Clinical trials are experiments designed to evaluate new interventions to prevent or treat disease in humans. The interventions evaluated can be drugs, devices (e.g., hearing aid), surgeries, behavioral interventions (e.g., smoking cessation program), community health programs (e.g. cancer screening programs) or health delivery systems (e.g., special care units for hospital admissions). We consider clinical trials experiments because the investigators rather than the patients or their doctors select the treatment the patients receive. Results from randomized clinical trials are usually considered the highest level of evidence for determining whether a treatment is effective because trials incorporates features to ensure that evaluation of the benefits and risks of treatments are objective and unbiased. The FDA requires that drugs or biologics (e.g., vaccines) are shown to be effective in clinical trials before they can be sold in the US. The course will explain the basic principles for design of randomized clinical trials and how they should be reported. In the first part of the course, students will be introduced to terminology used in clinical trials and the several common designs used for clinical trials, such as parallel and cross-over designs. We will also explain some of the mechanics of clinical trials, like randomization and blinding of treatment. In the second half of the course, we will explain how clinical trials are analyzed and interpreted. Finally, we will review the essential ethical consideration involved in conducting experiments on people.
Lecture 5B: Subgroup Analysis
Loading...
From the course by Johns Hopkins University
Design and Interpretation of Clinical Trials
1359 ratings
From the lesson
Outcomes and Analysis
This week focuses on a key design issue - selecting the primary outcome. We will also cover the gold standard for analysis of clinical trials, which is including all the participants in the analysis regardless of their actual treatment.
Meet the Instructors
Janet Holbrook, PhD, MPHAssociate Professor, Epidemiology
Bloomberg School of Public Health
Lea T. Drye, PhDAssistant Scientist, Epidemiology
Bloomberg School of Public Health
In this final section, I'm going to discuss subgroup analyses, which
is another kind of statistical analysis
that's frequently performed in clinical trials.
In the context of clinical trials subgroup analyses are analyses where we're looking
to see if there are varying treatment effects in different subsets of patients.
So why do we want to do subgroup analyses?
Well, we'd like to check the consistency of the treatment effect across subgroups.
For instance,
we'd like to know if the effect is the
same in women and men or in children and adults.
Or we might think that perhaps the treatment effect would vary
for people with less severe disease versus those with more severe disease.
And the two most common methods for doing subgroup analyses is
to do stratified analysis or to do a test of interaction.
And finally when we talk about doing a
series of subgroup analysis we need to think
about the problem of multiple comparisons.
To do a subgroup analysis by stratification, you estimate
the treatment effect separately in each of the subgroups.
So you estimate an effect in men and you estimate an effect in women.
And using this method, you can test whether there is a significant effect
in men, and you can test if there's a significant effect in women.
But what you cannot do is test whether the
effect in men differs from the effect in women.
So the important point here is that to
test whether there's a treatment effect separately in a
series of subgroups is not the same as testing
whether the treatment effect differs in the various subgroups.
The only way to make a statement about whether the treatment effects are
different in one subgroup versus another is to do a formal test for interaction.
So, to do this test we build a statistical model that includes main
effects for our treatment group and for our subgroups and we
use interaction terms to test
for interactions between treatments in subgroup.
And in this way, we can see if the treatment effects vary by subgroup.
We can also use that model to estimate the treatment effects
in the various subgroups like we could do with the stratification method.
Frequently, when we're do subgroup analyses in a
clinical trial, we aren't doing just one subgroup analysis.
So we aren't only interested in seeing whether
the effects differ in men and women, but
we're also interested to see if the effects
differ across people with different co-morbid conditions at baseline.
Or people who are taking different concomittent medications.
So in practice, we might do a whole series of subgroup analyses.
And when significance tests are performed in multiple subgroups, the
overall Type I error rate for subgroup analyses is inflated.
That means, by chance alone, the probability
that the pay value for a subgroup
difference is less than 0.05 in one or more subgroups is greater than 5%.
So the more subgroup analyses that we do, the more likely it
is by chance alone that we will find a statistically significant difference.
So this is problematic if you use statistical significance as the
only criteria for judging whether or not a difference is true.
To illustrate this point I've included, a graph from a
publication in the New England Journal of Medicine, that shows
the probability of getting a false positive, which is getting
a P value, less than the cutoff just by chance alone.
And on the y-axis
we have the probability of a false positive.
And on the x-axis we have the number of subgroup tests that were performed.
And, you can see that, as you do more
tests the probability of getting a false positive increases.
And, in fact, if you do 40 subgroup tests the probability
of getting at least one false positive is close to 90%.
The probability of getting at least two false positives is about 60% and
the probability of getting at least three false positives is close to 30%.
So the cautionary note here is that we shouldn't get overly excited about
significant findings that pop up when we're doing a series of subgroup analyses.
Instead we need to interpret these, with appropriate skepticism.
So that is, that's not to say that we shouldn't do subgroup analyses.
Clinical trials represent a significant investment of time and
money for the investigators and the sponsors,
and also potential health risks for the participants.
So it's our obligation to gain the maximum amount of
knowledge that we can from the data that we collect.
And it's important to determine if there are subgroups of people
who are more or less likely to be helped by an intervention.
So even if we think that the subgroup
analyses are explorative and not definitive, they can help
to guide future research priorities and they're important.
But how do we perform and report subgroup analysis in a valid and transparent way?
Well, as much as possible, the subgroup analysis
should be prespecified and if there are important subgroups,
investigators might consider inflating their sample size, so
that they have a decent power in that subgroup.
It's important in publications that investigators report
the number of separate subgroup analyses that
were performed so that the reader has
some idea about the probability of false positives.
In some cases, people will adjust for multiple comparisons.
So they'll make an adjustment in the level
of significance that's required to declare a difference significant.
And finally, epidemiologists and statisticians
really stress the importance of reporting confidence intervals
instead of or in addition to the p-values,
again so that the reader can see the
uncertainty and the estimate of the subgroup effect.
So these are some guidelines that we think
summarize how to proceed cautiously with sub-group analyses.
And this brings us to the end of the lecture
on analysis issues, and in this lecture we've reviewed analysis philosophy,
such as intention to trade, and the difficulties in
the analysis, such as missing data in subgroup analyses.
Explore our Catalog
Join for free and get personalized recommendations, updates and offers.
Coursera provides universal access to the world’s best education,
partnering with top universities and organizations to offer courses online.
© 2019 Coursera Inc. All rights reserved.
