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

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Presentation on theme: "© 2008 University of Pennsylvania School of Medicine Biostatistics Program at Penn J. Richard Landis, PhD, Professor and Director Division of Biostatistics/Biostatistics."— Presentation transcript:

1 © 2008 University of Pennsylvania School of Medicine Biostatistics 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 Presented at the ASA Philadelphia Spring Meeting Wyeth Conference Center Wyeth Collegeville Campus June 10, 2008 Challenges of the Past … Visions for the Future

2 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

3 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

4 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Outline: Developing Biostatistics at Penn  Historical Perspectives Personal experiences: institutions / mentors / roles –Millersville U ( ) student: math/statistics/computing –West Haven VA, CT ( ) statistical programmer –UNC, Chapel Hill ( ) biostatistics grad student –U Michigan, Ann Arbor ( ) professor –Penn State U, Hershey ( ) professor & director –U Penn, Phila. (1997-present) professor & director BU National context of academic departments Early phases at Penn

5 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine National Context - Biostatistics Birth of academic biostatistics departments 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 ~ 1993 Dept. Biostats & Epid. ~ 1995

6 CCEB © 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, large- scale, general-purpose digital computer  Natural home? No School of Public Health Where in the School of Medicine?

7 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 –

8 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

9 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Organizational 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

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

11 CCEB Biostatistics at Penn

12 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine

13 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

14 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: ColumbiaHarvardJohns Hopkins Macquarie UOld DominionPenn State UCLA U ChicagoU Conn U MichiganUNC-Chapel Hill U Wash-Seattle Geo. Wash. UEmory U

15 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Faculty Expansion: Cumulative No. (incld. expected) by Track & Year Year Total  1989 – `92 1  1993 – `95 2            2007 ‡ 28 ‡ Tenured 7; tenure track: 1 ; CE track: 20

16 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Areas 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

17 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Major 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

18 CCEB © 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

19 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

20 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

21 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

22 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

23 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

24 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

25 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

26 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Standard 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 Faculty Profile – October, 2006 Gender American Indian/ Alaska Native Asian Native Hawaiian/ Other Pacific Islander Black/ African American WhiteTotal Female (70.0) Male (30.0) TOTAL 8 (29.6) 4 (14.8) 15 (55.6) 27 (100.0)

27 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Distribution of Gender (Percent Female) by Rank and Track Gender Assistant ProfessorAssociate ProfessorProfessor Total TenureCETenureCETenureCE Female 2 (100.0) 10 (90.9) 0 (0.0) 4 (66.7) 0 (0.0) 1 (0.50) 17 (70.0) Male TOTAL

28 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Percent Female by Rank & Track

29 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Dist’n of Race by Rank, Gender and Track: Biostatistics Faculty RankGenderTrack American Indian/ Alaska Native Asian Native Hawaiian/ Other Pacific Islander Black/ African American WhiteTotal Professor Female Tenure0 CE11 Male Tenure134 CE11 Associate Professor Female Tenure0 CE44 Male Tenure112 CE22 Assistant Professor Female Tenure112 CE43310 Male Tenure0 CE11 Total 8 (29.6) 4 (14.8) 15 (55.6) 27 (100.0)

30 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

31 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine  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 Programs

32 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

33 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Semester 1 ST Year Curriculum: Required Course (Credit) Required – non-credit FALL BSTA 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 Certification POR Certification 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) Ethics Lectures Consulting 1 One semester of teaching required in either year 3,4, or 5. 2 One 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)

34 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Semester2 ND Year Proposed Curriculum: Required (Credit) Required – non-credit FALL BSTA 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 Lectures Consulting II Project Completion of Consulting II Project/MS Thesis by deadline Typical Course Sequence for Students in PhD Program (Year 02)

35 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Semester3 RD Year Proposed Curriculum: Required (Credit) Required – non-credit FALL BSTA 670: Statistical Computing (1.0) Advanced Elective Minor Teaching Assistantship 1 SPRING Minor Advanced Elective BSTA 999 Reading Course Ethics Lectures SUMMERThesis proposal, Oral Preliminary Examination 1 One semester of teaching required in either year 3,4, or 5. 2 One 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 03)

36 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Semester4 TH Year Proposed Curriculum: Required (Credit) Required – non-credit FALL BSTA 999 Reading Course (3 course units) or BSTA 920 Dissertation Research (3 course unit) 2 Teaching Assistantship 1 SPRING BSTA 920 Dissertation Research (3 course units) 2 Ethics Lectures 1 One semester of teaching required in either year 3,4, or 5. 2 One Advanced Elective (formal audit) or one special reading course (course credit) in any semester with approval of student’s thesis advisor. Semester5 th Year Proposed Curriculum: Required (Credit) Required – non-credit FALL BSTA 920 Dissertation Research (3 course units) 2 Teaching Assistantship 1 SPRING BSTA 920 Dissertation Research (3 course units) 2 Ethics Lectures Typical Course Sequence for Students in PhD Program (Year 04, 05)

37 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Proposal -- Center for Biostatistics Methods Research  New Faculty Use University Professorship (SOM, Wharton, SAS, SEAS) & Fairhill Chair to attract senior “Methods” leader 5+ tenure track faculty recruitments  Focus Clinical 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 / methods Pharmacoeconomics

38 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

39 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Research Challenges: The Mix Local Minimum Cumulative Percent * Approximate, pending not included (55% methods)

40 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine 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

41 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?

42 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

43 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine  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 Collaboration

44 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Faculty Leadership of Data Coordinating Centers (DCCs) Multi-center Clinical Research NetworksFaculty Leadership CRICNIDDK: Renal 13 sites; cohort/subcohort HI Feldman, JR Landis UPPCRNNIDDK: Urology ICCRN (10 sites; 2 RCTs) (Landis) CPCRN (11 sites, 2 RCTs) (Landis) JR Landis TAM/MRINCI: Cancer ChemopreventionT Rebbeck, J Ellenberg UCNIDDK: GastrointestinalLewis, J Ellenberg AACNIMH; HIV AA couplesJ Jemmott, JR Landis, SL Bellamy CATNAPNHLBI: Sleep ApneaT Weaver, S Ellenberg CHATNHLBI: Pediatric Sleep ApneaS Redline, Case Western, S Ellenberg NCSNICHD: 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

45 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Faculty Leadership of Cores CORESFaculty Leadership Alzheimer’s DiseaseTrojanowski/S. Xie CancerThompson/Heitjan/Landis; Guerry/Gimotty; Schnall/Boston Cardiovascular InstituteCappola/Putt Center for AIDS Research (CFAR)Hoxie/S Ellenberg Center of Excellence in Environmental Toxicology (CEET)Penning/Troxel Lung InjuryFisher/Lanken/Landis/Localio Mental Retardation and Developmental DisabilitiesYudkoff/Putt Parkinson’s DiseaseTrojanowski/S. Xie Photodynamic TherapyGladstein/Putt Psychiatry: SchizophreniaGur/Bilker Psychiatry: Depression in ElderlyKatz/Ten Have Psychiatry: Weight and Eating DisordersWadden/Stunkard/ Berkowitz/ Faith/Moore Women’s Reproductive Health ResearchDriscoll/Sammel

46 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine 61% injury reduction: belt-positioning boosters vs. seat belts ….. JAMA, 2003 Child in booster Child in belt without booster Partners for Child Passenger Safety Mechanism of injury

47 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine  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 Studies

48 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine  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 Case Studies

49 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine  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. Case Studies (Cont’d)

50 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, 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.

51 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine  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 Research

52 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)

53 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine  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)

54 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)

55 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine 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

56 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine  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 Cohort #1 –

57 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine  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 Cohort #2 –

58 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine  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 Cohort #3 –

59 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine  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 Cohort #4 –

60 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine  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 Cohort #5 –

61 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine  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 Cohort #5 (Cont’d)

62 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Cohort #6 –  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

63 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

64 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Cohort # 7 –  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

65 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

66 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Cohort # 8 –  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

67 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Biostatistics MS Graduates NameYearCurrent Employment Paula Martin2002AstraZeneca Jeffrey Botbyl2003GlaxoSmithKline Shane Raines2003AstraZeneca Shu-Wen Yang2003Current position unknown John Palcza2005Merck Mengye Guo2005Continuing, Penn Biostatistics PhD

68 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Biostatistics MS Graduates NameYearCurrent Employment Wenguang Sun2005Continuing, Penn Biostatistics PhD Ye Zhong2005Albert Einstein College of Medicine Rachel Hammond2006Center for Clinical Epidemiology and Biostatistics (CCEB), Penn Roger Mansson2006Current position unknown Valerie Teal2006Center for Clinical Epidemiology and Biostatistics (CCEB), Penn Peter Wahl2006Healthcore, Inc.

69 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Biostatistics MS Graduates NameYearCurrent Employment Huaqing Zhao2006Children’s Hospital of Philadelphia Angelo Elmi2007Continuing, Penn Biostatistics PhD Michelle Korenblit2007Towers Perrin Caiyan Li2007Continuing, Penn Biostatistics PhD Xiaoli Shi2007Gilead Chia-Hao Wang2007Continuing, Penn Biostatistics PhD

70 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Biostatistics PhD Graduates Name YearPosition Type Current Employment Heping Hu2004IndustryMerck Li Qin 2004AcademiaUniversity of Washington Gui-shuang Ying2004AcademiaUniversity of Pennsylvania, Dept. of Ophthalmology Jiameng Zhang 2004IndustryGenentech Yuehui Wu2004IndustryGlaxoSmithKline Jing Zhao2004IndustryMerck

71 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Biostatistics PhD Graduates NameYearPosition Type Current Employment Clara Kim2005GovernmentU.S. FDA Jing Cheng2006AcademiaUniversity of Florida J. Mark Donovan2006IndustryBristol-Meyers Squibb Benjamin Leiby2006AcademiaThomas Jefferson University Julia Lin2006AcademiaCambridge Health Alliance Tao Liu2006AcademiaBrown University

72 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Biostatistics PhD Graduates NameYearPosition Type Current Employment Laurel Bastone2007IndustryBristol-Myers Squibb Long Long Gao2007IndustryCentocor Robert Krafty2007AcademiaUniversity of Pittsburgh Lingfeng Yang2007IndustryWyeth

73 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

74 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

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

76 CCEB © 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

77 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 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

78 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

79 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Example Clinical Research Network

80 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

81 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Example DM System Menu Options

82 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

83 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Biomedical Research Computing

84 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

85 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Convergence & Optimization of Operations and Compliance HVAC, Power, Physical Space, & Physical Security “Data Center Facilities” Designed for Multiple organizations, Defense-in-depth concepts, IPv6, I 2, Virtual Private Networks (VPN), Remote/Secured Access, Network Address Translations (NAT), & Centralized Network Standards, Monitoring, & Reporting “Networks” Penn’s Progress toward a Research Computing Facility  Formation of a Hybrid RCF Single data instances with secured access based on Data 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” RCF Basic Laboratory Units/Applications Clinical Research Units/Applications Basic Science Units/Applications User Authentication via Federated/Centralized services Coupled w/ Data Layer “Identity Management” Security, Privacy, Compliance Reporting & Monitoring Units CRCU, ACC, BMIF, CVI, PGI, ITMAT,CEET, CFAR, etc.

86 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine C linical R esearch I nformatics

87 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

88 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

89 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Oracle Pharmaceutical Applications CRCU offers integrated research solutions through CTSA - Adverse Event Reporting/ Pharmacovigilance (Oracle AERS) Term Classification / Dictionary Management (TMS) Clinical Data Management System (Oracle Clinical) Clinical Trials Management System (Siteminder) Remote Data Capture (RDC)

90 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.

91 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

92 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine ORACLE Clinical RDC Screen

93 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Oracle Clinical Data Entry Screen

94 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/)http://www.cdisc.org/

95 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine No. of Case Report Forms (CRFs) & No. of Common Data Elements (CDEs) (in parentheses) Development Hours PIClinical Content AreaDeveloped New Re-used from Global Library Pilot Projects: 1 Snyder, PJEndocrinology16 (138)0 (0)638 2 Rader, DCardiology, Hematology17 (136)1 (12)272 3 Dunbar, SBCardiology, Hematology21 (351)0 (0)218 4 June, C.Infectious Diseases, Immunology18 (210)2 (23)402 5 FitzGerald, GCardiology, Hematology10 (85)10 (102)134 6 Reilly, MCardiology, Hematology3 (15)20 (173)116 Sponsored Projects: 7 Maguire, MOphthalmology: CRFs/CDEs : Web Landing Pad, Reports 24 (378),Utilities, Docs 2 (22) Projects #1 – #6: OC Pilots Project #7: OC MultiCenter (>50 sites) RCT

96 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine

97 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!!

98 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

99 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine

100 © 2008 University of Pennsylvania School of Medicine

101 CCEB © 2008 – 2009 University of Pennsylvania School of Medicine Oracle Pharmaceutical Applications in a CTSA World

102 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


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