A  Transla)onal  Framework  for  Methodological  Rigor  to  Improve  Pa)ent  Centered  Outcomes  in  End  of  Life  Cancer  Research Francesca Dominici, PhD Senior Associate Dean for Research Professor of Biostatistics Harvard TH Chan School of Public Health

K18 HS021991

PROJECT OVERVIEW

Specific  Aims

1.  Par0cipate  in  an  intense,  mentored  career  development  experience  in  CER  with  a  special  focus  on  cancer  

2.  Conduct  a  research  project  on  Glioblastoma  

3.  Maximize  the  policy  impact  of  the  research  

Students & Postdocs Nils Arvold, MD Attending Physician, Radiation Oncology Associates, St. Luke’s Cancer Center and University of Minnesota Duluth Former Assistant Professor, Harvard Medical School; and Neuro-Radiation Oncologist and Fellowship Director, Department of Radiation Oncology, Dana-Farber/Brigham & Women’s Cancer Center

Cory Zigler, PhD Assistant Professor of Biostatistics, Department of Biostatistics, Harvard T.H. Chan School of Public Health

Deborah Schrag, MD, MPH Professor of Medicine at Harvard Medical School and the Chief of the Division of Population Sciences in the Department of Medical Oncology at the Dana-Farber Cancer Institute

Danielle Braun, PhD Postdoctoral Fellow, Department of Biostatistics, Harvard T.H. Chan School of Public Health

Matthew Cefalu, PhD Associate Statistician at the RAND Corporation Former Postdoctoral Fellow, Department of Biostatistics, Harvard T.H. Chan School of Public Health

Yun Wang Senior Research Scientist in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health

Study Team

Joey Antonelli, PhD Doctoral Student, Department of Biostatistics, Harvard University

Dissemination

Results

Develop  new  methods  (whenever  is  necessary)  

Construct  the  analy0cal  data  set  from  claims  and  cancer  registry  data

Formulate  Clinical  Ques0ons  

Adjustment for confounding

Treatment effect heterogeneity/

personalized medicine

Measurement error in the treatment assignment

Combining heterogeneous sources of data

Methodological Challenges

NEW PROJECT

1. What factors are associated with a high hospitalization burden among brain cancer

patients?

3. Do palliative care interventions among terminal

cancer patients reduce hospitalizations, ER visits

and other measures of aggressive care?

2. Does adding chemotherapy to radiation prolong survival for elderly

brain cancer patients?

Clinical Questions

Statistical Methods

Development

1

2

3

4

CLINICAL CONTEXT Comparative Effectiveness Research for Medicare Patients with Glioblastoma

Glioblastoma  (GBM) •  55,000  primary  brain  tumors  per  year  in  U.S.  • GBM  is  the  most  common  malignant  primary  brain  tumor  •  Approximately  15,000  GBM  cases/year  in  U.S.  • Median  age  at  GBM  diagnosis:    65  years  

Glioblastoma  (GBM)

• Despite  half  of  all  GBM  pa0ents  being  elderly,  older  pa0ents  under-­‐represented  in  RCTs    

•  Age  ≥  70  y.o.  excluded  from  2005  landmark  RCT    

• Median  survival    range  for  GBM:    6  to  18  months    

• Uncertainty  about  how  to  treat  and  how  to  improve  quality  of  life    

Three  Clinical  Ques0ons 1.  What factors are associated with a high hospitalization

burden among brain cancer patients? •  Arvold ND, Wang Y, Zigler C, Schrag D, Dominici F (2014) Hospitalization burden and survival

among elderly patients with glioblastoma, Neuro-Oncology

2.  Does adding chemotherapy to radiotherapy prolong survival for elderly brain cancer patients?

•  Arvold ND, Cefalu M, Wang Y, Zigler C, Schrag D, Dominici F Radiotherapy with vs. without temozolomide in older patients with glioblastoma, submitted

3.  Do palliative care interventions among terminal cancer patients reduce hospitalizations, ER visits and other measures of aggressive care?

Routine care settings, populations with little/no clinical trial evidence

Ques0on  #2:  TMZ/RT  vs.  RT  in  Elderly  GBM  Pa0ents

Purpose: •  To examine overall survival

among elderly GBM patients receiving TMZ/RT vs. RT alone

Rationale: •  Concurrent TMZ/RT widely

used/recommended for elderly GBM patients, but benefit of TMZ is unclear in this population

Ques0on  #3:  Pallia0ve  Care  and  Quality  at  End  of  Life

Is receipt of palliative care associated with reduced aggressiveness of end-of-life care in advanced cancer Medicare patients?

§  Treatment: receiving a palliative care intervention at EOL

§  Outcomes •  receipt of chemotherapy within 30 days of death (Yes vs No) •  more than 1 emergency room visit within 30 days of death (Yes vs No) •  more than 1 hospitalization within 30 days of death (Yes vs No), or

alternatively, cumulative # of days hospitalized within 30 days of death •  death at home (%) •  enrolled on hospice (%) •  overall survival from diagnosis date

IN-­‐HOUSE  AVAILABLE  MEDICARE  DATA

In-­‐house  Data •  100% sample of inpatient claims data (1999-2013)

•  Condition-specific post-acute care data (2009-2011, for

pancreases, brain, colon, lung, and bladder cancers) –  Outpatient –  Nursing home –  Hospice (not in our SEER-Medicare data) –  Home health care –  Part B, and –  Durable medical equipment

•  100% sample of Medicare enrollment data (1999-2013)

•  Condition-specific SEER Medicare (1991-2009)

– Prostate, stomach, bladder, colorectal, breast, lung, and brain cancers

Post-acute Care, an Important Aspect of Care

Poten0al  Sequence  ader  Index  Hospitaliza0on    

Index Admission

To Home

To SNF Hospice

Re-Admission

Death

METHODOLOGICAL  CHALLENGES

Methodological  Challenge  #1:  Confounding  Adjustment  Uncertainty  

•  Treatments are not randomized and need statistical adjustment to estimate causal effects.

•  Existing approaches assume that the potential confounders are known and measured.

•  Often we have a high-dimensional set of possible confounders in administrative data:

 –  Demographics  (age,  race,  sex  ...)    –  Clinical  characteris0cs  (tumor  size,  comorbidi0es,  ...)    –  Hospital  characteris0cs  (pa0ent  volume,  teaching,  ...)    –  Physician  characteris0cs  (specialty,  case  volume,  ...)  

•  In reality, the factors required for adjustment are unknown and must be chosen from a high-dimensional set of possibilities.

Baseline characteristics (% experiencing) and 1-year mortality rate for patients treated with temozolomide plus radiotherapy (n=776) and radiotherapy alone (n=1111). We also report estimated inclusion probabilities defined as the probability that each of these characteristics to be an important confounder.

Cefalu M, Dominici F, Arvold N, Parmigiani G (2015) A Model averaged double robust estimator, Biometrics (under review)

All possible combinations (1000 x 1000) of propensity score models and outcome models based on which confounders they include

Pro

babi

lity

of d

eath

with

in o

ne y

ear

Unadjusted 11.7% (7.6-16.0%)

Adjusted 6.7% (2.4-10.7%)

Methodological  Challenge  2:  Treatment  Effect  Heterogeneity  (personalized  medicine)

•  Treatments  do  not  affect  everyone  the  same      

•  Exis0ng  methods  es0mate  what  happens  “on  average.”    

•  Ideally  we  would  like  to  iden0fy    popula0on  subgroups  with  different  treatment  effects  and    es0mate  a  different  effect  in  each  group.    

•  How  can  we  use  data  on  high-­‐dimensional  pa0ent  characteris0cs  to  iden0fy  which  subgroups  exhibit  heterogeneity  in  treatment  response?  

Wang C, Dominici F, Parmigiani G, Zigler CM (2015) Accounting for uncertainty in confounder and effect modifier selection when estimating average causal effects in generalized linear models, Biometrics, 20 April 2015. Doi: 10.1111/biom.12315

Important confounders

Important effect modifiers

Methodological  Challenge  3:  Treatment  Assignment  is  measured  with  error  in  claims  data

•  ICD9  billing  codes  in  claims  data  inaccurately  reflect  surgical  treatment  or  any  other  procedure      

•   In  SEER  data,  treatment  is  ascertained  using  medical  chart  reviews    

•  Medicare  part  A  claims  data  are  available  for  n=41,971  (1999-­‐2007)    

•  SEER-­‐Medicare  data  are  available  for  n=5,463  (1999-­‐2007)    

•  Sensi0vity=96.8%,  specificity=73.8%    

•  Goal:  doing  a  CER  of  surgical  resec0on  versus  biopsy  in  the  whole  Medicare  popula0on,  using  SEER-­‐Medicare  to  correct  for  error  in  the  treatment  assignment  

Medicare Part A/Seer-Medicare Data Analysis Results

SEER-Medicare (Gold Standard)

Medicare Part A No Adjustment

Medicare Part A (Adjustment)

Average Treatment Effect [95% CI]

-0.03 [-0.07, 0.01]

-0.16 [-0.18, -0.14]

-0.13 [-0.13, -0.12]

Braun D, Parmigiani G, Arvold N, Gorfine M, Dominici F, Zigler C Propensity Scores with Measurement Error in the Treatment Assignment: a Likelihood-Based Adjustment, submitted

An ATE of -0:16 implies that the probability of dying within one year is 16% larger for those who received a biopsy compared to those who had a resection

Methodological  Challenge  4:  Combining  Medicare  and  SEER-­‐Medicare  to  adjust  for  confounding

•  SEER-­‐Medicare  data  has  an  extensive  set  of    confounders  (stage  of  the  tumor,  loca0on  of  the  tumor,  extent  of  the  resec0on,  data)    

• Medicare  claims  data  has  very  limited  informa0on  on    confounders  (age,  race,  zip  code  of  residence)    

•  Goal:    CER  of  surgical  resec0on  versus  biopsy  on  1  year  mortality  in  the  whole  in  Medicare  popula0on  but  using  all  the  available  measured  confounders  in  SEER  

Y C X U

n>>m

Death within 1 year

Surgery vs. biopsy

Age, gender, race, comorbidities and region, from Medicare data

e.g. Tumor number, size, and location, from SEER-Medicare

m

m+n

Antonelli J, Dominici F, Using External Validation Data to Adjust for Confounding, in prep

DISSEMINATION

PUBLICATIONS  AND  ADDITIONAL  FUNDED  GRANTS

Publications

•  Arvold  ND,  Wang  Y,  Zigler  C,  Schrag  D,  Dominici  F  (2014)  Hospitaliza0on  burden  and  survival  among  elderly  pa0ents  with  Glioblastoma,  Neuro-­‐Oncology,  16-­‐11:1530-­‐40.  doi:  10.1093/neuonc/nov060.  PMCID:  PMC4201065.  Read  more  about  this  paper  on  Healio.com  

•  Zigler    CM,  Dominici  F  (2014)  Uncertainty  in  propensity  score  es0ma0on:  Bayesian  methods  for  variable  selec0on  and  model  averaged  causal  effects.  JASA,  109(505):95-­‐107.  PMCID:  PMC3703764  

•  Wang  Y,  Schrag  D,  Brooks  G,  Dominici  F  (2014)  Na0onal  trends  in  pancrea0c  cancer  outcomes  and  parern  of  care  among  Medicare  beneficiaries:  2000-­‐2010,  Cancer,  120(7):1050-­‐8.  DOI:  10.1002/cncr.2853.  PMCID:  PMC4019988.  

•  Obermeyer  Z,  Makar  M,  Abujaber  S,  Dominici  F,  Block  S,  Cutler  DM.  (2014)  Associa0on  between  the  Medicare  hospice  benefit  and  health  care  u0liza0on  and  costs  for  pa0ents  with  poor-­‐prognosis  cancer,  JAMA,  312(18):1888-­‐1896.  doi:10.1001/jama.2014.14950.  PMCID:  PMC4274169.  

•  Wang  C,  Dominici  F,  Parmigiani  G,  Zigler  CM  (2015)  Accoun0ng  for  uncertainty  in  confounder  and  effect  modifier  selec0on  when  es0ma0ng  average  causal  effects  in  generalized  linear  models,  Biometrics,  20  April  2015.  Doi:  10.1111/biom.12315  

•  Cefalu    M,  Dominici  F,    Arvold  N,  Parmigiani  G  (2015)  A  Model  averaged  double  robust  es0mator,  Biometrics  (under  review)  

•  Braun  D,  Parmigiani  G,  Arvold  N,  Gorfine  M,    Dominici  F  Zigler  C  Propensity  Scores  with  Measurement  Error  in  the  Treatment  Assignment:  a  Likelihood-­‐Based  Adjustment,  submired  

•  Arvold  ND,  Cefalu  M,  Wang  Y,  Zigler  C,  Schrag  D,  Dominici  F  Radiotherapy  with  vs.  without  temozolomide  in  older  pa0ents  with  glioblastoma,  submired  

hrp://wordpress.sph.harvard.edu/dominici-­‐lab/research/compara0ve-­‐effec0veness-­‐research-­‐in-­‐cancer/

Funded Grants •  R01  GM111339  (Normand/Dominici)  Bayesian  Methods  for  Compara)ve  Effec)veness  Research  with  Observa)onal  Data  

Cory Zigler, PhD Assistant Professor of Biostatistics, Department of Biostatistics, Harvard T.H. Chan School of Public Health

Francesca Dominici, PhD Professor of Biostatistics, Department of Biostatistics & Senior Associate Dean for Research, Office of the Dean, Harvard T.H. Chan School of Public Health

Sharon-Lise Normand, PhD Professor of Health Care Policy in the Department of Health Care Policy at Harvard Medical School and Mentor of Dr. Dominici K18

Sherri Rose, PhD Assistant Professor of Health Care Policy (Biostatistics) in the Department of Health Care Policy at Harvard Medical School

Ques0ons  for  the  Advisory  Board •  Addressing Methodological Gaps: How has this research project advanced

the field of CER in cancer and what are some of the questions that will be important to investigate moving forward?

•  Assessing Clinical Impact: Based on the research results, what are the next steps for translating this knowledge into actionable hospital and outpatient-based performance metrics?

•  Stakeholder Engagement & Patient Advocacy: How can we be more responsive to and involve stakeholders in the research project and what is the best approach to engage stakeholders?

•  Dissemination: What are the target populations for the dissemination of research results and what is the most effective way to reach these groups?

•  Policy Impact: Moving forward, how do we maximize the policy impact of the research?