Hi,
I'm
Sam
Gardner,
and
I'm
presenting
today
with
Rebecca
Lyzinski.
We're
going
to
be
talking
about
how
we
use
Graph
Builder
and
Tabulate
in
applications
for
visualizing
data
from
clinical
trials.
We
have
a
product
called
JMP
Clinical,
and
it's
a
focused
and
specialized
product
for
clinical
trial
data
review.
We
give
users
straight
out
of
the
box
functionality
to
do
a
thorough
review
of
clinical
trials
at
the
study
site
and
subject
level.
It
is
a
product
that's
used
across
the
pharmaceutical
industry
and
at
several
regulatory
agencies
across
the
world.
It's
built
on
top
of
JMP
Pro,
utilizing
JMP
scripting
language,
and
it
provides
a
user
interface
for
data
management,
configuration,
and
standardized
reports.
We
utilize
data
standards
as
part
of
JMP
Clinical.
We
utilize
the
CDISC
study
data
tabulation
model
and
analysis
data
model
as
required
input
formats
for
the
data
that
you're
going
to
visualize.
This
allows
us
then
to
develop
standardized
tools.
In
general,
if
you
want
to
develop
standardized
tools,
having
a
data
standard
to
follow
for
the
input
data
really
enables
you
to
do
that.
We
use
Graph
Builder
and
Tabulate
across
almost
all
of
our
reports
that
we
have
in
JMP
Clinical.
We
want
to
show
you
how
we
create
the
graphs
that
we
use
in
JMP
Clinical
so
you
can
learn
more
about
these
important
platforms,
Tabulate
and
Graph
Builder,
and
how
you
could
use
them
for
your
analysis
work
and
if
you
wanted
to
make
standardized
reports
yourself.
We're
going
to
show
you
several
of
the
analysis
reports
that
we
have
in
JMP
Clinical:
our
adverse
events
distribution
that
utilizes
Graph
Builder
and
Tabulate,
our
adverse
events
risk
report,
which
uses
a
multipanel
Graph
Builder
that
also
utilizes
virtual
joins
between
data
tables,
and
it
applies
some
data
filters.
We
also
are
going
to
show
you
our
findings
time
trend
result,
which
also
uses
a
complicated
graph
builder,
a
column
switching
and
virtual
joins.
Then
we'll
finish
up
by
showing
you
an
adva
nced-level
collection
of
graph
builders
that
we
use
for
our
patient
profiles.
One
of
our
most
popular
reports
is
the
Adverse
Event
Distribution
Report.
This
report
shows
a
bar
chart
at
the
top
and
a
tabulate
underneath
it.
The
bar
chart
has
dictionary-
derived term
on
the
X-
axis,
which
is
a
way
of
grouping
adverse
events,
and
planned
treatment
group
on
the
Y-
axis.
Each
bar
represents
a
count
of
the
adverse
events
for
a
given
dictionary-derived term.
Underneath
we
have
a
tabulate
that
is
also
showing
dictionary-derived term,
but
it
also
groups
the
adverse
events
by
body
system
or
organ
class.
We
have
two
different
grouping
variables.
The
columns
represent
one
column
for
each
treatment
group,
one
for
Nicardipine
in
the
study,
and
another
for
placebo,
as
well
as
a
total
column.
The
ends
represent
counts
and
the
percents
are
the
percents
of
subjects
with
an
adverse
event.
Down
at
the
bottom,
there's
an
all
row,
which
represents
any
subject
that
had
an
adverse
event
in
the
given
column.
For
example,
we
have
882
subjects
in
this
study
that
had
at
least
one
adverse
event.
One
of
the
options
that
JMP
Clinical
includes
is
a
way
to
stack
the
bar
chart
in
the
table.
For
example,
you
can
stack
by
severity
or
intensity.
This
creates
a
stacked
bar
chart
with
green
bars
representing
mild
events,
yellow
for
moderate,
and
red
for
severe.
It
also
splits
out
the
table
by
mild,
moderate,
and
severe
events.
In
order
to
recreate
these,
we
can
pop
out
the
data
table.
For
the
graph,
we
go
to
Graph
and
Graph
Builder.
We
select
our
planned
treatment
group
and
put
that
on
the
group
Y.
Make this
a
little
bigger.
There
are
a
lot
of
columns
in
this
data
table,
so
we're
going
to
search
for
dictionary-derived term
and
we'll
grab
that
and
place
it
on
the
X-
axis.
This
is
pretty
close
to
what's
on
the
report,
except
for
the
ordering.
If
you
right-click
on
the
X-axis
and
go
to,
Order
By,
we
can
select
Count
Descending,
and
now
it's
ordered
the
same
way
that
it
was
in
the
report.
If
we
want
to
also
add
that
stacking,
we
can
search
for
severity
and
drag
that
variable
over
to
the
overlay.
That
gets
us
close
to
what
we
saw,
but
the
bars
are
stacked
side-by-side.
In
order
to
change
that,
we
can
go
to
the
control
panel
and
instead
of
side-by-side,
select
Stacked
for
bar
style.
Now
we're
back
to
our
original
graph.
Next,
if
we
want
to
recreate
the
tabulate,
we
go
to
Analyze
Tabulate.
Again,
select
Plan
Treatment
Group
for
our
columns.
This
time
we're
going
to
select
dictionary-derived term
and
put
that
in
the
first
grouping
column.
But
we
also
want
to
select
Body
System
or
Organ
Class
and
put
that
as
a
grouping
column
as
well.
By
default,
they
show
up
as
two
separate
columns.
We're
going
to
select
both
columns,
right-click
and
go
to
Stack
Group
and
Columns.
That
allows
them
to
both
be
concatenated
into
one
column.
To
add
to
our
table,
we
are
going
to
drag
the
little
N
over
underneath
our
treatment
group
so
that
we
get
our
counts
and
the
percent
next
to
the
N.
By
default,
these
are
two
separate
columns.
In
order
to
create
one
column,
we're
going
to
first
drag
the
sum
up
above
so
that
Tabulate
knows
both
the
N
and
percent
are
supposed
to
be
sums.
We
again
select
both
columns
and
right-click.
Under
Pack
Columns,
go
to
Pack.
Now
we
see
the
account
and
the
percent
in
one
column.
The
percent
has
no
formatting
on
the
number
of
decimal
places.
If
we
go
to
Change
Format
and
go
to
the
percent,
we
can
change
from
best
to
one
decimal
place.
Now
we
have
a
better
formatted
table.
One
other
thing
that's
missing
is
we
have
a
group
N
that
we're
going
to
stick
underneath
our
treatment
groups.
That
way
we
can
see
how
many
subjects
were
in
each
treatment
group.
You'll
also
notice
by
default
that
these
rows
for
the
body
system
organ
class
are
missing
values.
In
order
to
fill
those
in,
we're
going
to
select
Add
Aggregate
Statistics.
Now
all
our
values
have
been
filled
in.
Now
we
just
need
to
do
a
little
cleanup.
Add
Aggregate
Statistics
adds
all
columns
for
each
of
our
different
columns.
The
first
step
is
we're
going
to
delete
some
of
these
just
to
clean
it
up
and
we'll
get
rid
of
this
all
at
the
end.
In
order
to
get
the
columns
back
together,
we
again
have
to
drag
the
sum
up
above
and
the
N
and
percent
are
now
back
in
one
column.
We
can
also
right-click
on
the
group N
and
remove
column
label
and
right-click
on
the
sum
and
we're
just
going
to
change
the
item
label
to
be
missing
so
that
it's
a
little
cleaner.
One
final
thing.
You'll
notice
that
these
ends
are
a
little
bit
bigger
than
what
was
on
the
report.
The
reason
for
that
is
automatically
the
top
grouping
variable
is
a
sum
of
all
of
the
rows
underneath
it.
This
is
not
what
we
want
for
this
report
because
these
categories
are
not
mutually
exclusive.
A
subject
could
have
both
a
vasoconstriction
event
and
a
hypertension
event.
But
we
don't
want
to
count
them
twice
under
vascular
disorders.
We
only
want
to
count
them
once.
In
order
to
do
that,
we're
going
to
grab
the
unique
subject
identifier
and
drag
it
into
the
ID.
Now
you'll
see
those
numbers
reduced
quite
a
bit.
Now
they
represent
a
unique
subject
count
for
that
type
of
event.
One
other
thing
to
point
out
on
this
report
is
that
we
do
on
this
data
table
have
a
reference
by
unique
subject
identifier
to
TADSL.
What
that
means
is
that
the
table
is
being
virtually
linked
to
this
TADSL
table,
which
allows
us
to
filter
on
demographic
variables
that
are
found
in
the
ADSL
table.
Now
I'm
going
to
hand
it
over
to
Sam
so
he
can
talk
about
the
risk
reports
and
how
we
use
virtual
joins
to
filter
those
tables.
Thanks,
Rebecca.
That
was
a
really
nice
description
of
that
report
and
how
it
was
put
together.
I'm
going
to
show
you
another
report
that
Rebecca
is
responsible
for
developing.
This
one
is
our
adverse
event
risk
report.
What
this
report
will
do
is
it
will
go
through
all
of
the
reported
adverse
events,
and
it
will
count
the
number
of
events
by
treatment
group
and
calculate
a
percent
or
a
rate.
It
displays
on
the
graph
two
things.
It
displays
two
different
graphs.
On
the
left-hand
side,
it
shows
the
rates
for
each
treatment
group.
You
can
see
that
the
color
key
there
shows
which
treatment
group
is
being
displayed
there.
For
pruritus,
we've
got
a
rate
of
pruritus
in
the
placebo
group
of
9.3%.
We
have
a
rate
of
pruritus
in
the
low-dose
group
of
27.381%,
and
in
the
high-dose
group
of
30.92%.
Usually,
what
we're
interested
in
in
this
case
is
the
difference
in
the
rates
compared
to
the
placebo
group.
What's
shown
on
the
right-hand
side
of
the
graph
is
the
calculation
of
the
differences
for
each
treatment
group
and
the
placebo
group.
There
are
three
treatment
groups,
so
there
are
two
computed
differences:
the
high
dose
and
the
low
dose
compared
to
placebo.
We
also
display
a
confidence
interval
around
those
points.
We
make
this
report
filterable.
One
thing
we
can
do
is,
let's
say
we
only
want
to
look
at
adverse
events
that
occur
at
a
certain
rate,
maybe
higher
than
5%
of
the
time,
and
we
can
narrow
that
down
quite
a
bit
and
reduce
it.
We
also
have
a
corresponding
tabulate
table
that's
very
similar
to
the
tabulate
that
Rebecca
showed
earlier.
What
I
want
to
do
is
show
you
how
to
recreate
this
Graph
Builder
graph
because
there's
lots
of
interesting
things
you
can
do
in
Graph
Builder
that
you
might
not
be
aware
of.
The
first
thing
I'm
going
to
do
is
I'm
going
to
open
up
the
data
tables
that
we
use,
and
we
actually
use
three
different
data
tables.
Let
me
clear
this
report
filter
selection
first.
There
we
go.
Now
I'm
going
to
open
up
the
data
tables
that
we
use.
The
first
one
is
the
data
that's u
sed
to
actually
make
the
plot.
We
call
that
the
risk
plot
table.
The
second
table
is
the
term
ID
table,
and
it's
just
the
list
of
unique
terms
that
we
want
to
display
in
the
graph
on
the
Y-axis.
Then
the
last
table
is
a
filter
table.
It's
what
we
use
to
actually
filter
the
report.
I've
got
all
those
three
open.
Let's
start
out
by
making
the
graph
of
the
risks
and
the
risk
differences.
I've
got
a
Graph
Builder.
We're
going
to
take
the
dictionary-derived
term,
drag
that
onto
the
Y-axis.
Make
this
bigger.
Then
we're
going
to
take
the
calculation,
in
this
case,
the
variable
we
just
labeled
percent,
and
we're
going
to
put
that
onto
the
X-axis.
Then
what
we
want
to
do
is
we
want
to
overlay
by
the
active
treatment.
Just
want
to
put
that
on
there.
The
next
thing
we
want
to
do
is
we
want
to
introduce
a
little
bit
of
customization
of
the
way
the
points
are
presented.
You
can
see
the
points,
there's
a
bar
here
where
you
can
control
the
points
and
the
way
they're
drawn.
One
thing
is
we
want
to
make
sure
that
they
have
some
jitter
in
them.
In
this
case,
the
jitter
is
set
to
auto,
but
we
can
set
that
type
of
jitter
that
we
want.
I'm
going
to
change
it
to
center
grid.
Another
thing
that
we
want
to
do
is
we
want
to
change
the
way
the
points
are
drawn.
Under
the
Graph
menu
that
you
can
access
through
right-clicking
in
the
graph,
I'm
going
to
right-click,
Select
Graph,
and
go
to
Marker
Drawing
mode,
and
I'm
going
to
choose
outlined.
That
changes
those
points
to
have
an
outline,
a
more
sharp
outline,
and
it
really
makes
them
stand
out
much
more
significantly.
That's
this
plot
of
the
percent
or
the
rates
of
each
of
those
adverse
events.
One
thing
I'm
also
going
to
do
is
I'm
just
going
to
add
a
grid
line
o
n
the
Y-axis,
which
you
can
just
look
across
then
so
you
can
see
where
the
individual
points
line
up
and
match
that
to
the
grouping
level
for
the
dictionary-d
erived
term.
That's
for
the
rates.
The
next
thing
I
want
to
do
is
I
want
to
look
at
the
differences.
We
have
a
risk
difference
variable
and
I'm
going
to
drag
that
and
I'm
going
to
drag
that
down
onto
the
X-axis
but
to
the
right.
What
it
actually
does
is
it
creates
another
graph.
Now
I
have
a
graph
of
the
risk
differences.
I
also
want
to
put
an
interval
around
those.
What
we
do
is
we
have
two
variables
that
have
the
upper
and
lower
limit
for
the
intervals.
I'm
going
to
select
those
and
drag
them
to
the
interval
zone.
You
notice
what
happens
when
I
do
that.
It
actually
draws
intervals
around
all
the
points
on
all
the
graphs
that
I've
added
in
here.
I
don't
want
that
on
the
points
for
the
percent
variable.
For
the
interval
style
or
the
error
interval,
I'm
going
to
set
that
to
be
none.
It
turns
those
off.
Essentially
what
we've
done
is
we've
recreated
the
graph.
This
is
a
little
busy
because
there's
lots
of
categories
here.
We
could
just
filter
this
table.
We
could
go
here
and
we
could
select
the
local
data
filter.
Let's
say
we
wanted
to
just
filter
on
risk
difference.
We
could
say,
let's
only
look
when
the
risk
difference
may be
greater
than
3%
in
absolute
value.
What
I'm
going
to
do
is
I'm
going
to
set
a
limit
that
it's
between
three
and
three
and
then
invert
that.
Then
that
reduces
the
number.
Maybe
I
want
that
to
be
a
little
bit
more
restrictive,
so
make
that
5, -
5
.
There
we
go.
That
reduces
the
number
that
are
selected.
But
the
thing
is,
it's kind of
weird.
It
only
filtered
the
values
for
the
risk
difference
and
it
didn't
filter
the
values
for
the
percent.
It
didn't
filter
everything
the
way
we
wanted
it
to.
We
needed
to
do
something
a
little
bit
different.
I'm
going
to
clear
that.
What
I'm
going
to
do
is
I'm
going
to
link
this
table,
the
left-hand
table,
which
is
used
to
make
the
graph,
to
the
table
of
all
of
the
unique
levels
that
are
displayed
on
the
Y-axis,
all
of
the
dictionary-derived terms.
The
way
we
do
that
first
is
for
the
dictionary-derived term,
we
have
to
make
sure
that
the
link
ID
property
is
set.
What
that
tells
JMP
is
that
if
I
link
to
this
table,
this
is
the
variable
that
I'm
going
to
use
for
matching
to
the
table
that
I'm
linking
it
to.
I
set
that
to
be
the
link
ID.
Then
over
here
for
the
dictionary-derived
term,
what
I
need
to
do
is
I
need
to
choose
the
link
reference,
and
I'm
going
to
choose
the
actual
table,
this
actual
table
as
the
link
reference.
Now
what's
happened
is
these
two
tables
are
linked.
The
last
thing
I
need
to
do
to
specify
this
virtual
linking
properly
is
to
right-click,
select
Column
Info,
and
under
the
link
reference
property,
choose
the
row
states
that
I
want
to
broadcast
from
the
middle
table
to
this
linked
table.
I
want
to
accept
the
row
states
from
the
reference
table.
I'm
going
to
choose
the
selected,
excluded,
and
hidden
row
states
and
accept
those
from
this
center
table.
Then
when
I
do
that,
when
I
select
rows
in
this
data
table,
I
end
up
selecting
rows
in
the
corresponding
link
table.
If
I
hide
and
exclude
those
rows,
they
become
hidden
and
excluded
in
this
plot
because
I've
hidden
and
excluded
several
of
the
rows
in
this
linked
table.
That's
one
level
of
linking.
But
what
we
really
want
to
do
is
we
want
to
filter
this
graph
based
on
corresponding
measures
or
metrics
that
are
calculated
in
this
table.
These
are
based
on
the
differences
and
the
risks
and
other
difference
measures
between
the
placebo
group
and
the
treatment
group.
What
I'm
needing
to
do
to
make
that
happen
is
I
need
to
link
this
right-hand
table
to
the
center
table.
I'm
going
to
do
that
as
well.
I'm
going
to
go
here
to
the
dictionary-d
erived
term
for
the
right-hand
table,
and
I'm
going
to
choose
the
link
reference
and
pick
that
table
that
I
want
to
link
to,
which
is
this
one
displayed
here.
Open
up
the
column
properties
here,
go
to
the
link
reference
property.
Now
what
I
want
to
do
is
I
want
to
dispatch
row
states,
the
selected,
excluded,
and
hidden
row
states,
from
this
table
to
the
linked
table.
What
I've
done
now
is
I've
linked
two
tables
together.
I
have
the
table
on
the
left
is
linked
to
the
center
table,
and
there's
a
many-to-one
relationship.
There's
actually
a
three-to-one
relationship.
For
every
row
in
the
center
table,
there
are
three
rows
in
the
left-hand
table.
I've
also
linked
the
table
on
the
left,
which
has
a
two-to-one
relationship
where
every
row
in
the
center
table,
there
are
two
rows
in
the
right-hand
table.
What
I
want
to
do
is
I
want
to
push
row
states
from
the
right-hand
table
to
the
far
left-hand
table,
and
having
this
linking
between
the
two
and
pushing
row
states
from
the
right-hand
table
to
the
center
table,
and
then
those
row
states
being
accepted
by
the
left-hand
table
allows
me
to
do
that.
Now
what
I
can
do
is
I
can
filter
on
this
table,
and
I'll
just
open
up
the
data
filter,
and
I'm
going
to
choose
the
absolute
risk
difference.
I'm
going
to
say
if
the
absolute
risk
difference
is
large,
that those
are
the
points
that
I
want
to
display.
Now
that
I
have
everything
linked
up,
I
can
use
the
table
that's
on
the
left-hand
side
to
filter
the
graph,
which
is
based
on
the
data
on
the
right-hand
side.
If
I
go
and
open
up
a
local
data
filter,
I'm
going
to
turn
on
show
and
include
as
the
options.
I'm
going
to
choose
absolute
risk
difference
because
that's
a
good
filtering
criteria
just
to
find
things
that
have
a
large
absolute
risk
difference.
Then
now
notice
it's
filtering
the
table.
I'm
filtering
the
left-hand
table,
pushing
it
through
to
the
middle
table,
which
is
pushing
those
row
states
through
to
the
right-hand
table.
Some
clever
virtual
joins.
One
thing
we
do
in
addition
to
JMP
Clinical
is
we
use
a
callback
function
when
we
use
our
filters.
Actually,
there's
a
way
with
JMP
scripting
you
can
write
a
little
function
that
will
run
every
time
you
change
a
data
filter
and
it
can
do
some
additional
things
to
customize
the
graph
just
a
little
bit
more.
If
you
see
it
in
our
product,
it
does
it
a
little
bit
differently,
but
it
looks
pretty
much
the
same
as
what
I'm
doing
here.
Now,
Rebecca
can
show
you
something.
One
of
our
other
most
commonly
used
reports
is
our
findings
time
trends.
When
you
launch
findings
time
trends,
you'll
get
two
graphs,
but
I'm
just
going
to
focus
on
the
first
graph.
The
first
graph
shows
the
mean
change
from
baseline
for
a
given
lab
test
over
the
course
of
the
study,
shown
by
visits.
The
bottom
graph
shows
the
number
of
records
at
each
visit
for
that
lab
test.
If
we
want
to
recreate
this
table,
we
can
pop
out
our
data
table.
Again,
we'll
go
to
Graph
and
Graph
Builder.
We're
going
to
use
Billy
Rubin
as
an
example.
We'll
grab
the
Billy
Rubin
record
and
put
it
on
our
Y-axis.
We
want
our
graph
plotted
by
visit.
We'll
grab
visit
and
put
it
on
the
X-axis.
By
default,
we
end
up
with
points,
but
we
want
to
switch
it
over
to
a
line
graph.
Once
we
have
our
line
graph,
we
also
want
to
split
it
out
by
treatment
group.
Treatment
group
is
not
actually
a
variable
in
our
base
data
set,
but
it
is
a
variable
in
the
linked
data
set
to
TADSL.
We
can
go
into
our
reference
data
set
and
actually
grab
that
plan
treatment
value
from
the
virtually
joined
one
and
drag
it
over
to
our
overlay.
That
splits
out
our
lines
into
two
different
treatment
groups.
We
also
want
to
add
intervals.
Under
error
interval,
instead
of
using
auto,
we'll
change
that
to
confidence
interval.
Now
we
have
a
confidence
interval
for
each
of
our
visits
for
each
of
our
treatment
groups.
We
also
want
to
add
the
bar
graph.
We're
going
to
grab
Billy
Rubin
again
and
this
time
drag
it
over
into
the
bottom c
orner
of
the
Y-axis
so
that
we
now
have
two
separate
panels.
By
default,
we
end
up
with
just
a
duplicate
of
the
panel
we
already
created.
We're
going
to
change
this
by
right-
clicking,
going
to
the
line
and
change
to
bar.
Now
we
have
a
bar
graph.
We
can
go
ahead
and
remove
our
confidence
intervals
from
this
one
by
selecting
none
under
error
interval.
We
get
pretty
close
to
our
final
results
with
just
these
two
panels.
But
we
also
want
to
add
points
onto
our
top
panel.
By
default,
we
get
a
point
for
every
single
row
in
the
data
table.
We're
going
to
just
go
over
here
and
change
our
summary
statistic
to
mean.
Now
we're
just
going
to
clean
up
a
little
bit.
We
can
change
the
labels
by
clicking
on
the
label
and
then
typing
in
a
new
one,
so number
of
records.
On
the
top,
we'll
do
the
same
thing
and
change
it
to
mean
change
from
baseline.
We
can
also
clean
up
our
legend
a
little
bit.
You'll
see,
because
we
have
two
panels
it
actually
show
the
treatment
group
twice,
we
don't
really
want
to
see
that.
We
can
go
to
the
red
triangle,
go
to
Legend
Settings,
and
we'll
just
unclick
a
couple
of
these
so
that
we
only
see
the
treatment
groups
once.
We're
back
to
our
panel
that
we
created
in
the
report
and
we
can
add
our
column
switcher.
If
you
go
to
the
red
triangle
and
go
to
Redo
Column
Switcher,
we
can
change
out
the
Billy
Rubin
record.
We
want
to
change
the
lab
test
that's
being
shown
on
the
Y-axis.
This
dataset
has
a
bunch
of
different
lab
tests
from
alkaline
phosphatase
all
the
way
down
to
partial
pressure
oxygen.
We're
just
going
to
select
all
those
lab
tests
and
click
OK.
Now
we
have
a
column
switcher
and
you
can
actually
select
each
lab
test
and
you'll
see
that
the
graph
changes
to
show
you
the
lab
test
that's
selected.
Now
I'm
going
to
hand
it
back
to
Sam
so
that
he
can
show
some
more
complicated
graphing
using
patient
profiles.
Thanks,
Rebecca.
Just
to
wrap
up
here,
I
want
to
show
you
a
more
advanced
collection
of
graphs
that
we
use
called
our
patient
profile.
This
is
a
way
just
to
visualize
everything
that
happens
to
a
subject
in
a
clinical
trial
that's
been
recorded
in
the
data.
We
collect
things
like
when
did
they
do
their
study
visits
and
what
day
did
that
happen
and
what
study
day?
What
were
the
disposition
events
that
they
had,
the
exposure
they
had?
Did
they
have
adverse
events
and
what
was
the
duration
of
the
adverse
events?
When
did
they
take
medications
during
the
trial?
Vital
signs
that
were
recorded
or
test
results
that
were
recorded
can
also
be
displayed.
We
have
a
lot
of
ability
to
customize
this
and
configure
it.
This
is
all
scripted,
so
I
can't
really
show
you,
"How
would
I
build
all
this
just
with
the
graphical
user
interface
and
JMP?"
You
need
to
know
a
little
bit
of
JMP
scripting.
But
in
reality,
each
one
of
these
graphs
is
just
a
graph
builder
that
have
been
put
together
on
top
of
each
other
and
then
a
little
bit
of
editing
of
the
report
layer
to
remove
some
of
the
controls.
That's
really
what's
happened.
We've
also
added
some
custom,
like
the
legend
here
is
a
custom
graphic
that
we
add
in
to
display,
and
it's
just
an
image
that
displays
in
the
report
window.
But
really,
all
of
this
is
just
Graph
Builder
for
the
most
part.
We
can
switch
between
patients
here
and
look
at
the
profile
of
each
different
patient
in
the
trial
as
we
would
want
to.
I
hope
you've
been
able
to
see
during
this
presentation
just
some
of
the
ways
you
can
use
Graph
Builder
and
Tabulate
to
summarize
your
data.
More
than
just
the
standard,
I
did
a
few
clicks
and
this
is
the
result
I
get.
With
a
little
bit
of
extra
knowledge
of
how
to
customize
the
output
for
the
graphs
and
the
tables
that
you
have
displayed,
you
can
actually
get
some
very
nice-looking
output
that
you
can
use
for
presentation
within
JMP
and
even
for
reporting.
Some
of
this
that
we've
used
has
been
driven
by
regulatory
guidelines
that
say
they
would
like
to
see
tables
and
graphs
look
this
way.
We've
been
able
to
achieve
that
using
some
of
these
features
like
stacking
and
packing
columns,
and
tabulate,
having
multipanel
displays,
doing
the
filtering
in
a
unique
way,
and
using
column-
switching,
to
make
it
not
just
a
reporting
tool,
but
an
interactive
tool
as
well.
Thanks
for
your
attention,
and
we
are
happy
to
hear
any
feedback
you've
got
or
questions
about
the
product
or
about
what
we've
shown
you
in
this
presentation.
