Hello.
My
name
is
Jennifer
Barb,
and
I'm
a
research
scientist
at
the
National
Institutes
of
Health
Clinical
Center.
I'm
going
to
talk
to
you
today
about
how
I
use
JMP
to
manipulate
research
clinical
medication
data
and
how
I
was
able
to
create
a
publication
quality
figure
to
show
how
the
patient
medications
were
used
through
the
course
of
a
treatment
protocol
at
the
Clinical
Center.
Clinical
data,
especially
in
a
research
setting,
can
be
extremely
noisy.
There
are
a
lot
of
staff
and
personnel
who
are
involved
in
research
protocols,
and
the
collection
and
storage
of
pertinent
research
data
is
not
always
streamlined.
I
will
talk
to
you
about
how
I
use
the
JMP
Graph Builder
tool
to
visualize
patient
medication
prescriptions
through
the
course
of
a
six-
month
treatment
protocol
and
how
we
were
able
to
visualize
what
is
called
a
Shannon Diversity
Index
with
relation
to
the
antibiotic
use
that
were
prescribed
in
the
patient
[inaudible 00:00:55]
in
the
clinical
research
setting.
I
will
go
through
how
I
created
the
illustration
in
JMP
using
four
patients
with
a
very
rare
disease
that
were
enrolled
in
the
treatment
protocol.
As
part
of
the
treatment
regimen
of
this
protocol,
the
four
patients
were
prescribed
a
range
of
antibiotics,
totaling
up
to
21
different
types
of
medications.
The
data
were
provided
to
me
in
a
long
format,
including
a
start
and
stop
date
of
medication
administration.
As
you
can
see
here,
I
zoomed
into
the
first
figure
of
the
poster.
What
we're
looking
at
here
is
a
snapshot
of
what
some
of
the
research
data
look
like.
In
the
long
format,
you
see
that
there
are
repetitive
rows
of
the
patient
ID
and
there
are
repetitive
rows
of
the
different
medications
that
the
patient
received
during
the
treatment
protocol.
There's
a
lot
of
redundancy
here.
In
addition
to
that,
we
have
a
start
of
medication
date
and
a
stop
of
medication
date
that
each
person
received.
One
of
the
first
steps
I
had
to
take
within
the
JMP
data
manipulation
tools
was
to
edit
the
medication
name
so
that
it
did
not
have
so
many
words
in
the
medication
name
and
also
did
not
include
the
dosage
information
so
that
we
could
use
this
as
one
of
the
axes
of
the
graph
that
I'm
going
to
make.
In
addition
to
that,
I
had
to
check
the
date
of
patient
consent
into
the
treatment
program
and
to
see
if
the
start
and
stop
date
of
that
person's
medication
administration
fell
within
the
treatment
protocol.
From
that
point
then,
I
had
to
normalize
each
person's
medication
start
and
stop
so
that
everybody
had
a
day
one
and
it
would
all
corresponded
to
the
certain
point
of
the
treatment
protocol.
All
of
this
information
will
be
used
to
create
the
figure
that
I
will
show
at
the
end
of
this.
Once
I
was
able
to
edit
the
medication
name
and
create
the
normalized
medication
start
and
stop,
I
will
then
use
the
Graph Builder
tool.
I
also
wanted
to
talk
about
one
other
aspect
of
this
particular
research
protocol,
and
that
is
the
fact
that
we
wanted
to
look
at
the
oral
microbiome
of
the
patients
in
the
treatment
program.
What
this
means
is
that
we
took
samples
of
each
patient's
oral
tongue
brushings
and
then
converted
those
into
specific
counts
of
bacteria
that
were
found
in
their
mouth.
What
we
ended
up
wanting
to
do
was
to
look
at
how
the
antibiotic
treatment
through
the
treatment
protocol
might
have
affected
the
oral
microbiome.
As
we
know,
antibiotics
can
drastically
change
your
gut
microbiome
and
can
cause
increases
and
decreases
of
different
microbial
diversity
in
the
gut.
But
one
question
that
has
not
been
elucidated
is
whether
or
not
antibiotic
use
would
also
affect
the
oral
microbiome.
What
I'm
showing
here
is
that
we
have
built
a
set
of
scripts
within
the
JMP
where
we
install
that
on
the
toolbar.
We
have
a
specific
set
of
scripts
that
would
calculate
the
Shannon Diversity
of
the
bacterial
counts
in
the
table
associated
with
the
medications
of
what
I
just
showed
on
the
previous
slide.
Back
to
the
medication
table,
the
first
step
that
I
took
was
to
open
up
the
JMP
Graph Builder
tool.
The
first
thing
that
I
did
was
to
drag
and
drop
the
medication
start
and
stop
date
into
the
X-
axis
as
shown
here.
Then
I
would
go
to
the
bar
graph
tool
and
click
that
to
make
the
data
into
a
bar
graph.
The
third
step
was
to
drag
and
drop
the
actual
antibiotic
shortened
medicine
name
into
the
Y-
axis.
And
then
finally,
in
order
to
create
the
graph
so
that
I
could
visualize
the
longitudinal
duration
of
medication
administration,
I
changed
the
bar
type
into
stock.
Finally,
as
I
talked
to
you
earlier
about
the
way
in
which
we
were
able
to
code
the
treatment
time
of
the
protocol
based
on
the
medication
start
and
stop,
we
also
were
able
to
stratify
the
antibiotic
use
into
this
different
time
point
of
the
treatment
protocol
as
here.
Now,
all
of
these,
if
you
are
familiar
with
the
JMP
Graph Builder
tool,
is
great
ways
that
there's
so
many
different
possibilities
on
how
you
can
manipulate
data
to
get
a
particular
graph
that
you
want.
And
finally,
one
last
thing
we
did
was
we
took
the
patient
ID
that
was
in
the
medication
table
and
colored
each
bar
on
the
graph
by
patient.
The
final
figure
looks
like
this.
So
what
you
see
here
is
all
of
the
different
antibiotics
that
were
prescribed
in
the
treatment
protocol.
You
also
see
time
point
B,
which
is
the
time
point
between
baseline
and
the
treatment
of
the
protocol,
and
time point
C,
which
is
the
intervention
point
starting
at
time
point
C,
and
then
the
end
of
the
treatment
protocol.
What
you
see
here
is
a
longitudinal
bar
indicating
the
amount
of
time
a
person
was
on
a
given
antibiotic.
And
then
you
also
see
each
of
these
different
bars
stratified
by
patient
color.
This
particular
figure
did
end
up
going
into
the
publication,
and
it
was
a
nother
way
to
look
at
a
large
table
of
medications
downloaded
from
our
research
database
into
a
graphical
form
to
visualize
all
of
the
different
medications
that
the
patient
received
during
the
treatment.
Now,
finally,
you
might
want
to
ask,
why
do
we
want
to
look
at
this?
One
thing
of
importance
for
us
was
to
actually
look
at
the
oral
microbial
diversity.
As
I
mentioned,
we
were
able
to
take
a
separate
table
that
corresponded
to
the
patients
within
the
treatment
protocol
and
calculate
what
is
called
a
Shannon Diversity
metric.
A
higher
diversity
indicates
higher
oral
microbial
diversity,
and
a
lower
index
indicates
lower
microbial
diversity.
From
within
JMP,
we
were
able
to
superimpose
the
treatment
leg
between
time
point
A
and
B
and
the
change
of
the
diversity
metric
from
time
point
the
start
of
the
treatment
to
the
end
of
the
treatment.
Also,
we're
able
to
look
at
within
one
patient
how
the
different
antibiotics
correspondent
to
this.
Then
the
second
leg
of
the
protocol,
we
were
able
to
see
a
slight
rebound
of
the
diversity
index
in
correlation
with
the
number
of
antibiotics
that
were
used
in
that
treatment
leg.
In
conclusion,
we
were
able
to
visualize
patient-
prescribed
antibiotics
through
the
course
of
a
treatment
protocol
using
the
JMP
Graph Builder
tool.
We
took
a
table
of
1,289
rows
of
medication
employed
in
the
protocol
and
created
a
simplified
graph
of
visualization.
We
also
were
able
to
calculate
a
Shannon
Diversity
Index
on
bacteria
data
associated
with
each
person's
oral
samples.
We
superimpose
these
two
graphs,
and
it
allowed
us
to
draw
conclusions
on
how
the
antibiotics
prescribed
to
each
patient
might
have
affected
the
oral
microbiome
of
individuals
in
the
treatment
protocol.
Finally,
our
group
has
used
the
graphical
nature
of
JMP
for
many
years
in
a
way
to
translate
complex
medical
research
data
into
data-
driven
discovery
and
investigation.
The
use
of
JMP
has
facilitated
many
publications
and
highly
cited
research
journals
for
our
group.
Thank
you
for
your
time
today.
