© 2008 University of Pennsylvania School of Medicine Biostatistics Program at Penn J. Richard Landis, PhD, Professor and Director Division of Biostatistics/Biostatistics

© 2008 University of Pennsylvania School of Medicine

Biostatistics Program at PennBiostatistics Program at Penn

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 MeetingWyeth Conference Center

Wyeth Collegeville Campus

June 10, 2008

Challenges of the Past … Visions for the Future

© 2008 – 2009 University of Pennsylvania School of MedicineCCEB

Outline: Developing Biostatistics at PennOutline: 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



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



Historical PerspectivesHistorical 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


National Context - BiostatisticsNational Context - Biostatistics

Birth of academic biostatistics departmentsJohns 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 ~ 1993Dept. Biostats & Epid. ~ 1995


Why Not U of Penn until 1995?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, large-scale, general-purpose digital computer

Natural home?• No School of Public Health• Where in the School of Medicine?

© 2008 – 2009 University of Pennsylvania School of MedicineCCEB © 2008 – 2009 University of Pennsylvania School of Medicine

Early Developments at PennEarly 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 –



Historical PerspectivesHistorical Perspectives Organizational issuesOrganizational issues Faculty recruitment and retention Launching and sustaining a nationally competitive graduate







Organizational Placement IssuesOrganizational Placement Issues

Separate, Centralized Unit• perceived equal access by

other departments• peer professional discipline

identity in biostatistics• specialized methods

expertise sharing• facilitates academic program

development• facilitates professional staff

recruitment / retention

Sub-unit within clinical or basic science department• perceived increased

access/integration in content area of “home” unit

• facilitates specialized content (cancer, AIDS, cardiovascular, neurosciences, etc.) expertise

• facilitates identity of biostatistician within larger clinical discipline


Centralized, but with Specialty CoresCentralized, but with Specialty Cores

Separate, Centralized Unit• Core faculty office space• Core administrative /

business resources• Core statistical analysts /

programmers• Core computing resources

Cores within Biostatistics Unit• Cancer• CFAR (HIV / AIDS)• Women’s Health (OB / GYN)• Cardiovascular• Neurodegenerative Diseases• Psychiatry• Pediatrics• Genomics / Genetics

CCEB

Biostatistics at Penn http://www.cceb.upenn.edu

Biostatistics at Penn http://www.cceb.upenn.edu




HistoryHistory Organizational issuesOrganizational issues Faculty recruitment and retentionFaculty recruitment and retention Launching and sustaining a nationally competitive graduate







Who are the Biostatistics Faculty?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 Harvard Johns HopkinsMacquarie U Old Dominion Penn StateUCLA U Chicago U ConnU Michigan UNC-Chapel Hill U Wash-SeattleGeo. Wash. U Emory U


Faculty Expansion: Cumulative No. (incld. expected) by Track & YearFaculty Expansion: Cumulative No. (incld. expected) by Track & Year

Year Total 1989 – `92 1 1993 – `95 2 1996 4 1997 8 1998 12 1999 15 2000 15 2001 17 2002 18 2003 20 2004 22 2006 27 2007 ‡ 28‡ Tenured 7; tenure track: 1 ;

CE track: 20


Areas of Faculty ExpertiseAreas of Faculty Expertise

Bayesian modeling Categorical data Causal inference Clinical trials Clustered data Complex sample surveys Cost-benefit analyses Cross-over trials Functional genomics Functional predictive modeling Genetic/genomic modeling Health Economics Health services research

Longitudinal methods Measurement error models Meta-analysis Missing data Multiple imputation Multivariate analysis Repeated measures Spatial analyses Statistical genetics/bioinformatics Survey sampling Survival analysis Time series


Major Areas of Faculty CollaborationsMajor Areas of Faculty Collaborations

Aging

Bioinformatics

Cancer

Clinical epidemiology

Clinical trials

Disparities research

Health services research

HIV/AIDS

Medical imaging Neurodegenerative diseases Pharmacoepidemiology Psychiatry Psychometrics Statistical

genetics/genomics Urology/Renal Women’s Health


Faculty Recruitment Goals2007 – 2012 (Target N = 36)(Current TT/8, CE/20; N=28)

Faculty Recruitment Goals2007 – 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


Biostatistics FacultyBiostatistics Faculty

Bellamy, Scarlett (2001) Assistant ProfessorScD (Biostatistics), Harvard, 2001; ScM (Biostatistics), Harvard, 1997

Bilker, Warren B. (1992) ProfessorPhD (Biostatistics), Johns Hopkins, 1992; MS (Statistics), Temple, 1984

Boston, Raymond C. (1996) ProfessorPhD (Physics), Univ. of of Melbourne, Australia, 1970; MS (Physiology), Univ. of Melbourne, Australia, 1967

Chen, Jinbo (2006) Assistant ProfessorPhD (Biostatistics), Univ. of Washington, Seattle, 2002; MS (Biostatistics), Univ. of Washington, 1999



Chen, Zhen (2003) Assistant ProfessorPhD (Statistics), Univ. of Connecticut 2001

Ellenberg, Jonas H. (2004) ProfessorPhD (Mathematical Statistics), Harvard, 1970; AM (Mathematical Statistics), Harvard, 1964

Ellenberg, Susan S. (2004) ProfessorPhD (Mathematical Statistics), George Washington Univ., 1980

Gimotty, Phyllis A. (1998) ProfessorPhD (Biostatistics), Univ. of Michigan, 1984; MS (Statistics), Univ. of Michigan, 1972



Guo, Wensheng (1998) Associate Professor PhD (Biostatistics), Univ. of Michigan, 1998; MS (Biostatistics), Univ. of Colorado, 1994

Heitjan, Daniel F. (2002) ProfessorPhD (Statistics), Univ. of Chicago, 1985; MS (Statistics), Univ. of Chicago, 1984

Hwang, Wei-Ting (2001) Assistant ProfessorPhD (Biostatistics), Johns Hopkins Univ., 2001

Joffe, Marshall M. (1996) Associate ProfessorPhD (Epidemiology), Univ. of California, Los Angeles, 1994; MD, Univ. of Maryland, 1988; MPH (Biostatistics), Harvard, 1989



Landis, J. Richard (1997) ProfessorPhD (Biostatistics), Univ. of North Carolina, Chapel Hill, 1975; MS (Biostatistics), Univ. of North Carolina, Chapel Hill, 1973

Li, Hongzhe (2004) ProfessorPhD (Statistics), Univ. of Washington, Seattle, 1995; MA (Mathematics), Univ. of Montana, Missoula, 1991

Li, Mingyao (2006) Assistant ProfessorPhD (Biostatistics), Univ. of Michigan, 2005; MS (Mathematics), Nankai Univ., 1999

Localio, A. Russell (1997) Associate ProfessorPhD (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



Mitra, Nandita (2005) Assistant ProfessorPhD (Biostatistics), Columbia Univ., 2001; MS (Biostatistics), Univ. of California, Berkeley, 1996

Moore, Reneé H. (2006) Assistant ProfessorPhD (Biostatistics), Emory Univ., 2006; MS (Biostatistics), Emory Univ., 2005; BS (Mathematics), Bennett College, 1999

Morales, Knashawn H. (2006) Assistant ProfessorScD (Biostatistics), Harvard, 2001; ScM (Biostatistics), Harvard, 1997

Propert, Kathleen Joy (1996) ProfessorScD (Biostatistics), Harvard, 1990; MS (Biostatistics) Harvard, 1984



Putt, Mary E. (1999) Assistant ProfessorScD (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



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 ProfessorPhD (Biostatistics), Univ. of Michigan, 2004; MA (Mathematical Statistics), Bowling Green State Univ., 1999

Xie, Sharon Xiangwen (2002) Assistant ProfessorPhD (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


Standard NIH Demographic Report: Faculty, Division of BiostatisticsStandard NIH Demographic Report: Faculty, Division of Biostatistics

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.

Study Title:

Penn Biostatistics FacultyProfile – October, 2006

GenderAmerican Indian/

Alaska NativeAsian

Native Hawaiian/Other Pacific

Islander

Black/ AfricanAmerican

White Total

Female 5 4 817

(70.0)

Male 3 0 710

(30.0)

TOTAL8

(29.6)4

(14.8)15

(55.6)27

(100.0)


Distribution of Gender (Percent Female) by Rank and TrackDistribution of Gender (Percent Female) by Rank and Track

Gender

Assistant Professor Associate Professor Professor

Total

Tenure CE Tenure CE Tenure CE

Female2

(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


Percent Female by Rank & TrackPercent Female by Rank & Track


Dist’n of Race by Rank, Gender and Track: Biostatistics FacultyDist’n of Race by Rank, Gender and Track: Biostatistics Faculty

Rank Gender TrackAmerican

Indian/Alaska Native

Asian

Native Hawaiian/

Other PacificIslander

Black/ AfricanAmerican

White Total

Professor

FemaleTenure 0

CE 1 1

MaleTenure 1 3 4

CE 1 1

AssociateProfessor

FemaleTenure 0

CE 4 4

MaleTenure 1 1 2

CE 2 2

Assistant Professor

FemaleTenure 1 1 2

CE 4 3 3 10

MaleTenure 0

CE 1 1

Total8

(29.6)4

(14.8)15

(55.6)27

(100.0)



HistoryHistory Organizational issuesOrganizational issues Faculty recruitment and retentionFaculty recruitment and retention Launching and sustaining a nationally competitive graduate Launching and sustaining a nationally competitive graduate

(PhD, MS) training program (2000 - (PhD, MS) training program (2000 - Promoting effective balance between collaborative and






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

Biostatistics Educational ProgramsBiostatistics Educational Programs


Degree Programs (MS, PhD)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


Semester 1ST Year Curriculum: Required Course (Credit)Required – non-credit

FALLBSTA 620: Probability I (1.0)BSTA 630: Statistical Methods and Data Analysis I (1.0) (Lecture and Lab)BSTA 509: Introductory Epidemiology (0.5)BSTA 510: Introduction to Human Health and Diseases (0.5)

HIPAA CertificationPOR Certification

SPRINGBSTA 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)

Ethics LecturesConsulting

1One semester of teaching required in either year 3,4, or 5.2One Advanced Elective (formal audit) or one special reading course (course credit) in any semester with approval of student’s thesis advisor.

Typical Course Sequence for Students in PhD Program (Year 01)Typical Course Sequence for Students in PhD Program (Year 01)


Semester 2ND Year Proposed Curriculum: Required (Credit)Required – non-credit

FALLBSTA 622: Statistical Inference II (1.0) BSTA 652: Categorical Data Analysis (1.0)BSTA 653: Survival Analysis (1.0)

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)

Advanced Elective

Ethics LecturesConsulting II Project

Completion of Consulting II Project/MS Thesis by deadline



Semester 3RD Year Proposed Curriculum: Required (Credit)Required – non-credit

FALLBSTA 670: Statistical Computing (1.0)Advanced ElectiveMinor

Teaching Assistantship1

SPRINGMinor Advanced ElectiveBSTA 999 Reading Course

Ethics Lectures

SUMMERThesis proposal, Oral Preliminary Examination




Semester 4TH Year Proposed Curriculum: Required (Credit)Required – non-credit

FALLBSTA 999 Reading Course (3 course units) orBSTA 920 Dissertation Research (3 course unit)2

Teaching Assistantship1

SPRINGBSTA 920 Dissertation Research (3 course units)2 Ethics Lectures


Semester 5th Year Proposed Curriculum: Required (Credit)Required – non-credit

FALLBSTA 920 Dissertation Research (3 course units)2 Teaching

Assistantship1

SPRINGBSTA 920 Dissertation Research (3 course units)2 Ethics Lectures

Typical Course Sequence for Students in PhD Program (Year 04, 05)Typical Course Sequence for Students in PhD Program (Year 04, 05)


Proposal -- Center for Biostatistics Methods ResearchProposal -- Center for Biostatistics Methods Research

New FacultyUse University Professorship (SOM, Wharton, SAS, SEAS) & Fairhill Chair to attract senior “Methods” leader

5+ tenure track faculty recruitments

FocusClinical and translational science (CTSA) – e.g., metabolism modeling, pharmacogenomic modeling

Causal inference / modeling

Measurement (tools and scale development / evaluation)

Statistical genetics

Pharmacoepidemiology

Clinical trial designs / methodsPharmacoeconomics




(PhD, MS) training program(PhD, MS) training program Promoting effective balance between collaborative and Promoting effective balance between collaborative and

methodological researchmethodological research Recruiting and retaining excellent biostatistical





Research Challenges: The MixResearch Challenges: The Mix

0.00000

0.20000

0.40000

0.60000

0.80000

1.00000

1.20000

0% 20% 40% 60% 80% 100% 120%

% Collaboration

Local Minimum

Cu

mu

lati

ve

Pe

rce

nt

* Approximate, pending not included

(55% methods)


Target for Mix over next six years?Target for Mix over next six years?

10% Methods, 90% Collaborative


30% Methods, 70% Collaborative?




Imperative Considerations for Transforming the Mix 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?


Faculty Involvement in Long Term, Large Collaborative Efforts with Coordinating Center Involvement?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


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)

Faculty Research Areas of CollaborationFaculty Research Areas of Collaboration


Faculty Leadership of Data Coordinating Centers (DCCs)Faculty Leadership of Data Coordinating Centers (DCCs)

Multi-center Clinical Research Networks Faculty Leadership

CRIC NIDDK: Renal• 13 sites; cohort/subcohort

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• 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

[Westst], J. Ellenberg


Faculty Leadership of CoresFaculty Leadership of CoresCORES 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 Reproductive Health Research Driscoll/Sammel


61% injury reduction: belt-positioning boosters vs. seat belts ….. JAMA, 2003

Child in booster

Child in belt without booster

Partners for Child Passenger SafetyMechanism of injuryPartners for Child Passenger SafetyMechanism of injury


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)

Case StudiesCase Studies


Cost-effectiveness of Pharmacogenetic Testing to Tailor Smoking Cessation TreatmentHeitjan 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

Case StudiesCase Studies


Copy Number Variation (CNV) and Human DiseasesCopy 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.

Case Studies (Cont’d)Case Studies (Cont’d)


Genomics and Informatics CoreGenomics 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, multi-site 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.


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)

Methodology ResearchMethodology Research


Methodology Research (Cont’d)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)


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)

Methodology Research (Cont’d)Methodology Research (Cont’d)


Methodology Research (Cont’d)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)


Who are the Students?Who are the Students?

Psychology Biochemistry & cell biology Epidemiology (genetics) Electrical engineering Mechanical engineering &

management Pharmacology

Multi-disciplinary backgrounds:

Reflects recognition that biostatistics is fundamentally a multi-disciplinary field

Preventive medicine Clinical epidemiology Microbiology Immunology Biology Mathematics Statistics Computer and information

sciences


J. Mark Donovan MS (Statistics), Northwestern University, 1990

Long Long GaoMS (Clinical Epidemiology), University of Pennsylvania, 2000

Heping HuMHS (Epidemiology), Johns Hopkins University, 2000

MS (Immunology), Peking Union Medical College, 1992

Clara KimMS (Statistics), University of California at Davis, 2000

MA (Applied Statistics), Yonsei University, 1998

Li QinMS (Statistics), Texas Tech University, 2000

Yuehui WuMS (Applied Statistics), Worcester Polytechnic Institute, 2000

Jing ZhaoME (Management Information Systems), Tsinghua University, 1998

Cohort #1 – 2000-01Cohort #1 – 2000-01


Laurel BastoneMS (Biostatistics), Columbia University, 2001

Benjamin LeibyBA (Mathematics), Messiah College, 1998

Julia LinBS (Psychology and Statistics), Carnegie Mellon University, 2000

Gui-shuang YingMS (Biostatistics), University of Michigan, 2000MPH (Toxicology), Zhejiang Medical University, 1996

Jiameng ZhangMS (Biostatistics), University of Vermont, 2001MS (Neurology), Shanghai Second Medical School, 1999

Cohort #2 – 2001-02Cohort #2 – 2001-02


Jing ChengMS (Nutrition), Cornell University, 2002

Carin KimMS (Biostatistics), Columbia University, 2002MS (Biochemistry and Biophysics), Rensselaer Polytechnic Institute, 1998

Robert KraftyMA (Mathematics), University of Pennsylvania, 2002

Robin MoggMS (Statistics), University of Wisconsin, 2000

Lingfeng YangMS (Biostatistics), University of Minnesota, 2002

Huaqing ZhaoMA (Applied Statistics), University of Pittsburgh, 1993

Cohort #3 – 2002-03Cohort #3 – 2002-03


Mengye GuoBS (Mathematics), Peking University, 2003

Tao LiuMS (Statistics), Iowa State University, 2002MS (Civil Engineering), Iowa State University, 2001

Roger ManssonMS (Mathematical Statistics), Lund University, Sweden, 2003

John Palcza BS (Pharmacology/Toxicology), University of the Sciences, 2003

Wenguang SunBS (Statistics), Peking University, 2003

Ye ZhongMS (Epidemiology and Statistics), Fudan University, 2001

Cohort #4 – 2003-04Cohort #4 – 2003-04


Bing CaiMS (Biostatistics), McGill University (Canada), 1999MS (Virology), Wuhan University (China), 1989

Shoshana DanielMS (Biostatistics), Columbia University, 2004

Angelo ElmiBS (Mathematics and Economics), State University of NY, Albany, 2003

Ziyue LiuMS (Biomathematics), North Carolina State University, 2004Master (Medicine), Sun Yat-Sen University, 1997

Valerie TealMS (Material Sciences & Engineering), Massachusetts Inst. of Tech., 1984

Peter WahlMLA (Liberal Arts), University of Pennsylvania, 2004

Cohort #5 – 2004-05Cohort #5 – 2004-05


Sumedha ChhatrePhD (Urban Planning), University of Louisville, 2000MS (International Development), University of Pennsylvania, 1993

Joel GreshockMS (Biology), Villanova University, 1998

Rachel HammondMS (Mathematics), Drexel University, 2004

Michal Magid-SlavMS (Biotechnology), University of Pennsylvania, 2001MS (Life Science), Weizmann Institute, 1999

Michael RamboBS (Mathematics), Alabama A&M University, 2001

Hao WangMS (Statistics), University of California, Davis, 2000MS (Chemistry), Institute of Chemistry, Chinese Academy of Science, 1994

Cohort #5 (Cont’d)Cohort #5 (Cont’d)


Cohort #6 – 2005-06Cohort #6 – 2005-06

Shannon ChuaiMS (Statistics), Texas A&M University, 2002MS (Biophysics), Institute of Biophysics, Chinese Academy of Science, 2000

Hanjoo KimBS (Statistics), George Washington University, 2005

Michelle KorenblitBS (Mathematics/Psychology), Carnegie Mellon University, 2005

Milena KurtineczMA (Applied Statistics), York University (Toronto), 2002

Caiyan LiBS (Mathematics), Peking University, 2005


Cohort #6 (Cont’d)Cohort #6 (Cont’d)

Kosha RuparelMS (Engineering), University of Pennsylvania, 2004

Xiaoli ShiBS (Medicine), Peking University, 2002

Hong WanMS (Biostatistics), University of Minnesota, 2004

MS (Ecology), Peking University, 2001

Chia-Hao WangBS (Computer Science), Rutgers University, 2005

Xiaoying WuMS (Computer Science), Drexel University, 2003


Cohort # 7 – 2006-07Cohort # 7 – 2006-07

Seunghee BaekMS (Biological Sciences), Seoul National University, 2004

Matthew GuerraBS (Biology and Statistics), Pennsylvania State University, 2006

Steffanie HalberstadtBA (Political Science, Statistics, and Women’s Studies),St. Olaf College, 2006

Jing HeMS (Chemistry), University of Pennsylvania, 2005

Yimei LiBS (Statistics), Peking University, 2006


Cohort # 7 (Cont’d)Cohort # 7 (Cont’d)

Kaijun LiaoMS (Statistics), University of Delaware, 2005

Chengcheng LiuMS (Biostatistics), University of Minnesota, 2006

Jichun XieBS (Statistics), Peking University, 2006

Rongmei ZhangMS (Biostatistics), University of California, Los Angeles, 2005


Cohort # 8 – 2007-08Cohort # 8 – 2007-08

Peter DawsonBS (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


Biostatistics MS GraduatesBiostatistics MS GraduatesName 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



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.



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


Biostatistics PhD GraduatesBiostatistics PhD Graduates

Name Year Position Type

Current Employment

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


Biostatistics PhD GraduatesBiostatistics PhD GraduatesName Year Position

TypeCurrent Employment

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


Biostatistics PhD GraduatesBiostatistics PhD GraduatesName Year Position

TypeCurrent 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




(PhD, MS) training program(PhD, MS) training program Promoting effective balance between collaborative and Promoting effective balance between collaborative and

methodological researchmethodological research Recruiting and retaining excellent biostatistical Recruiting and retaining excellent biostatistical

analyst/programmer, data management and project analyst/programmer, data management and project management research staffmanagement research staff




CCEB Service CentersCCEB 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


Functional UnitsFunctional Units

• Project Operations and Compliance • Project Management• Research Network Management• Regulatory Expertise

• Clinical Data Management• Case Report Form Design Expertise• Data Management Process Development• Data Quality Management• Data Entry Services


Functional UnitsFunctional Units

• Research Technology • Database Design & Administration• Data Management System Development• Software Design

• Biomedical Research Computing• Computational & Database Servers• Storage Management• High Performance Computing


Satisfying RegulatoryRequirementsSatisfying RegulatoryRequirements• Cross functional coordination and training on

applicable guidelines and regulations

• Filing and maintenance of investigator-initiated 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


Managing ComplexResearch NetworksManaging ComplexResearch Networks

• Network Development• Identify Collaborating Members• Establish Communication Protocols• Coordinate Collaboration Activities• Facilitate Results Dissemination

• Site Management• Develop Regulatory Documentation• Facilitate Protocol Training


Example Clinical Research NetworkExample Clinical Research Network


Data ManagementSystem DevelopmentData ManagementSystem 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


Example DM SystemMenu OptionsExample DM SystemMenu Options


System SecuritySystem 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


Biomedical

Research

Computing


Professional ComputingEnvironmentProfessional ComputingEnvironment• 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


Convergence & Optimization of Operations and Compliance

HVAC, Power, Physical Space, & Physical Security

“Data Center Facilities”

Designed for Multiple organizations, Defense-in-depth concepts, IPv6, I2,

Virtual Private Networks (VPN), Remote/Secured Access, Network Address Translations (NAT), & Centralized Network

Standards, Monitoring, & Reporting

“Networks”

Penn’s Progress toward a Research Computing FacilityPenn’s Progress toward a Research Computing Facility

Formation of a Hybrid RCF

Single data instances with secured access based onData Classification levels, ePHI protections and

Reporting/Monitoring, Backups/Archives, Snapshots, Project Roles, Groups, ACLS, & eDiscovery issues

“Data/Storage”

Active Directory, LDAP, DNS, Proxy, Portals, Meta-Directory, Asset tracking, Incident, System usage, Monitoring, and Reporting.

“Infrastructure Hardware/Software”

High Performance Computing, Databases, LIMS, Clinical Apps, Statistical Genetics

“Unit-Specific Applications”

RC

F

Basic Laboratory Units/Applications

Clinical Research Units/Applications

Basic Science Units/Applications

User Authentication via Federated/Centralized servicesCoupled w/ Data Layer

“Identity Management”

Security, Privacy, Compliance Reporting & Monitoring

Units

CRCU, ACC, BMIF, CVI, PGI, ITMAT,CEET, CFAR, etc.


Clinical

Research

Informatics


Clinical Research Informatics (CRI)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


Clinical Research Informatics (CRI)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


Oracle PharmaceuticalApplicationsOracle PharmaceuticalApplications

CRCU offers integrated research solutions through CTSA -

Adverse Event Reporting/Adverse Event Reporting/PharmacovigilancePharmacovigilance

(Oracle AERS)(Oracle AERS)

Term Classification / Dictionary Management (TMS)Term Classification / Dictionary Management (TMS)

Clinical Data Clinical Data Management SystemManagement System

(Oracle Clinical)(Oracle Clinical)

Clinical Trials Management System (Siteminder)Clinical Trials Management System (Siteminder)

Remote Data Capture (RDC)Remote Data Capture (RDC)


Why Oracle Clinical?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.


Oracle PharmaceuticalApplications Oracle PharmaceuticalApplications

• 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


ORACLE Clinical RDC ScreenORACLE Clinical RDC Screen


Oracle Clinical Data Entry Screen Oracle Clinical Data Entry Screen


Standards Development & AdoptionStandards 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/)


No. of Case Report Forms (CRFs) & No. of Common Data Elements

(CDEs) (in parentheses)

DevelopmentHours

PI Clinical Content Area DevelopedNew

Re-used fromGlobal Library

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 Cardiology, Hematology 3 (15) 20 (173) 116

Sponsored Projects:

7 Maguire, M Ophthalmology: CRFs/CDEs

: Web Landing Pad, Reports

24 (378)

,Utilities, Docs

2 (22) 396

860

Projects #1 – #6: OC Pilots Project #7: OC MultiCenter (>50 sites) RCTProjects #1 – #6: OC Pilots Project #7: OC MultiCenter (>50 sites) RCT



Efficiencies Gained / ReflectionsEfficiencies 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!!


Overarching Strategic GoalsOverarching 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


© 2008 University of Pennsylvania School of Medicine


Oracle Pharmaceutical Applications in a CTSA WorldOracle Pharmaceutical Applications in a CTSA World



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

Documents

© 2008 University of Pennsylvania School of Medicine Biostatistics Program at Penn J. Richard Landis, PhD, Professor and Director Division of Biostatistics/Biostatistics