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.