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IPPCR 2019 Overview of Hypothesis Testing Part 4 of 5 — Transcript · YTtoTranscript

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IPPCR 2019 Overview of Hypothesis Testing Part 4 of 5

NIH VideoCast · 2,695 words · 13 min read · EN

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Below is the complete, readable transcript of IPPCR 2019 Overview of Hypothesis Testing Part 4 of 5 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.

>> Paul Wakim: Hello I'm Paul Wakim. I'm chief of Biostatistics and Clinical Epidemiology Service at the NIH Clinical Center and this is the fourth segment on hypothesis testing and in this segment, we're going to talk about subgroup analysis and interaction -- very important topics under hypothesis testing. So, let's start with what is a subgroup?

A subgroup is defined based on baseline, pre-randomization characteristics. This is important. Baseline pre-randomization -- meaning pre-randomization characteristic -- in other words, it is highly unrecommended -- recommended against creating subgroups based on post-randomization -- like adherence to a treatment or retention. It's really, highly not recommended to use those kinds of subgroups. So, subgroup defined on baseline

pre-randomization characteristics. Examples of subgroups: sex, race, ethnic group, age group, socioeconomic status, geographical location, severity or disease or disorder before randomization, or comorbidity with other diseases or disorders -- again, at baseline, before randomization. These are all examples of subgroups. So, what are subgroup analyses? They focus on differences in treatment effect among subgroups of trial participants.

So, now we're interested in the differences in treatment effect between the subgroups. So, if -- we're going to talk about sex -- males and females. Is the treatment effect different for females and for males? That's really the question. So, subgroup analysis can be pre-specified or not pre-specified. Pre-specified means that you put that question about the subgroup

in the protocol or in the proposal. And not pre-specified -- it means, you know, it was an afterthought. It was ad hoc. You thought about it, "Oh, why don't I do this since I have the data?" -- kind of thing. There's a huge difference, in my mind, between pre-specified and not pre-specified. A huge difference because one tells me --

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or tells the reader -- that this is something that you suspected -- that you were wondering about upfront -- rather than, "Oh, well we have all this data, let's try this, let's try that." So, it's an important distinction in my own mind. Typically, the trial sample size is not based on subgroup analysis.

Some key points about subgroup analysis -- they are very important, very important. And I'm going to talk about this -- but basically what it says is, you know, you could have an overall treatment effect, but as soon as you go down to subgroups you see that there's a different story depending on which subgroup your talking about.

So, it is important to try to try to see if there are differences. They are valuable for generating new hypotheses. So, again, because the sample size and the experiment is not designed for subgroup analysis -- if -- most of the time it is not -- then they are not definitive, but they are valuable for generating hypotheses.

They should be based on clinically plausible hypotheses. So, you should ask yourself, "Okay, let's try to see if there are differences in these subgroups?" Well -- you have to have some kind of a logic -- a reason to do that, not just create subgroups just to try things. They should be pre-specified -- highly recommended.

They should be limited to a handful because you could say, "Oh, I'm going to pre-specify 200 subgroup analyses." Well, you should try to limit the number of subgroup analyses you're doing. They should not be attributed as definitive results. Their limitation should be clearly reported in manuscripts. So, again, this is a little bit of the modesty

and the uncertainty. So, here's a table -- I hope you can see it clearly, even though it's -- the characters are small. I like this table because it's very compact. It has a lot of information in a -- in one table. And -- so, what you have on the left is the subgroups.

So, it's doing a subgroup analysis but on several subgroups: the sex, the age, whether they have diabetes or not. What this trial is about -- it's a multi-centered trial in Spain and the participants with high cardiovascular risk but no cardiovascular disease at enrollment, were randomly assigned to one of three diets. One is a Mediterranean diet

supplemented with extra-virgin olive oil. Another is a Mediterranean diet supplemented with mixed nuts. And the third diet was just a control diet -- advice to reduce dietary fat. And the primary endpoint -- the -- was a composite of myocardial infarction, stroke, and death from cardiovascular causes. So -- and the overall hazard ratio

for Mediterranean diets combined -- so, in this table you see the combined Mediterranean diet. So, they put the two Mediterranean diets together versus the control -- and the hazard ratio between the two was .71 -- the overall -- and the p-value was .005. So, that's, kind of, the context of the trial. But what -- the reason I'm showing this slide

is, of course, to talk about subgroup analysis. Let's take the top one -- sex -- males and females. And the way not to do it -- that this is about hazard ratio -- so you see this dashed like, this vertical that's set at one. Basically, it says that if the interval crosses one there's no difference.

A hazard ratio of one means there is no difference between the Mediterranean -- the two Mediterranean diets and the control. And, like it says, on the bottom of the table, if you're on the left, the Mediterranean diet is better, if you're on the right the control diet is better. Okay, so one means there's no difference.

So, the way not to do it -- the way not to do it is to look at the two intervals that you see for males and females -- you see those two intervals. The female interval crosses one and the male does not. So, the way not to do it -- not to --

not the way to interpret it is to say, "Oh, well, it is better for males -- the Mediterranean diets are better for males but are not better -- or are just the same as the control for females." This is incorrect way of doing it. So, what's the correct way? The correct way is you look at the p-value for interaction.

And the p-value for interaction is .62. So, there doesn't seem to have an interaction. What this -- the -- what you're really asking yourself is the hazard ratio different for males and females. So, you are comparing the two intervals, not with respect to each one separately with respect to the one, but you are comparing them.

and you can see that the two intervals are not different. So, males and females are not different when it comes to the effect of -- the overall effect of the Mediterranean diet. Okay, just for fun or for exercise, let's look at the BMI. It says the p-value for interaction is .05. Now, let's look at what's going on with this BMI interaction.

Well, you can see that for BMI greater than 30, it seems that this is, you know, kind of different than -- certainly than the one for BMI between 25 and 30. So, it is -- you could say, based on this table -- again with modesty and uncertainty -- it appears that for participants or for individuals

with BMI greater than 30, the Mediterranean diet is better but not much for the ones below 30 BMI. And that's, kind of, reinforced -- confirmed with that p-value. Again, what we're trying to -- putting pieces together to come up with a conclusion rather than just focusing on one number. A better way even to do it is instead of just,

you know -- here -- I'm not going to go over the trial in detail -- but here, notice the dashed line on -- over one. But what they did -- the others did here -- is also put a solid line over the overall hazard ratio, which was not in the previous table. So, not only it says this is the line

one that represent no difference, but it says this is the line that represents overall hazard ratio." And if you take the first subgroup -- the positive and the negative nodal status -- you can see that even though one covers one and the other one does not, they both cover the overall. And the two are not --

the two subgroups are not different in terms of the treatment effect. So, that's for subgroup analysis. So, now I'm going to talk about interaction in statistical models. It's a very important concept. We just talked about interaction -- that table had the interaction p-value. So, lets talk about that. What is an interaction? So, in a model you have, let's say, an end point.

So, we're talking about cigarette smoking. We talked about the nicotine patch and the placebo patch. We talked about a number of cigarette smoke during the first three months of treatment. So, put the statistical model and we have an endpoint where we say, "Okay, there's the treatment effect." This is whether the medication is better than placebo.

That's what we test. Then we have sex effect. We add a sex effect in the model and that answers the question, is there a difference between females and males overall, over both treatments? And then we add treatment by sex interaction. So, what is it really means treatment by sex interaction? It means, is the treatment effect

different for females and males? Is the difference between placebo and medication different between males and females? I'm going to show you some illustration on that. So, it says, does the effect of the treatment depend on sex? Is the treatment effect different for males and females? If yes, there is a treatment by sex interaction.

If no, there is no interaction. Okay, so, questions for you. Can there be no overall treatment effect and yet a treatment by sex interaction? Can you have a -- you don't have an overall treatment effect and yet there is an interaction? The answer is yes. And I'll show you. Can there be no overall sex effect

and yet a treatment by sex interaction? And the answer is yes. Can there be no overall treatment effect and no overall sex effect and yet there is a treatment by sex interaction? And the answer is yes. In fact, all eight combinations -- eight combinations come from treatment effect being significant or not -- yes no --

sex effect being significant or not -- yes no -- so two by two. And a treatment by sex interaction being significant or not -- another two. So, two by two by two, you have eight possible combinations and all eight are theoretically possible, although, some are less likely than others. And I'm going to show you an illustration of each one

of those eight combination and, hopefully, that's going to help you, kind of, see visually what interaction means and how is it different from treatment effect and sex effect. So, lets start. So, here's the graph. So, you have the outcome -- so high is bad, number of cigarettes smoking -- high is bad. The green -- the diamond green is placebo.

The purple circle is the active medication. Okay? And then we have male -- females and males. Okay? So, this -- what I'm showing you is the result of the clinical trial. So, my questions is is there a treatment effect? No, because basically the placebo and the medication are on top of each other. There's no -- there's no effect.

There's no distance between the purple and the green for females or for males. Is there a sex effect? Are females -- do females smoke more or less than males? And the answer is no. Looks like they smoke about the same. Is there a treatment by sex interaction? Is the difference between placebo and medication

different from the difference between placebo and medication for males? And the answer is no. It's the same distance. So, that's one out of eight. Let's go over another one. Is there a treatment effect? Yes, now the placebo is higher than the medication. So, there is more cigarette smoking so there is a treatment effect. Is there a sex effect?

Well the way to look for sex effect -- look at the middle of the two points for females and the middle of the two points for males. Are they different? So, over both treatments, do females smoke more than males? And the answer is no. Is there a treatment by sex interaction? For the treatment by sex interaction,

you look at the distance between the two treatments for females -- that distance. Is it really the same as the distance for males? And the answer is yes. The distance is the same and there is no interaction. How about this? Is there a treatment effect? Yes. The active medication is lowering cigarette consumption.

Is there a sex effect? Well, yes because now you can see -- if I take the middle of the two points for females it's lower than the middle of the two points for males. So, overall, over both treatments, females smoke less than males. That's a sex effect. But is there a treatment by sex interaction?

No, because the distance -- the effect, the difference is the same for females and males. How about this? Is there a treatment effect? Yes. Is there a sex effect? No, because if you take the middle, they are the same. If you take the middle of the two points, and the middle of the two points they are the same.

Is there a treatment by sex interaction? Yes. Why? Because the distance -- the effect for female was a lot bigger than the effect for males. So, there's a difference in treatment effect. Is there a treatment effect? No, because they cancel each other. In this case, the two -- the two treatments cancel each other.

It's better for females but worse for male -- the medication. Is there a sex effect? No, because the middle of the two points is the same. Is there a treatment by sex interaction? Definitely. This is called a qualitative difference. Is it -- it is good for female and harmful for males. So, the ones for males --

medication actually makes them smoke more. Of course, all of these are hypothetical examples. Is there a treatment effect here? No. The distance is the same. Is there a sex effect? Yes, because females here appear to smoke less than males. Is there a treatment by sex in interaction? No. Let's go to the next one. Is there a treatment effect?

Yes. The green line is above -- clearly above the purple line. Is there a sex effect? Yes. Females smoke more than female [sic]. Is there a treatment by sex interaction? Yes, this is called a quantitative. It goes in the same direction for both females and males -- the medication does good, but it does good less for males than for females.

The distance is bigger for females than for males. Is there a treatment effect? No, because they -- again, they cancel each other. Is there a sex effect? Yes, because females here smoke less than males. And is there a treatment by sex interaction? Yes. Again, it flips, so it's a little bit good for females --

the medication is a little bit good for females and a little bit bad for males. So, here are the references. I hope you got a sense of subgroup analysis and interaction. In summary, results from subgroup analysis should not be interpret as definitive and their limitation should be clearly reported in manuscripts. And a statistical test for interaction determines

whether the treatment effect is different for different subgroups -- e.g. males and females. And my questions to you are what are subgroup analyses? Why are subgroup analyses important? And why is a statistical test for interaction important? Thank you for watching.