Hi.
Thank
you
for
joining
me
today.
My
name
is
Rebecca
Lyzinski.
I'm
a
senior
software
developer
for
JMP
Statistical
discovery.
Today
I'll
be
talking
about
how
JMP
Clinical
uses
some
of
the
new
JMP
17
features,
such
as
Pack,
Stack,
and
Response Screening.
First
I'll
talk
a
little
bit
about
what
JMP
Clinical
is.
Then
I'll
go
into
what
changes
have
occurred
in
JMP
Clinical
17
compared
to
previous
versions,
and
then
show
a
demo
of
JMP
Clinical
and
how
it
uses
the
new
tabulate
features
of
Stack,
Pack
and
unique
IDs,
as
well
as
the
new
Response Screening
platform.
First,
what
is
JMP C
linical?
JMP
Clinical
is
a
JMP
product
that
is
used
to
analyze
clinical
trial
data.
It
works
by
using
the
standard
formats
of
CDISC,
SDTM
and
Atom
data.
Once
the
data
is
loaded,
JMP
Clinical
runs
interactive
reports
for
events,
findings,
interventions
and
more.
JMP
Clinical
is
used
by
a
variety
of
fields,
including
medical
doctors,
medical
writers,
clinical
operations,
and
statisticians.
In
addition,
JMP
Clinical
works
with
JMP
Live
to
share
reports
across
your
organization.
With
JMP
Clinical
17,
there's
a
big
change,
in
that
JMP
Clinical
no
longer
uses
SAS
as
the
basis
for
the
code
underlying
the
reports.
Starting
with
JMP
Clinical
17,
it
is
now
completely
built
off
of
JMP.
This
means
that
we
have
a
faster
installation
because
the
installer
is
now
more
compact
than
it
was
before.
JMP
Clinical
17
also
has
all
of
its
reports
redesigned
using
JSL
as
the
underlying
code
system
for
the
reports.
Another
change
is
that
now
the
reports
will
auto
run.
There's
no
longer
a
need
to
click
a
button
in
order
to
get
the
report
to
run.
JMP
Clinical
17
will
also
include
some
new
reports,
including
the
FDA
Medical
Queries
and
the
Algorithmic
FDA
Medical
Queries?
One
additional
change
is
that
now
all
the
study
preferences
are
in
one
location.
You
only
have
to
go
to
one
place
to
change
a
preference,
and
it
will
take
effect
across
all
of
your
reports.
Now
I'm
going
to
switch
over
to
JMP
Clinical
for
a
quick
demo.
When
you
first
open
JMP
Clinical,
a
main
window
will
appear
with
three
different
tabs
one
for
Studies,
one
for
Reviews,
and
one
for
Settings.
The
Studies
tab
is
where
all
your
study
data
is
located.
Here
you'll
see
that
I
have
the
study,
the
Nicardipine
loaded.
You'll
see
paths
for
the
SDTM
and
Atom
locations
of
your
data,
as
well
as
which
domains
from
those
folders
have
been
loaded
for
the
study.
This
is
also
where
you
can
add
a
new
study.
You
can
refresh
the
study
metadata
for
an
existing
study.
If
you
add
data
to
it,
or
you
add
variables,
or
you
change
variable
names,
you
can
refresh
the
metadata
and
all
those
changes
will
take
effect.
You
can
also
set
study
preferences
or
set
the
value
order
in
color
for
a
given
study f
rom
this
tab.
Set
study
preferences
is
new
in
JMP
Clinical
17.
It
will
open
a
new
dialog.
Here
you
can
change
any
of
these
widgets
and
the
new
values
will
take
effect
across
all
of
your
reports.
F
or
example,
if
you
didn't
want
your
reports
to
run
off
the
safety
population
and
you
wanted
them
to
run
on
all
subjects
instead,
you
can
change
to
all
subjects
here.
Once
you
click
Okay,
all
your
reports
will
now
run
off
of
all
subjects
instead
of
the
safety
population.
The
next
tab
is
for
Reviews.
Here,
when
you
click
Start
New
Review,
the
Review
Builder
will
open
and
you'll
be
able
to
select
which
reports
you
want
to
see.
For
this
example,
I'm
going
to
look
at
the
demographics,
distribution
AE D
istribution,
AE
Risk
Report
and
the
two
FDA
medical
query
reports.
If
you
wanted
to
add
additional
reports,
you
can
click
on
Add
Report.
A
new
window
will
open
up
with
all
the
possible
reports
you
can
run
on
this
study,
and
you
can
make
additional
selections.
Demographics
distribution
is
usually
a
good
place
to
start
in
any
clinical
trial.
Here
there
are
tables
and
distributions
for
each
demographic
characteristics
such
as
sex,
race
and
age.
Tabulate
is
used
to
create
the
tables
at
the
top,
and
you
can
see
here
that
the
Counts and Percents
are
combined
into
one
column
using
Tabulate's
new
feature
of
Packed
Columns.
Underneath
is
a
distribution
for
each
of
the
demographic
characteristics.
On
the
side,
there's
an
option
to
add
additional
distributions
if
there
are
other
characteristics
you
would
like
to
see.
By
clicking
the
Add
button,
you
can
add
any
variable
from
either
the
ADSL
or
DM
data
set,
and
it
will
show
up
under
Distributions.
There's
also
an
option
to
perform
treatment
comparison
analyses.
When
this
button
is
clicked,
the
report
will
automatically
rerun.
Now
at
the
bottom
of
the
report,
there's
a
one
way
analysis
for
age
and
a
contingency
analysis
for
sex
and
race.
This
allows
for
comparisons
between
treatment
groups
to
be
done
to
see
if
there
are
any
differences
between
the
treatment
groups.
Typically,
an
important
safety
analysis
that
occurs
in
any
clinical
trial
is
to
analyze
the
adverse
events
that
occur
throughout
the
trial.
In
Adverse
events
distribution,
there's
a
graph
and
a
table
showing
the
distribution
of
adverse
events
across
treatment
groups.
At
the
top
is
a
bar
chart
with
the
count
of
adverse
events
split
out
by
NiNicardipine
and
Placebo,
the
two
different
treatment
groups
for
the
NiNicardipine
study,
they're
shown
in
descending
order
for
each
treatment
group.
Under
the
graph
is
a
tabulate.
Here,
you'll
see
that
the
first
column
is
body
system
organ,
class
and
dictionary
drive
term.
These
are
two
different
measure
terms
that
are
used
to
classify
adverse
events,
and
they're
being
stacked
on
top
of
each
other
in
the
tabulate.
In the
other
columns
are
Counts and Percent
split
out
by
the
planned
treatment
group,
as
well
as
a
total
count
and
Percent.
This
table
uses
a
lot
of
the
new
JMP
17
features
for
Tabulate.
The
first
one
is
the
Stack
Grouping
Columns.
Here
you
can
see
if
you
right-
click
on
the
Column,
the
Stack
Grouping
Columns
option
is
checked.
If
we
were
to
uncheck
it,
it
gets
split
back
out
into
two
separate
columns.
This
is
how
Tabulate
works
for
JMP
Clinical
8.1
in
previous
versions
where
we
had
to
have
two
separate
columns
for
the
two
different
variables.
Now,
by
selecting
both
columns
and
right
clicking
and
going
to
Stack
Grouping
Columns,
we
can
combine
them
back
into
one
column.
This
allows
the
table
to
now
be
publication
ready
for
any
PowerPoint
or
journal
article
that
it
might
want
to
be
used
in.
Somewhat
similarly,
we
have
the
Count
and
Percent
in
one
column
which
did
not
exist
before.
If
you
right-
click
on
one
of
these
columns,
you'll
see
the
new
Pack
Columns
option.
If
we
unpack
the
columns,
they're
now
separate
into
two
columns,
one
for
the
Count
and
one
for
the
Percent.
By
selecting
both
columns
and
right-
clicking
and
going
to
Pack
Columns,
we
can
now
pack
them
back
into
one
column
so
that
the
Count
and
Percent
show
up
together.
The
other
option
that
this
table
uses
is
if
you
open
up
the
control
panel
from
the
red
triangle,
you'll
see
that there's an
ID
variable
that's
been
added
that
didn't
exist
before.
Here
you'll
see
that
unique
subject
Identifier
has
been
entered
as
the
ID
variable
to
use
in
this
table.
What
that
option
does
is
it
counts
each
subject
only
once
on
each
row
of
the
table.
For
example,
if
the
subject
had
both
a
vasoconstriction
event
and
a
hypertension
event,
they
would
only
get
counted
once
with
in
vascular
disorders.
Previously,
before
the
ID
variable
existed,
this
Vascular
disorders
row
would
have
been
a
sum
of
all
of
the
events
that
happened
underneath
it,
which
may
overestimate
the
number
of
subjects
that
had
a
vascular
disorder
event.
You
can
also
see
at
the
bottom
of
the
table
that
this
option
now
adds
a
row
called
all.
What
this
represents
is
the
number
of
subjects
with
any
adverse
event.
That's
another
nice
additional
feature
added
through
the
ID
variable.
With
these
three
changes,
we
now
have
a
very
nice
publication
ready
table
to
print
out
to
whatever
word
document
PowerPoint
you
want
to
include
it
in.
A
couple
of
other
features
to
mention
on
this
report
before
moving
on
to
the
next
one
is
that
there
are
some
options
listed
under
Data.
For
example,
if
you
wanted
to
look
at
a
different
measure
term
than
the
ones
that
are
automatically
presented,
you
can
change
them
here
to
report
a
term,
Highlevel
Term,
etc
.
You
can
also
change
the
report
to
run
on
pretreatment
events,
treatment
events,
on-
treatment
or
off-
treatment
events.
The
Demographic
Grouping
Widget
will
change
out
the
variable
on
the
y
axis
of
the
graph
builder,
as
well
as
change
the
variable
used
in
the
Tabulate
to
whichever
variable
is
selected
from
demographic
grouping.
There's
also
an
option
to
Stack
both
the
table
and
the
graph.
For
example,
if
you
wanted
to
see
the
adverse
events
split
out
by
severity,
we
can
select
severity.
Now
the
bar
chart
is
stacked
by
mild,
moderate
and
severe
events.
The
table
is
also
split
out
into
columns
for
mild,
moderate
and
severe.
The
report
also
uses
a
local
data
filter
in
order
to
filter
both
bar
chart
and
the
tabulate.
You
can
filter
on
things
such
as
whether
or
not
the
event
is
serious,
whether
or
not
the
event
is
related
to
the
study
treatment.
We
can
also
filter
on
a correct overall
percent
occurrence
of
the
adverse
events.
For
example,
if
we
only
wanted
to
see
adverse
events
that
occur
in
5%
or
more
of
the
population,
we
can
change
this
filter.
Now
the
bar
chart
and
the
table
are
both
filtered
down
to
only
subjects,
only
adverse
events
that
have
at
least
a
5%
occurrence
in
the
population.
Another
way
to
analyze
adverse
events
is
through
the
Risk
Report.
This
risk
report
uses
the
new
JMP
17
Response Screening
Platform
to
create
both
a
Risk
Plat
and
a
Tabulate.
The
Risk
Plat
shows
the
percent
occurrence
of
subjects
within
both
treatment
groups,
so
Placebo
and
a
Nicardipine,
and
it
also
shows
the
risk
difference
in
comparing
the
Nicardipine to
Placebo
along
with
a
95%
confidence
interval.
The
table
repeats
this
information
just
in
tabular
form
with
columns
for
the
Counts and Percent
in
each
treatment
group,
as
well
as
a
column
for
the
risk
difference
in
the
95%
confidence
interval.
The
Response Screening
platform
works
off
a
table
that
looks
like
this
one,
where
we
have
unique
subject
identifier
as
the
first
column,
and
then
there's
a
column
for
each
adverse
event.
That's
an
indicator
column
with
zero
representing
no
event
and
one
representing
an
event.
If
we
pop
out
this
table.
The
Response Screening
platform
is
located
under
Analyze
Screening.
Response Screening.
It
will
open
up
a
new
dialog
where
you
can
select
your
variables
that
you
want
to
compare.
Because
there
are
202
different
adverse
event columns
,
we've
combined them into
a
group
of
columns
and
this
allows
you
to
just
select
one
variable
and
it
will
automatically
put
all
202
columns
into
the
Y
Response
Column
using
Plan
Treatment
for
our
X
and
click
Okay.
Response Screening
then
brings
up
this
window.
The
default
view
is
to
look
at
the
FDRP
values
and
a
table
of
those
values.
JMP
Clinical
uses
the
two-
by-
M
results
table.
This
is
where
Response Screening
calculates
the
relative
risk,
risk
difference
and
odds
ratio.
JMP
Clinical
works
by
creating
making
this
table
into
a
data
table
and
then
using
Graph
Builder
and
Tabulate
to
format
it
in
the
view
that
was
shown
in
the
report.
In
order
to
get
the
additional
columns
needed,
if
you
right-click
on
the
table
and
go
to
Columns,
you
can
select
the
different
95%
confidence
interval
variables
as
well
as
a
total
count
and
the
different
counts
for
the
positive
versus
negative
comparisons.
Once
that
Response Screening
is
run,
then
it's
created
into
a
data
table
in
this
bar
chart
and
the
tabulate
are
created.
The
tabulate
again
uses
the
Pack
columns
option
to
put
Counts and Percent
into
one
column,
but
it
also
uses
it
to
put
the
risk
difference
in
95%
confidence
interval
into
one
column.
If
we
were
to
unpack
this
group
of
columns,
you
would
see
that
it
originally
started
as
three
different
columns.
Even
with
three
different
columns,
we
can
still
pack
them
together
into
one
column.
If
you
didn't
like
the
format
of
the
way
that
they
automatically
packed
together,
you
can
right-
click
on
the
column,
go
to
Pack
Columns
and
Template.
Here
you
can
change
the
format
of
how
the
column
appears.
For
example,
if
you
wanted
to
see
brackets
instead
of
parentheses,
you
could
change
them
here.
You
could
also
change
how
the
columns
are
delimited.
The
default
is
a
comma,
but
you
could
use
a
semicolon
or
any
other
character
that
you
wanted
to
separate
out
your
columns.
Similar
to
the
AE
Distribution
Report,
this
report
has
a
few
different
options.
Some
that
are
different
are
that
you
can
change
the
risk
measurement,
so
you
can
look
at
either
risk
difference,
relative
risk,
or
odds
ratio.
You
can
also
display
the
risk
difference
as
either
a
percent
or
a
proportion,
and
you
can
sort
the
plot
in
the
tables
by
risk
measurement
count
or
alphabetically.
This
report
again
uses
a
local
data
filter
to
filter
both
the
plot
and
the
table
by
either
a
dictionary
drive
term,
the
risk
difference,
or
the
absolute
risk
difference.
Here
you
can
see
that
I
filtered
the
risk
difference
down
to
two
or
greater
so
that
we
can
see
the
Plot and Table
a
little
more
clearly.
Another
view
of
the
Risk
Plot
and
the
Response Screening
output
is
the
FDA
Medical
Query
Risk
Report.
This
starts
out
as
just
being
called
Medical
Query
Risk
Report,
and
then
there's
an
option
to
analyze
it
either
by
FDA
Medical
Queries
or
standardized
medical queries.
Medical
Queries
are
a
way
to
group
adverse
events
into
different
medical
conditions,
and
these
are
the
two
different
standards.
Standardized
Medical
Queries
are
created
by
MedDRA
and
usually
come
as
SD
files.
In
September
of
2022,
the
FDA
released
their
own
Medical
Queries
as
an
Excel
file
that
can
be
downloaded
from
the
web.
JMP
Clinical
handles
both
of
these
different
standards
and
can
be
switched
on
this
report
back
and
forth
by
selecting
either
FDA
Medical
Queries
or
Standardized
Medical
Queries.
Just
like
on
the
AE
Risk
Report,
there
is
a
risk
plot
with
the
percent
occurrence
for
each
treatment
group
and
the
risk
difference
between
the
Nicardipine
and
Placebo.
The
difference
is
that
on
this
report,
the
Risk
Plot
is
split
out
by
scope.
Either
a
broad
medical
query
or
a
narrow
medical
query.
Underneath
some
custom
scripting
is
used
to
create
tables
that
stack
the
medical
queries
by
the
preferred
terms
that
contribute
to
them.
Just
like
in
the
AE
Risk
Report,
we
have
counts
for
columns
for
the
Counts and Percents,
as
well
as
a
column
for
the
risk
difference
between
Nicardipine
and
Placebo.
Here
you
can
see
that,
for
example,
for
Arrhythmia,
the
dictionary
derived
terms
that
contribute
to
that
medical
query
are
Atrial,
Flutter,
Atrial f
ibrillation,
Arrhythmia,
Bradycardia
and
a
few
others.
Underneath
that
table
is
the
same
table
just
for
broad
medical
queries
split
out
by
preferred
terms,
a
table
for
medical
queries
split
out
by
broad
or
narrow,
depending
on
the
scope,
and
a
table
for
which
medical
queries
are
contained
in
each
system
organ
class.
For
example,
gastrointestinal
disorders
is
made
up
of
abdominal
pain.
The
last
report
I'm
going
to
show
is
a
brand
new
one
in
JMP
Clinical
17.1
inversions
beyond
that.
Within
the
FDA
medical
query
Excel
file,
there
are
some
text
boxes
for
different
algorithms
in
a
few
different
medical
queries.
The
algorithms
include
criteria
that's
not
just
limited
to
adverse
events.
For
example,
in H
yperglycemia,
a
subject
could
be
categorized
as
having
Hyperglycemia
if
they
have
an
adverse
event
that
falls
into
the
Hypergysemia
FMQ
category.
But
they
also
could
be
classified
as
having
Hyperglycemia
if
within
the
lab
data
set,
they
have
more
than
two
plasma
glucose
values
over
180
milligrams
per
deciliter.
This
report
uses
the
adverse
event
data
set,
the
lab
data
set,
and
the
continent
medications
data
set
to
determine
if
subjects
have
a
given
medical
query,
rather
than
just
looking
at
the
adverse
events
and
mapping
them
to
a
medical
query.
Similar
to
the
other
risk
reports,
this
report
uses
a
local
data
filter
to
allow
you
to
filter
on
the
medical
queries
the
risk
difference
and
the
absolute
risk
difference.
Again,
we
have
the
same
options
to
switch
between
event
type,
the
risk
measurement
for
risk
difference,
relative
risk,
or
odds
ratio,
and
for
sorting
the
table
by
risk
measurement,
count
or
alphabetically.
That
was
a
quick
overview
of
some
of
the
JMP
Clinical
features
and
how
JMP
Clinical
uses
the
new
JMP
17
features
in
Tabulate
and
Response Screening
to
make
our
reports.
However,
JMP
Clinical
is
a
much
bigger
product
than
just
those
five
reports.
We
actually
have
over
30
interactive
reports.
Some
commonly
used
ones
that
I
didn't
mention
are
the
Adverse
Event
Narratives,
The
Patient
Profiles,
A
Study
Flow
Diagram,
like
the
figure
below,
that
shows
you
how
subjects
progress
throughout
the
study
and
the
ability
to
analyze
by
high's
law
cases.
JMP
Clinical
also
works
with
JMP
Live.
At
the
top
of
each
report
there's
a
button
that
if
you
click
it,
it
will
publish
and
share
the
report
across
your
organization.
There
are
also
future
features
coming
in
17.1
and
future
versions,
such
as
adding
the
ability
for
crossover
support,
for
analyzing
crossover
studies.
There'll
be
even
more
reports
being
added,
such
as
a
couple
of
oncology
reports.
Thank
you
so
much
for
your
time.
I
would
appreciate
any
comments
or
feedback
if
you
want
to
leave
them
or
email
me
directly.
Again,
thank
you
for
your
time
and
hope
you
have
a
wonderful
day.
