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0:00
Hello, my name is Marc Buyse,
I'm the Chief Scientific Officer
of CluePoints,
a company devoted
to central statistical monitoring
of clinical trials
and also
a professor of Biostatistics
at Hasselt University in Belgium.
Today,
I'm going to talk about methods
to detect fraud and errors
in clinical trials.
Why is this problem important?
0:23
If we look at the number
of submissions to the FDA
between 2000 and 2012,
they were 332
such submissions
of which exactly half failed,
151 failed, at the first cycle,
and the other half was approved
at the first cycle.
So that is a high failure rate.
And of those applications
who failed,
80 were never approved,
so half of them
were never granted approval.
So this is a high failure rate.
And why do
so many applications fail?
The next slide shows you
some reasons for failures.
0:58
If we look at the reasons,
study conduct, and, in particular,
missing data
or data integrity problems
caused about 7% to 9%
of the applications to fail
either during the first cycle
or during any cycle.
And again, that is probably
too high a percentage
to be acceptable.
And if we look at the clinical data
submitted
to support the application,
again about 24% to 36%
of the applications fail
either during the first cycle
or at any cycle
because of inconsistent results
between trials, between sites,
or between endpoints.
And what I'm going
to concentrate on today is
differences in clinical data
submitted by different sites
in a clinical trial.
And again, this is important
because it is the cause
of between a quarter and a third
of the rejections
of new applications for approval.
