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Using Generalized Regression To Analyze Designed Experiments With Detection Limited Responses (2023-EU-30MP-1308)

Fangyi Luo, Group Scientist, Procter & Gamble
Christopher Gotwalt, Chief Data Scientist, JMP
Beatrice Blum, Senior Scientist, Procter & Gamble

 

Most measurement systems have detection limits above or below which one cannot accurately measure the quantity of interest. Although detection-limited responses are common in many application areas, such as the pharma, chemical, and consumer products industries, they are often ignored in the analysis. Ignoring detection limits biases in the results and even drastically lowers the power to detect active effects. Fortunately, the Custom Designer and Generalized Regression in JMP® make incorporating detection limits easy and automatic. In this presentation, we will use simulated versions of real designed experiments to show how to get the analysis right in JMP® Pro 17 and the pitfalls that will occur if detection limits are ignored in the analysis. We will also show how simple graphical tools can identify parts of the design region that could be problematic or even make it impossible to estimate certain model terms or interactions. Our examples will include an experiment designed to maximize the yield of a chemical product where the response is a reduction in the number of microorganisms in microbial susceptibility testing of consumer cleaning products.

 

 

Hi,  I'm  Chris  Gotwalt  with  JMP,  and  I'm  presenting  with  Fangyi  Luo  of  Procter  &  Gamble,  and  her  colleague,  Beatrice  Blum,  who'll  be  joining  us  for  the  in- person  presentation  at  the  Discovery  Conference  in  Spain.

Today,  we  are  talking  about  how  to  model  data  from  designed  experiments  when  the  response  is  detection  limited.  This  is  an  important  topic  because  on  the  one  hand,  detection  limits  are  very  common,  especially  in  industries  that  do  a  lot  of  chemistry,  like  the  pharmaceutical  and  consumer  products  industries.

While  on  the  other  hand,  the  consequences  of  ignoring  detection  limits  leads  to  seriously  inaccurate  conclusions  that  will  not  generalize.  This  leads  to  lost  R&D  time  and  inefficient  use  of  resources.  The  good  news  that  we  are  here  to  show  today  is  that  getting  the  analysis  right  is  trivially  easy  if  you  are  using  generalized  regression  in  JMP  Pro and  know  how  to  set  up  the  detection  limits  column  property.

In  this  talk,  we're  going  to  give  a  brief  introduction  to  sensor  data,  explaining  what  it  is,  what  it  looks  like  in  histograms  and  a  brief  description  of  how  you  analyze  it  a  little  bit  differently.  Then  Fangyi  is  going  to  go  into  the  analysis  of  some  designed  experiments  from  Procter  &  Gamble.  Then  I'm  going  to  go  through  an  analysis  of  a  larger  data  set  than  the  one  that  Fangyi  introduced.  Then  we're  going  to  wrap  up  with  a  summary  and  some  conclusions.

What  are  detection  limits?  Detection  limits  are  when  the  measurement  system  is  unable  to  measure,  at  least  reliably,  when  the  actual  value  is  above  or  below  a  particular  value.  If  the  actual  value  is,  say,  above  an  upper  detection  limit,  the  measured  value  will  be  observed  as  being  at  that  limit.  For  example,  if  a  speedometer  in  a  vehicle  only  goes  to  180  kilometres  an  hour,  but  you  are  driving  200 kilometres  an  hour,  then  the  speedometer  will  just  read  180  kilometres  an  hour.

In  the  graphs  above,  we  see  another  example.  We  see  five  histograms  of  the  same  data.  The  true  or  actual  values  are  over  here  on  the  left,  and  moving  to  the  right,  we  see  what  results  when  you  apply  an  increasing  detection  limit  to  this  data.

What  happens  is  we  see  this  characteristic  bunching  at  the  detection  limit.  When  you  see  this  pattern,  it's  a  really  good  sign  that  you  may  need  to  think  about  taking  detection  limits  into  account  in  your  distributional  or  regression  analysis.

Why  should  we  care  about  detection  limits  in  a  data  analysis?  Well,  if  you  don't  take  your  detection  limits  into  account  properly,  you'll  end  up  with  very  heavily  biased  results,  and  this  leads  to  very  poor  model  generalization.  The  regression  coefficients  will  be  way  off.  You'll  have  an  incorrect  response  surface,  which  leads  to  matched  targets  with  the  Profiler  being  way  off.

I  think  the  situation  is  a  little  bit  less  dire  when  maximizing  a  response,  but  there's  still  quite  a  lot  of  opportunity  for  things  to  go  wrong.  In  particular,  Sigma,  your  variance  estimate  will  still  be  way  off,  which  leads  to  much  lower  power,  you  have  completely  unreliable p- values.  The  tendency  is  that  variable  selection  methods  will  heavily  under  select  important  factors .  The  actual  impact  that  a  factor  has  on  your  response  will  be  dramatically  understated  if  you  don't  take  the  detection  limits  into  account.

The  two  tables  of  parameter  estimates  that  we  see  here  illustrate  this  very  nicely.  On  the  left  are  the  parameter  estimates  from  a  detection- limited  LogN ormal  analysis  of  a  regression  problem.  On  the  right, they  are  the  resulting  parameter  estimates  when  we  ignore  the  detection  limit.  We  see  that  the  model  on  the  left  is  a  lot  richer  and  that  a  lot of  our  main  effects,  interactions,  and  quadratic  terms  have  been  admitted  into  the  model.

Whereas  on  the  right,  when  we  ignore  the  detection  limit,  we're  only  able  to  get  one  main  effect  and  its  quadratic  term  included  in  the  model,  and  the  quadratic  term  is  heavily  overstated  with  a  value  of  negative  11.5  about  relative  to  the  value  in  the  proper  analysis  where  that  quadratic  term  is  equal  to  just  negative  3.

We  see  that  we're  really  missing  it  on  a  lot  of  the  other  parameters  here  as  well.  When  we  take  a  look  at  this  in  the  Profiler,  this  becomes  really  apparent.  O n  the  left,  we  have  the  Profiler  of  the  model  correctly  analyzing  with  the   Limit of Detection,  and  we  see  that  all  the  factors  are  there,  and  overall,  the  response  surface  is  pretty  rich- looking.

On  the  right,  we  see  that  only  the  one  factor,  dichloromethane,  has  been  included  in  the  model.  T he  solution  to  the  problem  that  you  would  get  with  the  problem  on  the  left  is  likely  rather  different  from  the  one  that  you  would  get  on  the  right.

Thanks,  Chris.  Now  I'm  going  to  share  with  you  a  little  bit  background  on  the  experiment  of  the  data  mentioned  by  Chris,  the  time  to  bacterial  detection.  The  objective  of  that  experiment  was  to  understand   hostility  impacts  of  our  formulation  ingredients  or  factors  on  a  liquid  consumer  cleaning  formulation.

The  experiment  was  a  micro- hostility  Design  of  Experiment  with  36  samples,  and  we  have  five  key  formulation  factors  A,  B,  C,  D,  E.  W e  have  two  responses  from  this  experiment.  They're  from  microbial  testing.  The  first  one  was  the  one  mentioned  by  Chris.  It  is  time  to  bacteria  detection  in  two  days,  and  it  was  measured  by  hour.  I f  we  are  not  able  to  detect  the  bacteria  in  two  days,  then  time  to  bacteria  detection  is  right  censored  at  48  hours.  So  the   Limit of Detection  for  this  endpoint  is  48  hours.

Another  endpoint  is  log  reduction  in  mode  from  Micro  Susceptibility  Testing.  For  this  endpoint,  what  we  did  is  that  we  add  certain  amount  of  mold  to  the  formulation,  wait  for  two  weeks,  and  measure  amount  of  mold  in  the  product  after  two  weeks.  T hen  we  calculate  the  reduction  in  log  base  time  mold,  and  this  is  the  second  endpoint.

Limit of Detection  for  this  endpoint  is   six  unit.  T his  shows  you  the  detailed  data  from  the  experiment,  the  first  15  samples.  Y ou  can  see  the  formulation  factors  A,  B,  C,  D,  E,  and  they  were  from  response  surface  design.  W e  have  two  endpoints,  the  bacteria  detection  time  in  hours  and  the  log  reduction  in  mold.  The  data  highlighted  in  red  are  right  censored  data.

We  can  use  histograms  and  scatterplots  to  visualize  our  data  as  well  as  factor  versus  censoring  relationship.  As  you  can  see  from  the  histogram,  more  than  50 %  of  samples  are  right  censored  at  48  hours.  If  an  observation  is  not  censored,  then  most  of  them  will  be  below  15  hours.

O n  the  right,  we  have  the  scatter plot.  The  red  circle  indicates  the  censored  data  points.  You  can  see  that  we  have  censoring  at  all  levels  of  the  factors   except  for  factor  C.  We  don't  have  the  censoring  at  higher  level  of  C,  but  we  observe  censoring  at  all  level  of  the  factors.

In  JMP  Pro  16  and  higher,  we  can  specify  column  properties  for  detection  limit.  W hen  you  go  to  column  property,  you  find  detection  limits,  and  then  you  can  specify  the  lower  detection  limit  and  upper  detection  limit. I f  a  data  point  is  below  the  lower  detection  limit,  that  means  it's  less  censored  at  the  lower  detection  limit.  If  a  data  point  is  higher  than  the  upper  detection  limit,  then  it  means  that  it's  right  censored  at  the  upper  detection  limit.

For  the  bacterial  detection  time,  we  have  an  upper  detection  limit  and  it's  48  hours.  W e  put  48  hours  in  the  upper  detection  limit  box.  After  we  specified  detection  limit  on  the  column  property  in  JMP,  then  we  can  use  JMP  generalized  regression  modeling  to  analyze  the  data  by  taking  into  account  the   Limit of Detection.  So  this  is  a  new  feature  in  JMP  Pro  16  and  higher.

For  this  type  of  analysis,  we  need  to  first  specify  the  distribution  for  a  response  and  estimation  method.  W e  try  different  distribution  for  the  data  and  use  the  forward  selection  method,  and  we  found  Normal  distribution  fits  the  data  the  best  because  it  has  lowest  AICc.

We  can  also  analyze  data  ignoring  the  detection  limit.  Y ou  can  see  that  we  will  have  a  much  smaller  model  with  five  factors  left  in  the  final  model.  T he  model  ignoring   Limit of Detection  will  have  much  less  power  to  detect  significant  factors.

This  showed  you  the  factors  left  in  the  final  model  from  the  generalized  regression  modeling.  If  we  take  into  account   Limit of Detection  for  the  response,  or  if  we  ignore   Limit of Detection  in  the  response.  As  you  can  see,  if  we  take  into  account   Limit of Detection,  then  we  have  much  more  significant  factors  in  the  model.  W e  can  only  detect  the  effect  of  C  and  D  and  their  quadratic  effect  in  the  model  if  we  ignore   Limit of Detection  for  our  response.

Again,  this  is  comparison  of  the  parameter  estimate  from  the  model  if  we  consider   Limit of Detection  in  the  modeling  or  ignoring   Limit of Detection  in  the  modeling.  Ignoring   Limit of Detection  in  the  modeling  would  give  us  the  bias  estimate  of  the  parameter  as  well.

This  slide  shows  you  the  prediction  Profiler  of  the  response  if  we  perform  the  modeling  by  considering  the   Limit of Detection  versus  ignoring  the   Limit of Detection.  If  we  consider  the   Limit of Detection  in  the  modeling,  then  we  get  a  model  with  all  the  terms  in  the  model,  the  main  effects  as  well  as  some  of  the  interaction  and  quadratic  terms.  T his  model  makes  much  more  sense  to  our  collaborators.

Remember  that  at  lower  level  of  C  and  at  higher  level  of  D,  we  have  more  censoring  data.  That  means  the  detection  time  is  longer  and  the  prediction  Profiler  showed  that  at  lower  level  of  C  and  a  higher  level  of  D,  the  predicted  detection  time  is  longer.  A lso  because  we  have  more  censored  data  in  those  region,  so  the  confidence  interval  for  the  prediction  P rofiler  is  wider.

If  we  ignore  the   Limit of Detection  in  the  analysis,  we  get  much  less  significant  factors.  Only  C  and  D  showed  up  in  the  model,  and  the  parameter  estimate  is  also  biased.  This  one  shows  you  the  diagnostic   plotting  of  observed  data  on  the  y- axis  versus  predicted  data  on  the  x- axis.

If  we  consider   Limit of Detection  in  the  generalized  regression  modeling,  it  gives  correct  prediction.  But  if  we  ignore   Limit of Detection  in  the  modeling,  then  it  will  give  incorrect  prediction  for  your  data.

In  addition  to  the  prediction  Profiler,  JMP  generalized  regression  modeling  would  also  give  you  two  profilers  similar  to  those  from  Parametric  Survival M odeling  platform.  Those  are  the  Distribution  Profiler  and  Quantile  Profiler.  The  distribution  profiler  will  give  you  the  failure  probability  at  a  certain  combination  of  our  formulation  factors  and  a  certain  detection  time.

The  Quantile  Profiler  will  give  you  the  quantile  of  the  detection  time  at  a  certain  combination  of  our  formulation  factors  and  the  specified  failure  probability.  T hese  two  profilers  are  available  in  JMP  under  the  Generalized  Regression  Modeling.

But  one  advantage  of  using  Generalized R egression  Modeling  to  analyze  time  to  failure  type  of  data  is  that  it  would  provide  you  the  Prediction  Profiler,  and  this  type  of  profiler  is  much  more  easier  for  our  collaborator  to  understand.  I t's  much  harder  to  explain  the  Distribution  Profiler  and  Quantile  Profiler  to  our  collaborators.

Now  it  comes  to  the  analysis  of  the  second  endpoint,  the  log  reduction  in  mold.  Again,  we  can  use  histogram  and  the  scatterplot  to  visualize  our  data  and  visualize  the  factor  versus  censoring  relationship.  As  you  can  see  from  the  left  histogram,  you  can  see  that  we  have  a  lot  of  data  that  are  right  censored  at  six  unit.

We  can  see  censoring  at  all  level  of  our  formulation  factors,  except  at  higher  level  of  C  and  lower  level  of  E.  T his  is  the  region  of  concern.  We  have  seen  a  lot  of  censoring  at  lower  level  of  C  and  higher  level  of  E.  That  means  at  lower  level  of  C  and  higher  level  of  E,  it's  good  for  the  product.  We  have  higher  log  mold  reduction.

Again,  we  can  use  detection  limit  on  the  column  property  to  specify  the   Limit of Detection  for  this  endpoint.  W e  used  upper  detection  limit  of  six  in  this  column  property.  N ow  the  next  step  is  to  analyze  this  data  using  the  Generalized R egression  modeling  by  taking  into  account  the   Limit of Detection.  W e  use  LogN ormal  distribution  and  forward  selection.

Interestingly,  we  found  that  the  RS quare  is  one  and  this  is  very  suspicious.  A lso,  we  see  some  red  flag.  The  AICc  had  a  severe  drop  after  step  17.  T he  standard  error  of  the  estimate  as  well  as  the  estimate  for  the  scale  parameter  seems  to  be  extremely  small.  A lso,  the  diagnostic  plot  showed  perfect  prediction  from  the  model.  W e  know  that  the  model  has  overfit.

This  is  the  Prediction  Profiler,  and  they  showed  very  narrow  confidence  interval  for  the  prediction,  and  we  knew  that  our  model  is  overfit.  So  what  we  did  for  the  modeling  is  that  we  tried  a  simpler  model  by  removing  the  quadratic  terms  from  the  initial  response  surface  model.

We  found  that  LogN ormal  with  forward  selection  model  fits  the  data  the  best  because  it  has  a  lowest  AICc  and  BIC.  T his  time,  the  solution  path  looks  more  reasonable  as  well  as  the  standard  error  estimate  of  our  parameters  and  estimate  of  the  scale  parameter  of  the  LogN ormal  distribution.  T he  diagnostic  plot  looks  more  reasonable  now.

This  is  the  Prediction  Profiler  of  the  final  model  after  we  removed  the  quadratic  terms.  This  Prediction  Profiler  makes  a  lot  more  sense.  Recall  that  at  lower  level  of  C  and  at  higher  level  of  E,  we  have  more  censored  data  you  can  see  here.  That  means  at  lower  level  of  C  and  higher  level  of  E,  we  have  higher  log  mold  reduction.

It  showed  on  the  Prediction  Profiler  because  it  has  more  censored  data  in  this  region  and  the  confidence  interval  for  the  prediction  is  wider.  We  can  also  compare  the  final  model  Prediction  Profiler  if  we  ignore   Limit of Detection  in  the  modeling.

If  we  ignore   Limit of Detection  in  the  modeling,  we  got  less  significant  model  factors  as  well  as  biased  results.  If  we  ignore   Limit of Detection  in  the  Generalized  Regression  modeling,  then  the  second  model,  which  is  incorrect  and  is  trying  to  use  the  quadratic  term  to  predict  in  the  lower  level  of  C  and  higher  level  of  E. So t rying  to  get  the  predictive  value  close  to  the   Limit of Detection,  and  we  knew  that  this  result  is  biased.

Fangyi has  nicely  shown  here  that  the  incorrect  analysis,  ignoring  the   Limit of Detection,  leads  to  some  seriously  biased  results.  And  that  getting  the  analysis  right  is  easy  if  you  set  up  the  detection  limits  in  either  the  custom  designer  or  as  a  column  property.

I'm  going  to  go  through  one  more  example  that  has  measurements  at  different  times,  which  adds  a  little  bit  more  complexity  to  the  model  set  up,  and  in  our  case,  required  some  table  manipulation  to  get  the  data  in  the  right  format.

Here  is  the  data  table  of  the  second  DOE  in  basically  the  form  that  it  originally  came  to  us.  In  this  data,  we  have  8  factors,  A  through  H,  and  the  data  has  measurements  at 1  day, 2  days,  and  7  days.  Originally,  our  intent  was  to  analyze  the  3  days  separately,  but  when  we  fit  the  day  7  data,  the  confidence  intervals  on  the  predictions  were  huge.

It  was  apparent  that  there  was  so  much  censoring  that  we  were  unable  to  fit  the  model,  and  so  we  were  either  going  to  have  to  come  up  with  another  strategy  or  back  away  from  some  of  our  modeling  goals.  What  we  ended  up  doing  was  we  used  a  stack  operation  from  under  the  tables  menu  so  that  the  responses  from  different  days  would  be  combined  together  into  a  single  column,  and  we  added  day  as  a  column  that  we  could  use  as  a  regression  term.

In  the  histogram  of  log  reduction,  we  see  the  characteristic  bunching  at  the  detection  limit  of  five.  Combining  the  data  like  this  certainly  seems  to  have  improved  the  impact  of  censoring  on  the  design  and  hopefully  allows  us  to  make  more  effective  use  of  all  the  data  that  we  have.

As  in  the  previous  examples,  we  start  off  fitting  a  full  RSM  model,  but  in  this  case,  because  we  have  day  as  a  term,  we  add  a  day  and  interact  all  of  the  RSM  terms  with  day  in  the  Fit  Model  Launch  Dialog  prior  to  bringing  up  the  generalized  regression  platform.  Again,  we're  going  to  use  the  LogN ormal  distribution  as  our  initial  response  distribution.

Because  this  is  a  large  model,  we  can't  use  best  subset  selection,  so  we  used  pruned  forward  selection  as  our  model  selection  criterion.  We  try  the  LogN ormal,  Gamma,  and  Normal  distributions,  and  clearly  the  LogN ormal  comes  out  as  the  best  distribution  because  its  A ICc  is  205.3,  which  is  more  than  10  less  than  the  second  best  distribution,  which  was  the  Normal,  whose  A ICc  was  257.

Here,  the  model  fit  looks  really  reasonable  with  nothing  suspicious.  The  solution  path  standard  errors,  scale  parameter,  and  the  actual  by- predicted  plots  all  look  pretty  good  and  realistic.  There's  a  little  bit  of  bunching  down  at  the  low  end  of  the  responses,  but  the  thinking  is  that  wasn't  due  to  a  detection  limit,  just  a  part  of  the  discreetness  of  the  measurement  system  at  lower  levels  of  reduction.

Now,  if  we  repeat  this  analysis,  ignoring  the  detection  limit,  it  guides  us  towards  the  normal  distribution.  Here  we  see  the  Profilers  for  the  model  that  incorporated  the  detection  limit  on  the  top  and  the  model  that  ignored  the  detection  limit  on  the  bottom.

As  in  the  other  examples,  we  see  that  the  size  of  the  effects  are  dramatically  muted  when  we  ignore  the  detection  limit  and  we  get  quite  a  different  story  as  there's  a  strong  relationship  between  log  reduction  in  factor  E  when  we  take  the  detection  limit  into  account  properly,  and  that  effect  is  seriously  muted  when  we  ignore  the  detection  limit.

If  we  compare  the  actual  by- predicted  plots  for  the  two  models,  the  model  with  the   Limit of Detection  taken  to  account  properly  is  tighter  around  the  45- degree  line  for  the  uncensored  observations.  W e  see  that  the  model  ignoring  the  detection  limit  is  just  generally  less  accurate  as  the  observations  are  more  spread  out  across  the  45- degree  line.

Those  are  our  two  case  studies.  In  summary,  I  want  to  reiterate  that  detection  limits  are  very  common  in  comical  and  biological  studies.  As  we've  seen  in  our  case  studies,  ignoring  detection  limits  introduces  severe  model  biases.  T he  most  important  message  is  that  using  the  column  property  or  setting  up  the  detection  limits  in  the  custom  designer  make  analyzing  detection- limited  data  much  easier  to  get  correct.

There  are  some  pitfalls  to  watch  out  for  in  that  if  you  see  standard  errors  that  are  unrealistically  small,  or  models  are  unrealistically  accurate,  you  may  need  to  back  off  from  the  quadratic  terms  or  possibly  even  interaction  terms.

We've  shown  how  histograms  can  be  used  to  identify  when  we  have  a  detection  limit  situation.  It's  useful  to  see  the  censoring  relationship  between  different  factors,  because  if  there  are  big  corners  of  the  factor  space  where  all  the  observations  are  missing,  then  we  may  not  be  able  to  fit  interactions  in  that  region  of  the  design  space.

A gain,  if  the  model  looks  too  good  to  be  true,  go  ahead  and  try  a  simpler  model,  back  off  a  bit.  That's  all  we  have  for  you  today.  I  want  to  thank  you  for  your  attention.

Comments

Why not using lasso or elastic net instead of AICC to reduce risk for over fitting?

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