My name is Stephen George.
I am a Professor Emeritus
in the Department of Biostatistics
and Bioinformatics at Duke University.
My topic today is
"Data Fraud in Clinical Trials".
An outline is given on this slide.
There are five different areas.
First, we'll talk about some definitions
that are important in setting
the stage for the discussion,
some estimates of prevalence
of data fraud,
some key examples
that have been uncovered,
some information on motivation
and contributing factors,
and end with
some statistical detection methods.
The US Public Health Service
has defined "Research misconduct
means fabrication, falsification,
in proposing, performing,
or reviewing research,
or in reporting research results...
Research misconduct does not include
honest error or differences of opinion".
That last stanza is very important.
In this presentation,
I will focus on what I call "data fraud"
which is simply the fabrication
or falsification of data in clinical trials.
In addition to data fraud,
there are of course
other questionable research practices
in clinical trials.
I'm not going to talk about
these much today,
but it's important to note
that these may be at least as important,
if not more so, in producing
And these include inadequate
or inappropriate design and analysis,
selective reporting of results,
inappropriate subgroup analyses,
not admitting that some data are missing,
over-interpretation of results
from small trials,
post-hoc analyses that are done
but not admitted as being post-hoc,
and withholding details of methodology,
or someone's day-to-day
withholding the data.