leidos Biomedical Research, but I work here as a contractor and support of the collaborative clinical research branch within the divisional -- division of clinical research in the National Institutes of Allergy and Infectious Diseases. So, I know this is one of the earliest talks

that you're getting in the introduction to principles and practice of clinical research course syllabus. And so, what we're going to do today is really cover some topics that will be covered in much greater depth in detail later. So, let's -- first of all, if we're going to talk about efficiency and clinical trials, let's define what are clinical trials, and let's define

what efficiency actually means. So, a clinical trial is a controlled prospect of study, enrolling human subjects, often used to evaluate the effectiveness and/or harms of interventions in, you know, the treatment, the prevention or diagnosis of disease. So, that's why I was joking. When I see mouthwash that's clinically tested, I doubt that they did

a clinical trial comparing this type of mouthwash to that type of mouthwash. So, I don't know what they mean by "clinically tested." On the other hand, efficiency, at least in physics, means the ratio of useful work to the energy supplied to it. So, in clinical trials, what it means is getting valid and reliable answers

to important questions with the least amount of resources. You don't want to put patients at risk by doing a study that's not valid. On the other hand, you don't want to have not enough people in the study to be able to answer the question. So, to be able to do a study efficiently means actually more planning,

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not less work. So, if you want to do a study with fewer patients, that translates inversely into more work. So, inverse relationship: fewer patients, more work. Because you have to think about it ahead of time. Back in 2001, the Institute of Medicine put out this monograph, which I'm sure you'll want to read because it's

300 pages long, called Small Clinical Trials: Issues and Challenges. And in it, they say, "Clinical trials with small numbers of participants must address broad sets of issues different from those that must be addressed in trials with large numbers of participants. It is in those circumstances of trials with small sample sizes that approaches

to optimization and study design and data interpretation pose greater challenges." In other words, that can be summarized in two words: more work. But that gets the issue. What's a large or small trial? What does that actually mean? So, I went to find some large trials, one that has adequate sample size or numbers of patients enrolled,

that's what sample size means, to answer the primary research questions. So, large means, large enough. The very first randomized trial that was ever done was in streptomycin for tuberculosis. It only had 105 patients in it, because streptomycin, compared to the standard of care of the day of bedrest, lowered mortality by 20 percent,

and it was still able to show a clinically significant difference with only 105 patients. On the other hand, some modern cardiology trials have had over 10,000 patients in them to be able to show any kind of effect at all, because the effect is very small. So, what means large is large enough the answer the question.

A trial with very few participants like that TB study, they still have adequate statistical power if the effect of the drug that you're testing is very large, like that one was where you had a 20 percent decrease in mortality. So, this means balancing, exposing research subjects to potential harms of the experimental intervention with the bounds of obtaining valid answers.

But remember, if you don't obtain valid answers, there wasn't a point to do the study to begin with. So, this relates to the ethics of the study to start with. And in fact, there have been a number of editorials in the literature complaining about this issue, that trials from their inception are too small to begin with,

so you can't really answer the question that you set out to address. And so, this is one editorial which actually argued that it's unethical to start those studies to begin with. The other side of the argument is that, well, if I do a small study, somebody someday is going to put that together

in a med analysis and answer the question for real. I have to say, personally, I find that to be a very weak response. You don't know what's going to happen in the future. For those of you who are familiar with Yogi Berra, who was a famous New York Yankee, who just passed away a couple of weeks ago -- Yogi

said, "Prognostication is very difficult, especially when it concerns the future." So, you don't know what somebody else is going to do, and that also doesn't have anything to say about the validity of your study. It doesn't explain why you should be doing the study now, if you're expecting somebody else to do it better in the future, maybe then somebody else should do it.

So, the IOM also says, "A proposed study that cannot answer the question being asked because the necessary sample size cannot be obtained should not be conducted on ethical grounds. That is, it is unacceptable to expose patients or research participants to harms, even inconveniences, if there is no prospect that useful and potentially generalizable

information will result from the study." That's pretty clear. So, especially on what I do, there have been discussions about studies in patients who are infected with resistant pathogens, talking about the use of smaller data sets. First of all, it's unclear what's meant by "smaller data sets," but one could assume that what they mean is "study's too small

to actually answer the research question that was posed." So, that would seem to violate a basic ethical premise to begin with and why you're doing the research to start with. So, the other thing that the IOM says is that really doing small trials is a last resort, and it says that they should only be done when there are no alternatives.

And it says, "This cannot be overemphasized, and the committee is not encouraging the use of small clinical trials, but rather providing advice on strategies to consider in the design and analysis of these trials." And actually, it even says, "For some trials, it might be impossible to definitively answer research questions with a high degree of confidence.

And in those cases, perhaps, the best that one can do is assess the next set of questions to be asked." So, again, they're saying that if you can't definitively answer the question, maybe you need to move on to a different question. So, what's the issue about small numbers in a study? Why is that a problem to begin with?

The problem is that the smaller the numbers of people in the study, it increases the variability of the results that you get and increases the play of chance on the results that you might actually obtain. One of the examples I always like to use is -- I'm a football fan. My wife would love it if fantasy football disappeared

from the face of the earth, because she says my mood on Sundays goes up and down, depending upon how my fantasy football team is doing. But if you actually look at the coinflip in a Superbowl, the NFC team, up until two years ago, had won the coinflip in the Superbowl 14 years in a row.

Now, what do you think that's due to? The ref cheating? The ref putting his thumb on the scales? No. Because there's no correlation at all between who wins the coinflip and who wins the game. So, it's not that big a deal. So, what do you know is going to happen over the next succeeding 50 years

of Superbowls? Anybody? It's going to even out, right? The AFC team's going to have some long run of winning the coinflip, and it's going to end up 50-50 over the next 100 years or so. But the point is, me only looking at the last 14 years of coinflips is too small. And what I'm looking at is random chance, that we happen to get 14 NFC teams

winning in a row is nothing more than random chance. Is it statistically significant? You bet you. 0 out of 14 comes out to have a p-value of less than 0.05. But it's wrong. It's wrong because I didn't take a random sample, and I didn't look at a sample that's big enough, which shows

you why p-values do not have anything to do with bias at all. And it also means that even though you reach statistically significant results, those results may not be generalizable, or they may not even be true. So, the other issue is too many variables are present in a small study to assess cause and effect.

In other words, if you're looking at really sick patients, there's a lot of stuff going on with those people. And you may not be able to actually balance all those variables between the test in the control group. You may only be able to discern gross effects, like, "Did people ever die," in a very limited ability

to analyze the baseline factors which may result in death. So, if you only do a study with 20 people, you're not going to be able to analyze whether older people die more often than younger people, because there aren't going to be that many people in the study to be able to answer that question.

And you may be completely incapable of identifying any adverse effects because you can't figure that out, especially if you study 25 people and the side effect happens in 1 in every 50 people, you may see nothing. So, that gets to the other issue of "absence of evidence is not evidence of absence." So, the fact that you do a study

and you don't see side effects of the drug doesn't meant it doesn't have side effects. It may just be really depending on the numbers of people that you actually studied. So, where does situations where smaller clinical studies maybe justifiable? So, they may be in rare diseases where there aren't many people who have the disease to begin with.

They may be unique study populations. And by "unique study populations," I will show you what the Institute of Medicine uses as the example, astronauts in space. They're not talking about people with the disease, they're talking about really rare situations. So, individually tailored therapies, environments that might be isolated like some guy flying to Mars

and who might be -- by the way, I saw that movie, "Interstellar," last night. Did I like that movie? They had to wake me up at the end. So, emergency situations and public health urgencies are the places where we might actually want to do these things. So, while smaller clinical trials are a last resort, efficient clinical

trials are always justifiable and really what you should want to be doing. So, what we're going to do here is look at different methods to improve the efficiency of a study which are useful or not, depending upon what situation we're talking about, what disease we're talking about, what research questions you're trying to answer,

and what setting you're actually doing the study in. But these kinds of things are always applicable whether you're doing a larger study or a smaller study. So, first of all, we need to talk about what are the basic components of a clinical research study to begin with. Because if we're going to talk about altering some of these things

to try to make the study more efficient, we've got to know what those basic building blocks are to begin with. So, the first thing is that we spent the first half of this talk on is you have to have a clear objective of the study to begin with. If it's a comparative, or what I call analytical study, you need to actually

have a quantitative comparison between the test group and the control group. That does not apply if it's just to descript the study. You need to select patients for inclusion in the study. And if it's a comparative study, you need to make sure there's baseline comparability between the test group and the control group.

Now, that applies if you are testing interventions. If you're looking at risk factors for an outcome, you don't need baseline comparability, because that's in fact what you're trying to analyze. What are the differences between the people at baseline who get one disease versus another? You want to minimize the biases in the study.

You want to have well-defined and reliable outcome measures that actually means something to patients. Many outcome measures and clinical research studies are based on what the doctor chooses to do. So, for instance, in what I do in Infectious Diseases, many of the studies in the past have been based on the doctor's judgment that the person doesn't need more antibiotics.

But it's not clear why the doctors were making those judgments. So, we've been trying to move away from that to get to more patient-centered outcomes and measure, "Is the patient living longer, or are they living better?" In other words, if they're alive, which is easy to measure, can we have some patient-centered measures

that measure patient disability or patient symptoms which actually showed their ability to function in their daily lives, rather than just judging whether the doctor needs to give more medicine or not. Nobody comes to the doctor saying, "My goal being here is to get more medicine." They actually want to feel better in their lives.

And then finally, appropriate statistical analysis. So, that is the summary of this course basically in a nutshell, right? You're going to hear about all these different things in much greater detail as march through the rest of this course.