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Pack, Stack, and Response Screening Hacks: How JMP® Clinical Uses New JMP® 17 Features (2023-EU-30MP-1285)

With version 17, JMP Clinical is now a fully JSL implemented product. This presentation will demonstrate the reimagined JMP Clinical and how it uses new JMP 17 features. Three new features in Tabulate format the tables produced by clinical reports to be publication-ready. Pack combines counts and percentages (or other statistics) into one column, while stack allows multiple grouping variables to be combined into one column. Tables displaying event counts also take advantage of the new Unique ID feature in Tabulate to count events only once per subject identifier. With these three new features, tables can be copied and pasted into any publication or report. JMP Clinical’s risk reports also use JMP’s new Response Screening platform to identify safety signals by calculating risk difference, relative risk, and odds ratio faster than previous versions. With all these new JMP features, JMP Clinical produces publication-ready reports quickly and effectively. 

 

 

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.