Hello, this is Tatsuki Koyama.
I'm an Associate Professor of Biostatistics at Vanderbilt University School of Medicine.
In this lecture, I'm going to talk about the sample size in adaptive clinical designs.
And these are the three big topics that I'm going to cover.
Let's start with general considerations for the sample size in clinical trials,
and these are the single stage conventional designs.
In general, a large sample size is needed if you want
a small type I error rate and a small type II error rate or a large power,
and these are the things that the investigator chooses.
There are also, a large sample size is required,
when the treatment effect to detect is small,
and one needs to make sure that its critical meaning is no difference,
and the treatment effect reflect the truth.
And then lastly, the large sample size is required when the data variability is large.
And the last two things,
the treatment effect and data variability,
these are the things that you may not have a good idea before the trial begins,
so sometimes, it requires you to have a good guess.
In the first example,