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Elashoff approach section in grant applications

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Elashoff Approach Section in Grant Applications

Elashoff Approach Section in Grant Applications

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  • 1. How  to  cra)  the  “Approach”   sec1on  of  an  R  grant  applica1on   David  Elashoff,  PhD   Professor  of  Medicine  and  Biosta7s7cs   Director,  Department  of  Medicine  Sta7s7cs  Core   Leader,  CTSI  Biosta7s7cs  Program  
  • 2. Overview   •  Preliminary  Data   •  Study  Design   •  Sample  Size  and  Power  Analysis   •  Sta7s7cal  Methods   •  Collaborators   •  Wri7ng  Strategies  
  • 3. Preliminary  Data   •  Primary  Ques7on:  “Is  there  reason  to  believe   that  the  study  hypotheses  could  be  true  and  is   this  research  team  capable  of  carrying  out  the   study?”    
  • 4. Necessary  Elements:  Preliminary   Data   •  Strong  and  relevant  preliminary  data  key  for   R01  grants   •  Demonstrate:   – Exper7se  with  assays   – Novel  assays  work  in  pa7ents/samples  to  be   collected   – Support  for  hypotheses   •  Use  figures  and  tables  where  possible      
  • 5. Ways  to  Fail:  Preliminary  Data   •  Insufficient  annota7on  for  figures/tables   •  Poor  data  analy7c  techniques   •  Weak  support  for  hypotheses   •  Unrealis7cally  strong/naïve  preliminary  results   •  Presen7ng  needle  in  a  haystack  results   •  Presen7ng  too  much  preliminary  data  at   expense  of  rest  of  the  approach  
  • 6. Study  Design   •  Primary  Ques7on:  “Is  the  design  of  the  study   appropriate  to  address  the  study  aims?”  
  • 7. Necessary  Elements:  Study  Design   •  What  is  overall  study  design  (RCT,  Cohort   study,  Case-­‐Control,  Cross-­‐sec7onal,   Biomarkers)     •  Describe  endpoints  and  clarify,  if  necessary,   how  they  will  be  quan7fied  and  their   measurement  scale.   •  Describe  study  popula7on  and  control  groups   •  Inclusion/Exclusion  Criteria   •  Describe  all  study  measures  with  appropriate   measurement  process  details  
  • 8. Addi7onal  Considera7ons:  Study   Design   •  Describe  exis7ng  popula7on  clearly.    -­‐  Include  relevant  demographics    -­‐  Include  informa7on  on  prognos7c  or    confounding  measures.   •  Nothing  says  that  this  is  a  ready  to  go  study   be^er  than  a  clearly  defined  popula7on  that  is   relevant  to  the  study  aims.  
  • 9. Addi7onal  Considera7ons   •  Randomiza7on  methods  for  clinical  trials   •  Collect  confounding  factors   •  How  long  will  follow-­‐up  period  be?   •  Validity  and  reliability  of  study  measures   •  Subject  matching?   •  Valida7on  of  model  building  either  with  cross-­‐ valida7on  or  training-­‐test  designs  
  • 10. Ways  to  Fail:  Study  Design   •  Study  popula7on  or  design  doesn’t  match   objec7ves   •  Insufficient  7me  for  recruitment  and  follow-­‐ up.   •  Lack  of  clarity  with  respect  to  availability  of   subjects   •  Very  uninteres7ng  to  read  technical  details  of   assays  that  are  standard  
  • 11. Sample  Size   •  Primary  Ques7on:  “Is  the  sample  size   sufficient  to  give  the  study  the  ability  to   answer  the  primary  study  ques7ons?”  
  • 12. Necessary  Elements:  Sample  Size   •  Iden7fy  study  endpoint(s)  for  all  aims.   •  Clearly  describe  sample  size  for  each  aim   •  For  each  endpoint:   –  What  is  the  effect  of  interven7on  or  magnitude  of  the   rela7onship?   –  How  much  variability?   –  Level  of  power?   –  One  or  two  sided  test?   –  What  is  the  sta7s7cal  test  used  to  compute  power?    
  • 13. Addi7onal  Considera7ons:  Sample  Size   •  Account  for  study  dropouts   •  Account  for  mul7ple  comparisons  (either   Bonferroni  or  False  Discovery  Rate)   •  Ocen  useful  to  examine  sample  sizes  for  a   variety  of  scenarios  when  uncertainty  exists   concerning  what  is  to  be  expected  for  an   endpoint  
  • 14. Ways  to  Fail:  Sample  Size   •  No  power  analysis   •  Sample  size  calcula7on  does  not  have  sufficient   informa7on  for  a  reviewer  to  replicate   •  Sample  size  calcula7on  does  not  use  relevant   preliminary  data  or  methods  described  in  the   sta7s7cal  analysis  sec7on.   •  Predic7on  modeling  with  large  number  of   predictors  rela7ve  to  sample  size   •  Unrealis7c  assump7ons  about  magnitude  of   effect  
  • 15. Bad  Examples   “A  previous  study  in  this  area  recruited  150  subjects  and  found  highly  significant   results  (p=0.014),  and  therefore  a  similar  sample  size  should  be  sufficient  here.”     “Our  lab  usually  uses  10  mice  per  group.”     “Sample  sizes  are  not  provided  because  there  is  no  prior  informa7on  on  which  to   base  them.”     "The  throughput  of  the  clinic  is  around  50  pa7ents  a  year,  of  whom  10%  may  refuse   to  take  part  in  the  study.  Therefore  over  the  2  years  of  the  study,  the  sample  size   will  be  90  pa7ents.  “     “It  is  es7mated  that  for  a  sample  size  consis7ng  of  6  animals  in  each  trial  and  with  a   tumor  volume  variance  from  0.1  to  1.0  cm3  –  that  when  the  difference  in  the   popula7on  reaches  0.25,  the  power  will  reach  100%.”      
  • 16. Good  Examples   “A  sample  size  of  38  in  each  group  will  be  sufficient  to  detect  a  difference  of  5  points   on  the  Beck  scale  of  suicidal  idea7on,  assuming  a  standard  devia7on  of  7.7  points,  a   power  of  80%,  assuming    a  two  sided  significance  level  of  5%  and  a  two  sample  t-­‐test.   This  number  has  been  increased  to  60  per  group  (total  of  120),  to  allow  for  a  predicted   drop-­‐out  from  treatment  of  around  one  third.  This  difference  of  5  points  is  based  on   our  prior  study  in  which…..  ”     “A  sample  size  of  292  babies  (146  in  each  of  the  treatment  and  placebo  groups)  will  be   sufficient  to  detect  a  difference  of  16%  between  groups  in  the  sepsis  rate  at  14  days,   with  80%  power.  This  16%  difference  represents  the  difference  between  a  50%  sepsis   rate  in  the  placebo  group  and  a  34%  rate  in  the  treatment  group.  This  assumes  a  Chi-­‐ square  test  with  a  two  sided  0.05  significance  level.  This  es7mated  difference  in  sepsis   rate  is  based  on  the  study  of  Bob  et  al  [ref]  in  which  they  observed….”  
  • 17. Sta7s7cal  Methods   •  Primary  Ques7on:  “Are  the  sta7s7cal  methods   appropriate  for  the  analysis  of  the  data  that   will  be  collected?”  
  • 18. Necessary  Elements:  Sta7s7cal   Methods   •  Need  methods  sec7on  for  each  aim.   •  Clearly  describe  analy7c  strategies  for  each   endpoint.   •  Methods  should  be  appropriate  for  type  of   variable  (ex.  categorical,  ordinal,  count)  and   study  design   •  Typically  includes  inferen7al  tes7ng  of   endpoints  and  model  building  
  • 19. Addi7onal  Considera7ons:  Sta7s7cal   Methods   •  Sta7s7cal  methods  appropriate  for  sample   size  (ex.  Fisher  test  vs  Chi-­‐square  test)   •  Include  evalua7on  and  valida7on  strategies  for   regression/predic7on  models   •  Can  include  model  assump7on  checking   methods   •  Accoun7ng  for  missing  data  
  • 20. Ways  to  Fail:  Sta7s7cal  Methods   •  Ignoring  key  confounders  or  demographic   variables.   •  Ignoring  standard  prognos7c  or  predic7ve   measures  in  models   •  Describing  socware  but  not  ideas/methods   •  Analy7cal  approach  not  appropriate  for  design   and  research  ques7on  
  • 21. Ways  to  Fail:  Sta7s7cal  Methods   •  Ignoring  key  confounders  or  demographic   variables.   •  Ignoring  standard  prognos7c  or  predic7ve   measures  in  models   •  Describing  socware  but  not  ideas/methods   •  Analy7cal  approach  not  appropriate  for  design   and  research  ques7on  
  • 22. Collaborators   •  Primary  Ques7on:  “Does  the  study  have   appropriate  collaborators  with  sufficient  effort   to  perform  the  research  described?”  
  • 23. Necessary  Elements:  Collaborators   •  Need  an  iden7fied  sta7s7cal  collaborator  with   appropriate  experience   •  Biosketchs  for  faculty  collaborator   •  Budget  jus7fica7on  for  collaborator   •  Le^er  of  Support  if  no  funding  is  in   applica7on.   – Make  use  of  collaborators  from  on-­‐campus  service   groups.  (Ex:  CTSI,  Cancer  Center)  
  • 24. Addi7onal  Considera7ons:   Collaborators     •  Staff  collaborator  only  need  biosketch  if  no   faculty  on  applica7on.   •  Can  include  small  %  effort  for  expensive   faculty  and  larger  %  for  staff  support.   •  Make  sure  areas  of  weakness  are  covered  with   experienced  collaborator   •  Don’t  include  many  collaborators  with   minimal  effort   •  Not  enough  to  men7on  collaborators  and   write  that  they  will  take  care  of  details  
  • 25. Wri7ng  Strategies   •  Use  the  resources  and  human  subjects   sec7ons  to  full  effect   – Can  give  details  of  available  study  popula7on  and   subject  demographics   •  Standard  experimental  methods  can  be   referenced   •  Long  blocks  of  text  are  boring  a  can  ocen  get   skimmed.   •  Emphasize  key  points:  bold,  underline  
  • 26. Wri7ng  Strategies   •  Graphical  displays:   – Theore7cal  Framework   – Experimental  Design   – Aims  flowchart   – Pa7ent  characteris7cs   – Study  measures  
  • 27. Don’t  Waste  Space  
  • 28. Grant  Applica7ons  Assistance   •  Assistance  with  preparing  grant  applica7ons   (CTSI)   –  Study  Design   –  Data  Analysis  Protocols   –  Sample  Size  and  Power  Analysis   –  Budge7ng  and  Iden7fying  Appropriate  Collaborators   –  Core  facili7es     •  Substan7al  lead  7me  with  opportunity  for   mul7ple  itera7ons  is  necessary  for  high  quality   grant  applica7on  assistance:  Study  Design  vs   Analysis  sec7ons  
  • 29. Final  Thoughts   •  Consult  sta7s7cal  collaborator  for  study  design   and  approximate  sample  size  some  weeks  in   advance   •  Most  successful  proposals  require  mul7ple   itera7ons  of  research  design  sec7ons  

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