Biostatistics Program at Penn
Challenges of the Past …
Visions for the Future
J. Richard Landis, PhD, Professor and Director
Division of Biostatistics/Biostatistics Unit
Center for Clinical Epidemiology and Biostatistics (CCEB)
University of Pennsylvania School of Medicine
Philadelphia, PA 19104-6021
Presented at the
ASA Philadelphia Spring Meeting
Wyeth Conference Center
Wyeth Collegeville Campus
June 10, 2008
© 2008 University of Pennsylvania School of Medicine
Outline: Developing Biostatistics at Penn
 Historical Perspectives
 Organizational issues
 Faculty recruitment and retention
 Launching and sustaining a nationally competitive graduate
(PhD, MS) training program
 Promoting effective balance between collaborative and
methodological research
 Recruiting and retaining excellent biostatistical
analyst/programmer, data management and project
management research staff
 Promoting and deploying a leading-edge research IT
infrastructure
 Deploying biomedical informatics methods and tools, within a
rapidly changing research landscape
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Outline: Developing Biostatistics at Penn
 Case Studies in Collaborative & Methodological Research
 Major challenges
• Cultivating a new generation of biostatistical scientists with
the technical breadth, as well as the leadership skills, to
guide multidisciplinary research teams within the evolving
clinical and translational science award (CTSA) paradigm of
NIH Roadmap research
• Pursuing new partnership approaches with industry for
graduate education/training that includes collaborative
approaches to scientific inquiry
• Promoting multidisciplinary teams (industry, academia) to
harvest the research potentials of enterprise-wide healthcare
system practice data
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Outline: Developing Biostatistics at Penn
 Historical Perspectives
• Personal experiences: institutions / mentors / roles
– Millersville U (1965-69) student: math/statistics/computing
– West Haven VA, CT (1969-71) statistical programmer
– UNC, Chapel Hill (1971-75) biostatistics grad student
– U Michigan, Ann Arbor (1975-88) professor
– Penn State U, Hershey (1988-97) professor & director
– U Penn, Phila. (1997-present) professor & director BU
• National context of academic departments
• Early phases at Penn
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
National Context - Biostatistics
Birth of academic biostatistics departments
CCEB
Johns Hopkins University
~ 1923
Harvard University
~ 1946
UNC, Chapel Hill
~ 1949
Univ. of Michigan, Ann Arbor
~ 1959
Univ. of Washington, Seattle
~ 1970
Univ. of Wisconsin, Madison
~ 1981
Univ. of Pennsylvania:
CCEB
Dept. Biostats & Epid.
~ 1993
~ 1995
© 2008 – 2009 University of Pennsylvania School of Medicine
Why Not U of Penn until 1995?
 Medical School highly ranked in NIH funding
 Major university
• Penn is the nation's first university – including the first
medical school, first business school, first university
teaching hospital and first modern liberal-arts curriculum
• Penn is the birthplace of technological invention. In 1946,
Penn introduced ENIAC, the world's first electronic, largescale, general-purpose digital computer
 Natural home?
• No School of Public Health
• Where in the School of Medicine?
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Early Developments at Penn
 “First” School of Public Health (1890s? - ??)
 Department of Preventive Medicine (19?? – 19??)
 Department of Community Medicine (19?? – 1971)
 Department of Research Medicine (19?? – 1981)
 Clinical Epidemiology Unit (1977 –
 Center for Clinical Epidemiology and Biostatistics (CCEB)
(1993 –
 Department of Biostatistics and Epidemiology (1995 –
 Biostatistics Unit / Division of Biostatistics (1997 –
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Outline: Developing Biostatistics at Penn
 Historical Perspectives
 Organizational issues
 Faculty recruitment and retention
 Launching and sustaining a nationally competitive graduate
(PhD, MS) training program
 Promoting effective balance between collaborative and
methodological research
 Recruiting and retaining excellent biostatistical
analyst/programmer, data management and project
management research staff
 Promoting and deploying a leading-edge research IT
infrastructure
 Deploying biomedical informatics methods and tools, within a
rapidly changing research landscape
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Organizational Placement Issues
 Separate, Centralized Unit
 Sub-unit within clinical or basic
science department
• perceived equal access by
other departments
• perceived increased
access/integration in content
• peer professional discipline
area of “home” unit
identity in biostatistics
• facilitates specialized content
• specialized methods
(cancer, AIDS, cardiovascular,
expertise sharing
neurosciences, etc.) expertise
• facilitates academic program
• facilitates identity of
development
biostatistician within larger
• facilitates professional staff
clinical discipline
recruitment / retention
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Centralized, but with Specialty Cores
 Separate, Centralized Unit
• Core faculty office space
• Core administrative /
business resources
• Core statistical analysts /
programmers
• Core computing resources
CCEB
 Cores within Biostatistics Unit
• Cancer
• CFAR (HIV / AIDS)
• Women’s Health (OB / GYN)
• Cardiovascular
• Neurodegenerative Diseases
• Psychiatry
• Pediatrics
• Genomics / Genetics
© 2008 – 2009 University of Pennsylvania School of Medicine
Biostatistics at Penn
http://www.cceb.upenn.edu
CCEB
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Outline: Developing Biostatistics at Penn
 History
 Organizational issues
 Faculty recruitment and retention
 Launching and sustaining a nationally competitive graduate
(PhD, MS) training program
 Promoting effective balance between collaborative and
methodological research
 Recruiting and retaining excellent biostatistical
analyst/programmer, data management and project
management research staff
 Promoting and deploying a leading-edge research IT
infrastructure
 Deploying biomedical informatics methods and tools, within a
rapidly changing research landscape
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Who are the Biostatistics Faculty?
 Currently, there are 28 primary faculty
 Experience…
• From 0-33 years each, as faculty
• Curriculum & graduate school experience
from:
Columbia
Macquarie U
UCLA
U Michigan
Geo. Wash. U
CCEB
Harvard
Old Dominion
U Chicago
UNC-Chapel Hill
Emory U
© 2008 – 2009 University of Pennsylvania School of Medicine
Johns Hopkins
Penn State
U Conn
U Wash-Seattle
Faculty Expansion: Cumulative No.
(incld. expected) by Track & Year
Year
Total
20
 1989 – `92
1
 1993 – `95
2
18
 1996
4
16
 1997
8
14
 1998
12
 1999
12
15
 2000
15
 2001
17
8
 2002
18
6
 2003
20
4
 2004
22
 2006
27
 2007
‡
‡ Tenured 7; tenure track: 1 ;
28
TT
CE
10
2
0
'89 '92 '95 '98 '01 '04 '07
CE track: 20
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Areas of Faculty Expertise
 Bayesian modeling
 Longitudinal methods
 Categorical data
 Measurement error models
 Causal inference
 Meta-analysis
 Clinical trials
 Missing data
 Clustered data
 Multiple imputation
 Complex sample surveys
 Multivariate analysis
 Cost-benefit analyses
 Repeated measures
 Cross-over trials
 Spatial analyses
 Functional genomics
 Statistical genetics/bioinformatics
 Functional predictive modeling
 Survey sampling
 Genetic/genomic modeling
 Survival analysis
 Health Economics
 Time series
 Health services research
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Major Areas of Faculty Collaborations
 Medical imaging
 Aging
 Neurodegenerative diseases
 Bioinformatics
 Pharmacoepidemiology
 Cancer
 Clinical epidemiology
 Clinical trials
 Health services research
CCEB
 Psychometrics
 Statistical
 Disparities research
 HIV/AIDS
 Psychiatry
genetics/genomics
 Urology/Renal
 Women’s Health
© 2008 – 2009 University of Pennsylvania School of Medicine
Faculty Recruitment Goals
2007 – 2012 (Target N = 36)
(Current TT/8, CE/20; N=28)
 Increase leadership in research methodology
• Coverage for emerging new areas requiring
specialized methods (e.g., microarrays, image &
signal data, genetics, genomics, bioinformatics,
proteomics)
 Increase diversity and availability of dissertation
advisors
 Increase mentoring for junior faculty in both
methods and career development
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Biostatistics Faculty
 Bellamy, Scarlett (2001) Assistant Professor
ScD (Biostatistics), Harvard, 2001; ScM (Biostatistics),
Harvard, 1997
 Bilker, Warren B. (1992) Professor
PhD (Biostatistics), Johns Hopkins, 1992; MS (Statistics),
Temple, 1984
 Boston, Raymond C. (1996) Professor
PhD (Physics), Univ. of of Melbourne, Australia, 1970;
MS (Physiology), Univ. of Melbourne, Australia, 1967
 Chen, Jinbo (2006) Assistant Professor
PhD (Biostatistics), Univ. of Washington, Seattle, 2002;
MS (Biostatistics), Univ. of Washington, 1999
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Biostatistics Faculty
 Chen, Zhen (2003) Assistant Professor
PhD (Statistics), Univ. of Connecticut 2001
 Ellenberg, Jonas H. (2004) Professor
PhD (Mathematical Statistics), Harvard, 1970; AM
(Mathematical Statistics), Harvard, 1964
 Ellenberg, Susan S. (2004) Professor
PhD (Mathematical Statistics),
George Washington Univ., 1980
 Gimotty, Phyllis A. (1998) Professor
PhD (Biostatistics), Univ. of Michigan, 1984; MS
(Statistics), Univ. of Michigan, 1972
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Biostatistics Faculty
 Guo, Wensheng (1998) Associate Professor
PhD (Biostatistics), Univ. of Michigan, 1998;
MS (Biostatistics), Univ. of Colorado, 1994
 Heitjan, Daniel F. (2002) Professor
PhD (Statistics), Univ. of Chicago, 1985;
MS (Statistics), Univ. of Chicago, 1984
 Hwang, Wei-Ting (2001) Assistant Professor
PhD (Biostatistics), Johns Hopkins Univ., 2001
 Joffe, Marshall M. (1996) Associate Professor
PhD (Epidemiology), Univ. of California, Los Angeles, 1994;
MD, Univ. of Maryland, 1988;
MPH (Biostatistics), Harvard, 1989
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Biostatistics Faculty
 Landis, J. Richard (1997) Professor
PhD (Biostatistics), Univ. of North Carolina, Chapel Hill, 1975;
MS (Biostatistics), Univ. of North Carolina, Chapel Hill, 1973
 Li, Hongzhe (2004) Professor
PhD (Statistics), Univ. of Washington, Seattle, 1995;
MA (Mathematics), Univ. of Montana, Missoula, 1991
 Li, Mingyao (2006) Assistant Professor
PhD (Biostatistics), Univ. of Michigan, 2005;
MS (Mathematics), Nankai Univ., 1999
 Localio, A. Russell (1997) Associate Professor
PhD (Epidemiology), Univ. of PA, 2005;
MS (Biostatistics), Harvard, 1984;
MPH (Health Services), Harvard, 1982;
MA (Economics), Michigan State Univ., 1981;
JD (Law), Univ. of Michigan, 1975
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Biostatistics Faculty
 Mitra, Nandita (2005) Assistant Professor
PhD (Biostatistics), Columbia Univ., 2001;
MS (Biostatistics), Univ. of California, Berkeley, 1996
 Moore, Reneé H. (2006) Assistant Professor
PhD (Biostatistics), Emory Univ., 2006;
MS (Biostatistics), Emory Univ., 2005;
BS (Mathematics), Bennett College, 1999
 Morales, Knashawn H. (2006) Assistant Professor
ScD (Biostatistics), Harvard, 2001;
ScM (Biostatistics), Harvard, 1997
 Propert, Kathleen Joy (1996) Professor
ScD (Biostatistics), Harvard, 1990;
MS (Biostatistics) Harvard, 1984
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Biostatistics Faculty
 Putt, Mary E. (1999) Assistant Professor
ScD (Biostatistics), Harvard, 1998;
PhD (Biology), Univ. of California at Santa Barbara, 1987;
MS (Biology), McMaster Univ., 1983
 Ratcliffe, Sarah (2002) Assistant Professor
PhD (Statistics), Macquarie Univ., Australia, 2001
 Sammel, Mary D. (1997) Associate Professor
ScD (Biostatistics), Harvard, 1995;
MA (Applied Statistics), Univ. of Michigan, 1988
 Shults, Justine (1999) Assistant Professor
PhD (Applied & Computational Mathematics),
Old Dominion Univ., 1996
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Biostatistics Faculty
 Ten Have, Thomas R. (1997) Professor
PhD (Biostatistics), Univ. of Michigan, 1991;
MPH (Biostatistics), Univ. of Michigan, 1982
 Troxel, Andrea B. (2003) Associate Professor
ScD (Biostatistics), Harvard, 1995
 Xie, Dawei (2007) Assistant Professor
PhD (Biostatistics), Univ. of Michigan, 2004;
MA (Mathematical Statistics), Bowling Green State Univ., 1999
 Xie, Sharon Xiangwen (2002) Assistant Professor
PhD (Biostatistics), Univ. of Washington, Seattle, 1997;
MS (Biostatistics), Univ. of Washington, Seattle, 1995
 Yang, Wei Peter (2008) Instructor
PhD (Biostatistics) SUNY at Albany, 2007;
BS (Cell Biology and Genetics), Peking Univ., 2001
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Standard NIH Demographic Report:
Faculty, Division of Biostatistics
Study Title:
GENDER AND MINORITY INCLUSION
Provide the number of subjects enrolled in the study to date
(cumulatively since the most recent competitive award) according to
the following categories. (See Page 9 for definitions.) If there is more
than one study, provide a separate table for each study. In addition,
report on the subpopulations, which are included in the study.
Gender
American Indian/
Alaska Native
Asian
Native Hawaiian/
Other Pacific
Islander
Penn Biostatistics Faculty
Profile – October, 2006
Black/ African
American
White
Total
Female
5
4
8
17
(70.0)
Male
3
0
7
10
(30.0)
TOTAL
8
(29.6)
4
(14.8)
15
(55.6)
27
(100.0)
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Distribution of Gender (Percent Female) by Rank
and Track
Assistant Professor
Associate Professor
Professor
Gender
Total
Tenure
CE
Tenure
CE
Tenure
CE
Female
2
(100.0)
10
(90.9)
0
(0.0)
4
(66.7)
0
(0.0)
1
(0.50)
17
(70.0)
Male
0
1
2
2
4
1
10
TOTAL
2
11
2
6
4
2
27
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Percent Female by Rank & Track
100
90
80
70
60
50
40
30
20
10
0
Tenure
CE
Asst. Prof. Assoc. Professor
Prof.
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Dist’n of Race by Rank, Gender and Track:
Biostatistics Faculty
Rank
Gender
Female
Professor
Male
Female
Associate
Professor
Male
Female
Assistant
Professor
Male
Total
CCEB
Track
American
Indian/
Alaska Native
Asian
Native
Hawaiian/
Other Pacific
Islander
Black/ African
American
White
Tenure
0
CE
Tenure
1
CE
1
1
3
4
1
1
Tenure
0
CE
Tenure
1
CE
Tenure
1
1
CE
4
3
4
4
1
2
2
2
2
3
Tenure
CE
Total
10
0
1
8
(29.6)
© 2008 – 2009 University of Pennsylvania School of Medicine
1
4
(14.8)
15
(55.6)
27
(100.0)
Outline: Developing Biostatistics at Penn
 History
 Organizational issues
 Faculty recruitment and retention
 Launching and sustaining a nationally competitive graduate
(PhD, MS) training program (2000  Promoting effective balance between collaborative and
methodological research
 Recruiting and retaining excellent biostatistical
analyst/programmer, data management and project
management research staff
 Promoting and deploying a leading-edge research IT
infrastructure
 Deploying biomedical informatics methods and tools, within a
rapidly changing research landscape
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Biostatistics Educational Programs
 Strong foundation in theory (partnership with Wharton –
Department of Statistics)
 Excellent collaborative/consulting exposure (partnership
with Clinical Epidemiology)
 Intentional integration of theory, methods & applied fields
 We want our graduates to be known as
“well-rounded & balanced”
• Theory & methods
• Biomedical/Clinical research applications
• Strong collaborative/communication skills
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Degree Programs (MS, PhD)
 Both MS & PhD programs conducted in collaboration with
the Department of Statistics at the Wharton School of
Penn, with many courses offered jointly by the two
departments
 MS program trains students in basic theory and
applications of statistical methods to problems in the
biomedical sciences
 PhD program aimed at training independent researchers
in biostatistics applications and methodology
development
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Typical Course Sequence for Students in
PhD Program (Year 01)
Semester
1ST Year Curriculum: Required Course (Credit)
FALL BSTA 620:
BSTA 630:
and Lab)
BSTA 509:
BSTA 510:
Probability I (1.0)
Statistical Methods and Data Analysis I (1.0) (Lecture
Introductory Epidemiology (0.5)
Introduction to Human Health and Diseases (0.5)
SPRING BSTA 621 Statistical Inference I (1.0)
BSTA 631: Statistical Methods and Data Analysis II (1.0) (Lecture
and Lab)
BSTA 651: Introduction to Linear Models & GLM (1.0)
1One
CCEB
2One
Required –
non-credit
HIPAA
Certification
POR Certification
Ethics Lectures
Consulting
semester of teaching required in either year 3,4, or 5.
Advanced Elective (formal audit) or©one
special
reading
courseof(course
credit) School
in any semester
with approval of student’s thesis advisor.
2008
– 2009
University
Pennsylvania
of Medicine
Typical Course Sequence for Students in
PhD Program (Year 02)
Semester
2ND Year Proposed Curriculum: Required (Credit)
FALL BSTA 622: Statistical Inference II (1.0)
BSTA 652: Categorical Data Analysis (1.0)
BSTA 653: Survival Analysis (1.0)
Required –
non-credit
Consulting II Project
Written Qualifying Examination Parts A & B (first week in January)
SPRING
BSTA 656: Longitudinal Data Analysis (1.0)
BSTA 659: Design of Biomedical Studies (1.0)
Ethics Lectures
Consulting II Project
Advanced Elective
Completion of Consulting II Project/MS Thesis by deadline
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Typical Course Sequence for Students in
PhD Program (Year 03)
Semester
3RD Year Proposed Curriculum: Required (Credit)
FALL BSTA 670: Statistical Computing (1.0)
Advanced Elective
Minor
SPRING Minor
Advanced Elective
BSTA 999 Reading Course
Required –
non-credit
Teaching
Assistantship1
Ethics Lectures
SUMMER Thesis proposal, Oral Preliminary Examination
1One
2One
CCEB
semester of teaching required in either year 3,4, or 5.
Advanced Elective (formal audit) or one special reading course (course credit) in any semester with approval of student’s thesis advisor.
© 2008 – 2009 University of Pennsylvania School of Medicine
Typical Course Sequence for Students in
PhD Program (Year 04, 05)
Semester
4TH Year Proposed Curriculum: Required (Credit)
FALL
BSTA 999 Reading Course (3 course units) or
BSTA 920 Dissertation Research (3 course unit)2
SPRING BSTA 920 Dissertation Research (3 course units)2
Semester
5th Year Proposed Curriculum: Required (Credit)
FALL BSTA 920 Dissertation Research (3 course units)2
SPRING BSTA 920 Dissertation Research (3 course units)2
1One
2One
CCEB
Required –
non-credit
Teaching
Assistantship1
Ethics Lectures
Required –
non-credit
Teaching
Assistantship1
Ethics Lectures
semester of teaching required in either year 3,4, or 5.
Advanced Elective (formal audit) or one special reading course (course credit) in any semester with approval of student’s thesis advisor.
© 2008 – 2009 University of Pennsylvania School of Medicine
Proposal -- Center for Biostatistics
Methods Research
 Focus
 New Faculty
Use University Professorship
(SOM, Wharton, SAS, SEAS) &
Fairhill Chair to attract senior
“Methods” leader
Clinical and translational science
(CTSA) – e.g., metabolism
modeling, pharmacogenomic
modeling
Causal inference / modeling
5+ tenure track faculty
recruitments
Measurement (tools and scale
development / evaluation)
Statistical genetics
Pharmacoepidemiology
Clinical trial designs / methods
Pharmacoeconomics
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Outline: Developing Biostatistics at Penn
 History
 Organizational issues
 Faculty recruitment and retention
 Launching and sustaining a nationally competitive graduate
(PhD, MS) training program
 Promoting effective balance between collaborative and
methodological research
 Recruiting and retaining excellent biostatistical
analyst/programmer, data management and project
management research staff
 Promoting and deploying a leading-edge research IT
infrastructure
 Deploying biomedical informatics methods and tools, within a
rapidly changing research landscape
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Research Challenges: The Mix
1.20000
Local Minimum
Cumulative Percent
1.00000
0.80000
0.60000
0.40000
(55% methods)
0.20000
0.00000
0%
20%
40%
60%
80%
% Collaboration
* Approximate, pending not included
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
100%
120%
Target for Mix over next six years?
 10% Methods, 90% Collaborative
 20% Methods, 80% Collaborative
 30% Methods, 70% Collaborative?
 40% Methods, 60% Collaborative
 50% Methods, 50% Collaborative
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Imperative Considerations for Transforming the Mix
 Choose mix that promotes academic biostatistics division
strengths, while sustaining current strengths of SOM collaborative
mission
 In recruitment of new faculty
• Potential to create focus groups within the Division (e.g.
genetics, causal inference, clinical trials)
• Division's goals w.r.t. number of students and their incoming
competencies
 Ratios of methods to collaboration revenue neutral? If not, what
ranges can we afford?
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Faculty Involvement in Long Term, Large Collaborative
Efforts with Coordinating Center Involvement?

Faculty mentors should ensure a mix of collaborative projects that
provide healthy collaborative research and publication throughput for
individual junior faculty working on large CC clinical studies

Consider COAP requirements for promotion at all faculty levels –
esp. junior faculty publication productivity in determining the proper
mix for each individual faculty member

Consider incorporation of methods research components within long
term collaborative projects, esp. CCs

Consider strengthening the BAC to allow for high level MS support to
coordinate day to day long term study responsibilities under the
supervision of faculty
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Faculty Research Areas of Collaboration
 Aging (disabilities, depression, social functioning)
 AIDS (treatment adherence, viral genomics)
 Cancer (chemoprevention, lung, pancreas)
 Epidemiology (dermatology, pharmaco-epidemiology,
cardiovascular, renal)
 Genetics of Complex Traits (SNPs, microarrays, proteomics)
 Injury Prevention (child safety, firearms)
 Lung Injury (ARDS)
 Neurodegenerative Diseases (Alzhemier’s, Parkinson’s)
 Schizophrenia, Depression
 Sleep (sleep apnea)
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Faculty Leadership of
Data Coordinating Centers (DCCs)
Multi-center Clinical Research Networks
CRIC
NIDDK: Renal
• 13 sites; cohort/subcohort
Faculty Leadership
HI Feldman, JR Landis
UPPCRN
NIDDK: Urology
• ICCRN (10 sites; 2 RCTs) (Landis)
• CPCRN (11 sites, 2 RCTs) (Landis)
JR Landis
TAM/MRI
NCI: Cancer Chemoprevention
T Rebbeck, J Ellenberg
UC
NIDDK: Gastrointestinal
Lewis, J Ellenberg
AAC
NIMH; HIV AA couples
J Jemmott, JR Landis,
SL Bellamy
CATNAP
NHLBI: Sleep Apnea
T Weaver, S Ellenberg
CHAT
NHLBI: Pediatric Sleep Apnea
S Redline, Case Western,
S Ellenberg
NCS
NICHD: National Children’s Study
[Westst], J. Ellenberg
• Cohort study of national random sample of
100,000 women to assess the relationship of
environmental and genetic factors in the
development of childhood disorders and well being
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Faculty Leadership of Cores
CORES
Faculty Leadership
Alzheimer’s Disease
Trojanowski/S. Xie
Cancer
Thompson/Heitjan/Landis;
Guerry/Gimotty; Schnall/Boston
Cardiovascular Institute
Cappola/Putt
Center for AIDS Research (CFAR)
Hoxie/S Ellenberg
Center of Excellence in Environmental Toxicology (CEET)
Penning/Troxel
Lung Injury
Fisher/Lanken/Landis/Localio
Mental Retardation and Developmental Disabilities
Yudkoff/Putt
Parkinson’s Disease
Trojanowski/S. Xie
Photodynamic Therapy
Gladstein/Putt
Psychiatry: Schizophrenia
Gur/Bilker
Psychiatry: Depression in Elderly
Katz/Ten Have
Psychiatry: Weight and Eating Disorders
Wadden/Stunkard/ Berkowitz/
Faith/Moore
Women’s
Driscoll/Sammel
CCEB Reproductive Health Research
© 2008 – 2009 University of Pennsylvania School of Medicine
Partners for Child Passenger Safety
Mechanism of injury
Child in booster
Child in belt
without booster
61% injury reduction: belt-positioning
boosters vs. seat belts ….. JAMA, 2003
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Case Studies
 Novel Methods for the Investigation of Metabolic Systems
using conventional Statistical Tools'. Demonstrates how
metabolic models are solved, and fitted to data using routine
statistical software (R. Boston)
 Development of Improved methods for analysis of diverse
populations (J. Shults)
• Assessment of the role of social support in weight loss studies
in African-American women, via improved estimation of the
correlations with quasi-least squares (Justine Shults &
Shiriki Kumanyika)
• Novel Approaches for analysis of bone strength in children with
renal disease (Justine Shults, Mary Leonard)
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Case Studies
 Cost-effectiveness of Pharmacogenetic Testing to Tailor
Smoking Cessation Treatment
Heitjan DF, Asch DA, Rukstalis M, Patterson F, Lerman C. (2008)
Pharmocogenomics Journal.
In smoking cessation drug trials, some genetic markers appear to have strong interactions
with treatments, e.g., smokers homozygous for the –141C Ins/Del Ins C allele in the
dopamine receptor DRD2 gene do better on bupropion; the rest do better on transdermal
nicotine (the patch).
Suggests a pharmacogenetic (PG) "test-and-treat" strategy: Perform a genetic test to
determine which drug therapy is best.
Methods: Using a Monte Carlo simulation model, we estimated the lifetime smoking
cessation treatment costs and survival under various smoking cessation treatment plans.
Results: showed i) drug therapies are generally cost-effective compared to
counseling alone; ii) varenicline is superior to other drugs and to a PG strategy,
but iii) in a sensitivity analysis, PG was competitive under favorable assumptions.
Conclusions: PG strategies are not yet ready to replace best one-size-fits-all drug
therapy for smoking cessation, but they may be close
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Case Studies (Cont’d)
 Copy Number Variation (CNV) and Human Diseases
Wang K, Chen Z, Tadesse M, Glessner J, Grant SFA, Hakonarson H, Bucan M, Li
M. (2008). Genome Research.
Copy number variation (CNV) is a genomic region that is present at a variable copy number
with respect to a reference genome. CNVs are ubiquitous in the human genome, and many
of them have functional consequences.
CNVs have been shown to be associated with susceptibility to HIV, autism, schizophrenia,
and cardiovascular diseases.
Current available high-throughput whole-genome SNP genotyping technologies allow
detection of CNVs at a higher resolution than conventional approaches.
Methods: Developed a hidden Markov model based approach that jointly models
correlation of signal intensities across markers and genetic inheritance of CNVs for family
members.
Results: Showed that i) incorporation of genetic inheritance in CNV analysis can
significantly increase accuracy of CNV calls and identification of CNV boundaries; ii) can
allow detection of both inherited and de novo CNVs, iii) had superior performance as
compared to existing CNV calling algorithms.
Conclusions: i) CNV is a newly recognized genetic polymorphism, so there is lots of room
for developing new statistical methods. ii) Future studies should consider modeling genetic
inheritance of CNVs in the analysis.
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Genomics and Informatics Core
“(SPIROMICS): Genomics and Informatics Core” (J.R. Landis, Co-PI
with H. Hakonarson, Co-PI) features CTSA-related informatics, research
IT support, Penn inter-disciplinary translational, and CHOP collaborative
efforts. This Genomics and Informatics Center (GIC), will serve as a
Scientific and Data Coordinating Center (SDCC), to support a large, multisite cohort study of 3,200 COPD patients.
This GIC proposal names scientific investigators representing diverse
disciplines in (i) Pulmonary Medicine and Applied Genomics, (ii)
Pathology and Laboratory Medicine, Biomedical Informatics, (iii)
Pulmonary Medicine and Clinical Epidemiology, (iv) Statistical Genetics,
(v) Biostatistics and Clinical Research Informatics, (vi) Biomedical
Informatics and Molecular Genetics, and (vii) Proteomics. The GIC portion
of this clinical and translational science proposal alone represents an NIH
investment of approximately $ 25 M in research funding.
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Methodology Research
 Simulation of power curves for permutation-based testing
method for “correlated correlations” (Bilker)
 The representation of kinetic (e.g. drug, or mineral,
metabolism) data and in terms of mathematical models and
the interpretation of plasma disappearance profiles in terms of
metabolic indices (Boston)
 Methods for correlated data and high dimensional problems,
such as longitudinal data, time series, functional data, imaging
analysis and density estimation. (Guo)
 Diagnostics for sensitivity to nonignorability (Heitjan)
 Bayesian statistical methods in health economics (Heitjan)
 Bayesian analysis in pharmacogenetics (Heitjan)
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Methodology Research (Cont’d)
 Estimating subject-specific variance components from
multivariate longitudinal data (Hwang)
 Developing methods for analyzing data from a new design
(case-control follow-up studies) useful in the analysis of data
on the efficacy of cancer screening (Joffe)
 Developing appropriate assumptions for causal inference for
typical observational epidemiologic data with repeated
measures of exposure and methods of inference appropriate
for those assumptions (Joffe)
 Survival models for mapping genes for complex human
diseases, methods for admixture mapping, methods for
genetic studies of aging and longevity, methods for analysis of
high-dimensional genomic data (H. Li)
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Methodology Research (Cont’d)
 Multi-center longitudinal clinical trial simulations, using 4 to 6
random effects, typical of longitudinal study in which patients
are sampled by cluster and then followed over time (Localio)
{using existing PC-based hardware would take 2 to 3 years to
complete a single simulation}
 Estimating the cost-effectiveness of cancer therapies using
propensity score methodology (Mitra)
 Estimating the sensitivity of the hazard ratio to nonignorable
treatment assignment in non-randomized studies (Mitra)
 Evaluating the impact of individual haplotypes on disease in
molecular epidemiology studies (Mitra)
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Methodology Research (Cont’d)
 High dimensional genetic data normalization (Putt)
 Impact of misspecifying multi-level correlation structures
(Shults)
 Design and analysis of randomized trial designs to account for
treatment non-adherence and patient and provider preference;
causal modeling for understanding the mechanisms
(mediators) of treatment effects; latent class growth curve
models for identifying sub-groups of populations for which
interventions are effective (Ten Have)
 Extensions of frailty models for quality of life data (Troxel)
 Sensitivity to nonignorably missing data (Troxel)
 Survival analysis simulations with measurement error (S. Xie)
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Who are the Students?
Multi-disciplinary backgrounds:
 Preventive medicine
 Psychology
 Clinical epidemiology
 Biochemistry & cell biology
 Microbiology
 Epidemiology (genetics)
 Immunology
 Electrical engineering
 Biology
 Mechanical engineering &
management
 Pharmacology
 Mathematics
 Statistics
 Computer and information
sciences
Reflects recognition that biostatistics is
fundamentally a multi-disciplinary field
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Cohort #1 – 2000-01
 J. Mark Donovan
MS (Statistics), Northwestern University, 1990
 Long Long Gao
MS (Clinical Epidemiology), University of Pennsylvania, 2000
 Heping Hu
MHS (Epidemiology), Johns Hopkins University, 2000
MS (Immunology), Peking Union Medical College, 1992
 Clara Kim
MS (Statistics), University of California at Davis, 2000
MA (Applied Statistics), Yonsei University, 1998
 Li Qin
MS (Statistics), Texas Tech University, 2000
 Yuehui Wu
MS (Applied Statistics), Worcester Polytechnic Institute, 2000
 Jing Zhao
ME (Management Information Systems), Tsinghua University, 1998
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Cohort #2 – 2001-02
 Laurel Bastone
MS (Biostatistics), Columbia University, 2001
 Benjamin Leiby
BA (Mathematics), Messiah College, 1998
 Julia Lin
BS (Psychology and Statistics), Carnegie Mellon University, 2000
 Gui-shuang Ying
MS (Biostatistics), University of Michigan, 2000
MPH (Toxicology), Zhejiang Medical University, 1996
 Jiameng Zhang
MS (Biostatistics), University of Vermont, 2001
MS (Neurology), Shanghai Second Medical School, 1999
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Cohort #3 – 2002-03
 Jing Cheng
MS (Nutrition), Cornell University, 2002
 Carin Kim
MS (Biostatistics), Columbia University, 2002
MS (Biochemistry and Biophysics), Rensselaer Polytechnic Institute, 1998
 Robert Krafty
MA (Mathematics), University of Pennsylvania, 2002
 Robin Mogg
MS (Statistics), University of Wisconsin, 2000
 Lingfeng Yang
MS (Biostatistics), University of Minnesota, 2002
 Huaqing Zhao
MA (Applied Statistics), University of Pittsburgh, 1993
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Cohort #4 – 2003-04
 Mengye Guo
BS (Mathematics), Peking University, 2003
 Tao Liu
MS (Statistics), Iowa State University, 2002
MS (Civil Engineering), Iowa State University, 2001
 Roger Mansson
MS (Mathematical Statistics), Lund University, Sweden, 2003
 John Palcza
BS (Pharmacology/Toxicology), University of the Sciences, 2003
 Wenguang Sun
BS (Statistics), Peking University, 2003
 Ye Zhong
MS (Epidemiology and Statistics), Fudan University, 2001
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Cohort #5 – 2004-05
 Bing Cai
MS (Biostatistics), McGill University (Canada), 1999
MS (Virology), Wuhan University (China), 1989
 Shoshana Daniel
MS (Biostatistics), Columbia University, 2004
 Angelo Elmi
BS (Mathematics and Economics), State University of NY, Albany, 2003
 Ziyue Liu
MS (Biomathematics), North Carolina State University, 2004
Master (Medicine), Sun Yat-Sen University, 1997
 Valerie Teal
MS (Material Sciences & Engineering), Massachusetts Inst. of Tech., 1984
 Peter Wahl
MLA (Liberal Arts), University of Pennsylvania, 2004
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Cohort #5 (Cont’d)
 Sumedha Chhatre
PhD (Urban Planning), University of Louisville, 2000
MS (International Development), University of Pennsylvania, 1993
 Joel Greshock
MS (Biology), Villanova University, 1998
 Rachel Hammond
MS (Mathematics), Drexel University, 2004
 Michal Magid-Slav
MS (Biotechnology), University of Pennsylvania, 2001
MS (Life Science), Weizmann Institute, 1999
 Michael Rambo
BS (Mathematics), Alabama A&M University, 2001
 Hao Wang
MS (Statistics), University of California, Davis, 2000
MS (Chemistry), Institute of Chemistry, Chinese Academy of Science, 1994
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Cohort #6 – 2005-06
 Shannon Chuai
MS (Statistics), Texas A&M University, 2002
MS (Biophysics), Institute of Biophysics, Chinese Academy of Science, 2000
 Hanjoo Kim
BS (Statistics), George Washington University, 2005
 Michelle Korenblit
BS (Mathematics/Psychology), Carnegie Mellon University, 2005
 Milena Kurtinecz
MA (Applied Statistics), York University (Toronto), 2002
 Caiyan Li
BS (Mathematics), Peking University, 2005
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Cohort #6 (Cont’d)
 Kosha Ruparel
MS (Engineering), University of Pennsylvania, 2004
 Xiaoli Shi
BS (Medicine), Peking University, 2002
 Hong Wan
MS (Biostatistics), University of Minnesota, 2004
MS (Ecology), Peking University, 2001
 Chia-Hao Wang
BS (Computer Science), Rutgers University, 2005
 Xiaoying Wu
MS (Computer Science), Drexel University, 2003
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Cohort # 7 – 2006-07
 Seunghee Baek
MS (Biological Sciences), Seoul National University, 2004
 Matthew Guerra
BS (Biology and Statistics), Pennsylvania State University, 2006
 Steffanie Halberstadt
BA (Political Science, Statistics, and Women’s Studies),St. Olaf College, 2006
 Jing He
MS (Chemistry), University of Pennsylvania, 2005
 Yimei Li
BS (Statistics), Peking University, 2006
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Cohort # 7 (Cont’d)
 Kaijun Liao
MS (Statistics), University of Delaware, 2005
 Chengcheng Liu
MS (Biostatistics), University of Minnesota, 2006
 Jichun Xie
BS (Statistics), Peking University, 2006
 Rongmei Zhang
MS (Biostatistics), University of California, Los Angeles, 2005
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Cohort # 8 – 2007-08
 Peter Dawson
BS (Mathematics) ,Washington & Lee University, 2006
 Victoria Gamerman
BA/MA (Mathematics, Statistics), Boston University, 2007
 Arwin Thomasson
BS (Statistics), Virginia Tech, 2007
 Saran Vardhanabhuti
MS (Bioinformatics), University of Pennsylvania, 2005
BS (Computer Engineering), University of Michigan, 2000
 Yubing Yao
MS (Biology), Pennsylvania State University, 2005
BS (Biology), Nanjing University (China), 2002
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Biostatistics MS Graduates
Name
Year Current Employment
Paula Martin
2002 AstraZeneca
Jeffrey Botbyl
2003 GlaxoSmithKline
Shane Raines
2003 AstraZeneca
Shu-Wen Yang
2003 Current position unknown
John Palcza
2005 Merck
Mengye Guo
2005 Continuing, Penn Biostatistics PhD
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Biostatistics MS Graduates
Name
Year Current Employment
Wenguang Sun
2005 Continuing, Penn Biostatistics PhD
Ye Zhong
2005 Albert Einstein College of Medicine
Rachel Hammond
2006 Center for Clinical Epidemiology
and Biostatistics (CCEB), Penn
Roger Mansson
2006 Current position unknown
Valerie Teal
2006 Center for Clinical Epidemiology
and Biostatistics (CCEB), Penn
Peter Wahl
2006 Healthcore, Inc.
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Biostatistics MS Graduates
Name
Year Current Employment
Huaqing Zhao
2006 Children’s Hospital of Philadelphia
Angelo Elmi
2007 Continuing, Penn Biostatistics PhD
Michelle Korenblit
2007 Towers Perrin
Caiyan Li
2007 Continuing, Penn Biostatistics PhD
Xiaoli Shi
2007 Gilead
Chia-Hao Wang
2007 Continuing, Penn Biostatistics PhD
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Biostatistics PhD Graduates
Name
Year
Heping Hu
2004
Industry
Merck
Li Qin
2004
Academia
University of Washington
Gui-shuang Ying
2004
Academia
University of Pennsylvania,
Dept. of Ophthalmology
Jiameng Zhang
2004
Industry
Genentech
Yuehui Wu
2004
Industry
GlaxoSmithKline
Jing Zhao
2004
Industry
Merck
CCEB
Position
Type
Current Employment
© 2008 – 2009 University of Pennsylvania School of Medicine
Biostatistics PhD Graduates
Name
Year
Position
Type
Clara Kim
2005
Government U.S. FDA
Jing Cheng
2006
Academia
University of Florida
J. Mark Donovan 2006
Industry
Bristol-Meyers Squibb
Benjamin Leiby
2006
Academia
Thomas Jefferson
University
Julia Lin
2006
Academia
Cambridge Health
Alliance
Tao Liu
2006
Academia
Brown University
CCEB
Current Employment
© 2008 – 2009 University of Pennsylvania School of Medicine
Biostatistics PhD Graduates
Name
Year
Position
Type
Current Employment
Laurel Bastone 2007
Industry
Bristol-Myers Squibb
Long Long Gao 2007
Industry
Centocor
Robert Krafty
2007
Academia
University of Pittsburgh
Lingfeng Yang
2007
Industry
Wyeth
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Outline: Developing Biostatistics at Penn
 History
 Organizational issues
 Faculty recruitment and retention
 Launching and sustaining a nationally competitive graduate
(PhD, MS) training program
 Promoting effective balance between collaborative and
methodological research
 Recruiting and retaining excellent biostatistical
analyst/programmer, data management and project
management research staff
 Promoting and deploying a leading-edge research IT
infrastructure
 Deploying biomedical informatics methods and tools, within a
rapidly changing research landscape
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
CCEB Service Centers
 Biostatistical Analysis Center (BAC)
• Provides consultation services involving design and analysis support
for School of Medicine investigators.
• Provides biostatistical support (statistical programming and analyses)
for both short-term and ongoing collaborative research projects.
 Clinical Research Computing Unit (CRCU)
• Clinical trials coordination, clinical data management services and
research computing support for sponsored research projects
throughout Penn Medicine
• Provides a progressive computing environment for the faculty and staff
of the Biostatistics Unit and the CRCU within the Center for Clinical
Epidemiology and Biostatistics (CCEB)
• Provides an academic computing environment for the biostatistics
graduate program
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Functional Units
• Project Operations and Compliance
• Project Management
• Research Network Management
• Regulatory Expertise
• Clinical Data Management
•
•
•
•
CCEB
Case Report Form Design Expertise
Data Management Process Development
Data Quality Management
Data Entry Services
© 2008 – 2009 University of Pennsylvania School of Medicine
Functional Units
• Research Technology
• Database Design & Administration
• Data Management System Development
• Software Design
• Biomedical Research Computing
• Computational & Database Servers
• Storage Management
• High Performance Computing
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Satisfying Regulatory
Requirements
•
Cross functional coordination and training on
applicable guidelines and regulations
•
Filing and maintenance of investigatorinitiated INDs/IDEs
•
Assigning treatment codes and maintaining
associated confidential documentation
•
Informed consent review for compliance with
ICH and HIPAA requirements
•
Safety reporting to regulatory authorities
(U.S. and international)
•
Project start-up regulatory consultation
•
Regulatory resource for U of Penn
investigators
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Managing Complex
Research Networks
• Network Development
• Identify Collaborating Members
• Establish Communication Protocols
• Coordinate Collaboration Activities
• Facilitate Results Dissemination
• Site Management
• Develop Regulatory Documentation
• Facilitate Protocol Training
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Example Clinical
Research Network
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Data Management
System Development
•
Secure, Reliable, & Available Data
•
21 CFR Part 11 Compliance
•
Complete Data Management Tools
•
•
•
•
•
•
Patient Recruitment Tracking
Data Entry (Double & Single)
Programmatic Data Validation
Data Editing & Electronic Audit Trails
Electronic Data Importing
Reporting
•
Web Deployed
•
Expert User Support
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Example DM System
Menu Options
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
System Security
• Firewall protection and secure storage area network
• Each account request approved by DCC project
manager
• Username and password protected
• Site-specific access limited
• Complete audit trail
• Business continuity plan
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Biomedical
Research
Computing
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Professional Computing
Environment
•
UPHS Data Center
•
3440 Market Street
•
100+ servers/devices
•
150+ network connections
•
55 2Gb-fibre channel high
speed storage connections
•
Unix, Solaris, Linux, Windows
OS
•
Oracle Databases
•
16+TB storage
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Penn’s Progress toward a
Research Computing Facility
 Formation
a Hybrid
RCF
CRCU, of
ACC,
BMIF, CVI,
PGI, ITMAT,CEET, CFAR, etc.
Units
High
PerformanceClinical
Computing,
Databases,
LIMS,
Basic
Laboratory
Research
Basic
Science
Clinical Apps,
Statistical Genetics
Units/Applications
Units/Applications
Units/Applications
“Unit-Specific Applications”
RCF
User Authentication
viaMultiple
Federated/Centralized
services
Designed
for
organizations,
Active
Directory,
LDAP,
DNS,
Proxy,
Portals,
Security,
Privacy,
Compliance
Reporting
&based
Single
data instances
with
secured
access
on
Coupled
w/concepts,
Data
Layer
2,
Defense-in-depth
IPv6,
IMonitoring
Meta-Directory,
Asset
tracking,
Incident,
Data Classification
levels,
ePHI protections
and
Virtual
Private
Networks
(VPN),
System
usage,
Monitoring,
and
Reporting.
Reporting/Monitoring,
Backups/Archives,
Snapshots,
“Identity
Management”
Remote/Secured
Access,
Network
Address
Convergence
& Optimization
of issues
Project
Roles,
Groups,
ACLS,
& eDiscovery
Translations
(NAT),
&
Centralized
Network
“Infrastructure
Operations Hardware/Software”
and Compliance
Standards, Monitoring, & Reporting
“Data/Storage”
HVAC, Power, Physical Space, & Physical Security
“Networks”
“Data Center Facilities”
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Clinical
Research
Informatics
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Clinical Research
Informatics (CRI)
• Successful conduct of clinical and translational
science requires integration of biomedical and clinical
research informatics
• Methods and data systems
• Tools and IT systems
• Fully integrated, enterprise-wide informatics highway
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Clinical Research
Informatics (CRI)
•
CRCU is developing facilities, networks, hardware, & software
infrastructures to support CRI
•
CRCU is collaborating with CTSA principals to promote data
governance
•
CRCU is partnering with School of Medicine to pilot clinical
trials management using Oracle Pharmaceutical Applications
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Oracle Pharmaceutical
Applications
CRCU offers integrated research solutions through CTSA -
Clinical Trials Management System (Siteminder)
Remote Data Capture (RDC)
Clinical Data
Management System
(Oracle Clinical)
Adverse Event Reporting/
Pharmacovigilance
(Oracle AERS)
Term Classification / Dictionary Management (TMS)
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Why Oracle Clinical?
•
Oracle Corporation provides Oracle Clinical as an already
validated system, consistent with CFR Part 11 standards.
•
Oracle Clinical will provide standardization for use among
replicated studies.
•
Oracle Clinical is specifically designed for use in clinical trials.
•
Oracle Clinical manages clinical data and provides a
revolutionary way to offer Electronic Data Capture
(EDC). EDC speeds clinical trial data management by allowing
real-time data collection and batch validation for investigator
sites with Internet access.
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Oracle Pharmaceutical
Applications
•
Oracle Clinical (OC): a comprehensive clinical data management
solution, allowing standardization and control of data definitions and
data usage across a large-scale clinical research enterprise, ensuring
that data elements are defined, managed, and interpreted
consistently
•
SiteMinder for managing patient scheduling, visits, and budgeting
•
Remote Data Capture (RDC) for entering and managing data from
the investigative site
•
Thesaurus Management System (TMS) for classifying terms
against medical dictionaries
•
Adverse Event Reporting System (AERS) for managing patient
safety and regulatory reporting
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
ORACLE Clinical
RDC Screen
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Oracle Clinical
Data Entry Screen
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Standards Development & Adoption
 Downloaded NCI-sponsored OC Global Library, developed via
the caBIG program, into Penn’s CRCU OC environment
 Developed series of new Case Report Forms (CRFs), utilizing
Common Data Elements (CDEs) from the OC Global Library
(if already present), for each of 6 successive pilot projects,
spanning content areas of
• endocrinology
• infectious diseases, immunology
• Cardiology, hematology
 Inserted newly developed CDEs into Penn’s OC Global
Library for re-use in subsequent CRFs
 Beginning with Project #2, all CDEs developed using CDISC
standards for variable names/formats (http://www.cdisc.org/)
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Projects #1 – #6: OC Pilots
Project #7: OC MultiCenter (>50 sites) RCT
No. of Case Report Forms (CRFs)
& No. of Common Data Elements
(CDEs) (in parentheses)
PI
Clinical Content Area
Developed
New
Re-used from
Global Library
Development
Hours
Pilot Projects:
1
Snyder, PJ
Endocrinology
16 (138)
0 (0)
638
2
Rader, D
Cardiology, Hematology
17 (136)
1 (12)
272
3
Dunbar, SB
Cardiology, Hematology
21 (351)
0 (0)
218
4
June, C.
Infectious Diseases, Immunology
18 (210)
2 (23)
402
5
FitzGerald, G Cardiology, Hematology
10 (85)
10 (102)
134
6
Reilly, M
3 (15)
20 (173)
116
2 (22)
396
860
Cardiology, Hematology
Sponsored Projects:
7
Maguire, M
CCEB
Ophthalmology: CRFs/CDEs
24 (378)
: Web Landing Pad, Reports ,Utilities, Docs
© 2008 – 2009 University of Pennsylvania School of Medicine
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Efficiencies Gained / Reflections
 Reduced development time with each successive trial
 Increase in size and diversity (clinical content) of global
CRF library and content area of CDE’s
 Alignment with CDISC data standards
 This BAA “Re-engineering CRNs” Roadmap Program has
served as incubator permitting Penn Medicine to develop
some of the critical and fundamental perspectives and
technologies being advanced further within CTSA
 Our special thanks to NCRR for their vision and
support!!
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Overarching Strategic Goals
1. Center for BioMedical Informatics
• Create Center for BioMedical Informatics (CBMI) and
recruit Director / Vice Dean for academic and research
programs (as reviewed by Brian during last mtg.)
2. Strategic infrastructure development
• Develop infrastructure for Penn Medicine (UPHS, SOM)
Informatics and IT, in parallel w/ CHOP, and compatible
w/ national CTSA vision for data standards,
interoperability and institutional data sharing
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
© 2008 University of Pennsylvania School of Medicine
Oracle Pharmaceutical
Applications in a CTSA World
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine
Outline: Developing Biostatistics at Penn
 Major challenges
• Cultivating a new generation of biostatistical scientists
with the technical breadth, as well as the leadership
skills, to guide multidisciplinary research teams within
the evolving clinical and translational science award
(CTSA) paradigm of NIH Roadmap research
• Pursuing new partnership approaches with industry for
graduate education/training that includes collaborative
approaches to scientific inquiry
• Promoting multidisciplinary teams (industry, academia)
to harvest the research potentials of enterprise-wide
healthcare system practice data
CCEB
© 2008 – 2009 University of Pennsylvania School of Medicine