My name is Ben Saville.
I'm a statistical scientist
from Berry Consultants.
I'm also an adjunct
at the Vanderbilt University School
of Medicine in Nashville, Tennessee.
And this is a talk on Bayesian
Adaptive Designs for Clinical Trials.
Many of us have probably
moved to a new place
or a new city and started a new job,
and you might be posed with
the question of how do you get to work
and you know there are
different options, of course.
You could take a highway,
you could take the back roads,
you have to figure out
which way you're going to go.
You want to go the optimal way
to get you to work as fast as you can
and without stressing you out too much.
There are different solutions
to decide how to do this.
One possible solution
which I'm sure we've all done
is you take 30 envelopes
in your car with you
and it has one of three routes on it
and you randomly select an envelope
and you drive that route.
Record the drive time and at the end
of the 30 days you look at your data,
that point you drive whichever route
had the fastest average drive time.
I'm sure we've all done that
and I'm being facetious here
because obviously that's something
that probably no one would do.
The more intuitive way
that mostly all of us
would decide how to drive to work
would be to pick one route,
you drive it the first day,
you make a note of how long it took you
and then maybe the next day
you drive a different way.
And if it took you longer
than the first day
then maybe you switch back to the first
and we iterate through this process
of trying different ways.