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Biostatistics Program at Penn

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1 Biostatistics Program at Penn
Challenges of the Past … Visions for the Future J. Richard Landis, PhD, Professor and Director Division of Biostatistics/Biostatistics Unit Center for Clinical Epidemiology and Biostatistics (CCEB) University of Pennsylvania School of Medicine Philadelphia, PA Presented at the ASA Philadelphia Spring Meeting Wyeth Conference Center Wyeth Collegeville Campus June 10, 2008 © 2008 University of Pennsylvania School of Medicine

2 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 © 2008 – 2009 University of Pennsylvania School of Medicine

3 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 © 2008 – 2009 University of Pennsylvania School of Medicine

4 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 © 2008 – 2009 University of Pennsylvania School of Medicine

5 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 © 2008 – 2009 University of Pennsylvania School of Medicine

6 © 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? © 2008 – 2009 University of Pennsylvania School of Medicine

7 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 – © 2008 – 2009 University of Pennsylvania School of Medicine © 2008 – 2009 University of Pennsylvania School of Medicine 7

8 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 © 2008 – 2009 University of Pennsylvania School of Medicine

9 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 © 2008 – 2009 University of Pennsylvania School of Medicine

10 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 © 2008 – 2009 University of Pennsylvania School of Medicine

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

12 © 2008 – 2009 University of Pennsylvania School of Medicine

13 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 © 2008 – 2009 University of Pennsylvania School of Medicine

14 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 Hopkins Macquarie U Old Dominion Penn State UCLA U Chicago U Conn U Michigan UNC-Chapel Hill U Wash-Seattle Geo. Wash. U Emory U © 2008 – 2009 University of Pennsylvania School of Medicine

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

16 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 © 2008 – 2009 University of Pennsylvania School of Medicine

17 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 © 2008 – 2009 University of Pennsylvania School of Medicine

18 © 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 © 2008 – 2009 University of Pennsylvania School of Medicine

19 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 © 2008 – 2009 University of Pennsylvania School of Medicine

20 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 © 2008 – 2009 University of Pennsylvania School of Medicine

21 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 © 2008 – 2009 University of Pennsylvania School of Medicine

22 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 © 2008 – 2009 University of Pennsylvania School of Medicine

23 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 © 2008 – 2009 University of Pennsylvania School of Medicine

24 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 © 2008 – 2009 University of Pennsylvania School of Medicine

25 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 © 2008 – 2009 University of Pennsylvania School of Medicine

26 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 White Total Female 5 4 8 17 (70.0) Male 3 7 10 (30.0) TOTAL 8 (29.6) 4 (14.8) 15 (55.6) 27 (100.0) © 2008 – 2009 University of Pennsylvania School of Medicine

27 Distribution of Gender (Percent Female) by Rank and Track
Assistant Professor Associate Professor Professor Total Tenure CE Female 2 (100.0) 10 (90.9) 0 (0.0) 4 (66.7) 1 (0.50) 17 (70.0) Male 1 2 4 10 TOTAL 11 6 27 © 2008 – 2009 University of Pennsylvania School of Medicine

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

29 Dist’n of Race by Rank, Gender and Track: Biostatistics Faculty
American Indian/ Alaska Native Asian Native Hawaiian/ Other Pacific Islander Black/ African American White Total Professor Female Tenure CE 1 Male 3 4 Associate Professor 2 Assistant Professor 10 8 (29.6) 4 (14.8) 15 (55.6) 27 (100.0) © 2008 – 2009 University of Pennsylvania School of Medicine

30 Outline: Developing Biostatistics at Penn
History Organizational issues Faculty recruitment and retention Launching and sustaining a nationally competitive graduate (PhD, MS) training program (2000 - Promoting effective balance between collaborative and methodological research Recruiting and retaining excellent biostatistical analyst/programmer, data management and project management research staff Promoting and deploying a leading-edge research IT infrastructure Deploying biomedical informatics methods and tools, within a rapidly changing research landscape © 2008 – 2009 University of Pennsylvania School of Medicine

31 Biostatistics Educational Programs
Strong foundation in theory (partnership with Wharton – Department of Statistics) Excellent collaborative/consulting exposure (partnership with Clinical Epidemiology) Intentional integration of theory, methods & applied fields We want our graduates to be known as “well-rounded & balanced” Theory & methods Biomedical/Clinical research applications Strong collaborative/communication skills © 2008 – 2009 University of Pennsylvania School of Medicine

32 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 © 2008 – 2009 University of Pennsylvania School of Medicine

33 Typical Course Sequence for Students in PhD Program (Year 01)
Semester 1ST 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 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 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. © 2008 – 2009 University of Pennsylvania School of Medicine

34 Typical Course Sequence for Students in PhD Program (Year 02)
Semester 2ND 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 Completion of Consulting II Project/MS Thesis by deadline © 2008 – 2009 University of Pennsylvania School of Medicine

35 Typical Course Sequence for Students in PhD Program (Year 03)
Semester 3RD Year Proposed Curriculum: Required (Credit) Required – non-credit FALL BSTA 670: Statistical Computing (1.0) Advanced Elective Minor Teaching Assistantship1 SPRING BSTA 999 Reading Course Ethics Lectures SUMMER Thesis proposal, Oral Preliminary Examination 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. © 2008 – 2009 University of Pennsylvania School of Medicine

36 Typical Course Sequence for Students in PhD Program (Year 04, 05)
Semester 4TH 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 Assistantship1 SPRING BSTA 920 Dissertation Research (3 course units)2 Ethics Lectures Semester 5th Year Proposed Curriculum: Required (Credit) Required – non-credit FALL BSTA 920 Dissertation Research (3 course units)2 Teaching Assistantship1 SPRING Ethics Lectures 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. © 2008 – 2009 University of Pennsylvania School of Medicine

37 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 © 2008 – 2009 University of Pennsylvania School of Medicine

38 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 © 2008 – 2009 University of Pennsylvania School of Medicine

39 Research Challenges: The Mix
Local Minimum Cumulative Percent (55% methods) Based on sponsored funding only. So, proportion of sponsored funding that is collaborative Local Minimum reflects likelihood that methods research realistically will peak at 60% * Approximate, pending not included © 2008 – 2009 University of Pennsylvania School of Medicine

40 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 Than 30%/70% DEPARTMENTAL wide would be achievable unless the department faculty changed dramatically. There was a strong sentiment that 10%/ 90% was an exciting prospect for many of the % collaborative faculty Issue of movement to greater capacity for larger, longer term studies with heavy coordinating center activities ( later slide) will probably impact on our ability to increase the methods proportion. © 2008 – 2009 University of Pennsylvania School of Medicine

41 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? © 2008 – 2009 University of Pennsylvania School of Medicine

42 © 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 © 2008 – 2009 University of Pennsylvania School of Medicine

43 Faculty Research Areas of Collaboration
Aging (disabilities, depression, social functioning) AIDS (treatment adherence, viral genomics) Cancer (chemoprevention, lung, pancreas) Epidemiology (dermatology, pharmaco-epidemiology, cardiovascular, renal) Genetics of Complex Traits (SNPs, microarrays, proteomics) Injury Prevention (child safety, firearms) Lung Injury (ARDS) Neurodegenerative Diseases (Alzhemier’s, Parkinson’s) Schizophrenia, Depression Sleep (sleep apnea) © 2008 – 2009 University of Pennsylvania School of Medicine

44 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 © 2008 – 2009 University of Pennsylvania School of Medicine

45 Faculty Leadership of Cores
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 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 © 2008 – 2009 University of Pennsylvania School of Medicine

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

47 © 2008 – 2009 University of Pennsylvania School of Medicine
Case Studies Novel Methods for the Investigation of Metabolic Systems using conventional Statistical Tools'. Demonstrates how metabolic models are solved, and fitted to data using routine statistical software (R. Boston) Development of Improved methods for analysis of diverse populations (J. Shults) Assessment of the role of social support in weight loss studies in  African-American women, via improved estimation of the correlations with quasi-least squares (Justine Shults & Shiriki Kumanyika) Novel Approaches  for analysis of bone strength in children with renal disease (Justine Shults, Mary Leonard) © 2008 – 2009 University of Pennsylvania School of Medicine

48 © 2008 – 2009 University of Pennsylvania School of Medicine
Case Studies Cost-effectiveness of Pharmacogenetic Testing to Tailor Smoking Cessation Treatment Heitjan DF, Asch DA, Rukstalis M, Patterson F, Lerman C. (2008) Pharmocogenomics Journal. In smoking cessation drug trials, some genetic markers appear to have strong interactions with treatments, e.g., smokers homozygous for the –141C Ins/Del Ins C allele in the dopamine receptor DRD2 gene do better on bupropion; the rest do better on transdermal nicotine (the patch). Suggests a pharmacogenetic (PG) "test-and-treat" strategy: Perform a genetic test to determine which drug therapy is best. Methods: Using a Monte Carlo simulation model, we estimated the lifetime smoking cessation treatment costs and survival under various smoking cessation treatment plans. Results: showed i) drug therapies are generally cost-effective compared to counseling alone; ii) varenicline is superior to other drugs and to a PG strategy, but iii) in a sensitivity analysis, PG was competitive under favorable assumptions. Conclusions: PG strategies are not yet ready to replace best one-size-fits-all drug therapy for smoking cessation, but they may be close © 2008 – 2009 University of Pennsylvania School of Medicine

49 © 2008 – 2009 University of Pennsylvania School of Medicine
Case Studies (Cont’d) Copy Number Variation (CNV) and Human Diseases Wang K, Chen Z, Tadesse M, Glessner J, Grant SFA, Hakonarson H, Bucan M, Li M. (2008). Genome Research. Copy number variation (CNV) is a genomic region that is present at a variable copy number with respect to a reference genome. CNVs are ubiquitous in the human genome, and many of them have functional consequences. CNVs have been shown to be associated with susceptibility to HIV, autism, schizophrenia, and cardiovascular diseases. Current available high-throughput whole-genome SNP genotyping technologies allow detection of CNVs at a higher resolution than conventional approaches. Methods: Developed a hidden Markov model based approach that jointly models correlation of signal intensities across markers and genetic inheritance of CNVs for family members. Results: Showed that i) incorporation of genetic inheritance in CNV analysis can significantly increase accuracy of CNV calls and identification of CNV boundaries; ii) can allow detection of both inherited and de novo CNVs, iii) had superior performance as compared to existing CNV calling algorithms. Conclusions: i) CNV is a newly recognized genetic polymorphism, so there is lots of room for developing new statistical methods. ii) Future studies should consider modeling genetic inheritance of CNVs in the analysis. © 2008 – 2009 University of Pennsylvania School of Medicine

50 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. © 2008 – 2009 University of Pennsylvania School of Medicine

51 © 2008 – 2009 University of Pennsylvania School of Medicine
Methodology Research Simulation of power curves for permutation-based testing method for “correlated correlations” (Bilker) The representation of kinetic (e.g. drug, or mineral, metabolism) data and in terms of mathematical models and the interpretation of plasma disappearance profiles in terms of metabolic indices (Boston) Methods for correlated data and high dimensional problems, such as longitudinal data, time series, functional data, imaging analysis and density estimation. (Guo) Diagnostics for sensitivity to nonignorability (Heitjan) Bayesian statistical methods in health economics (Heitjan) Bayesian analysis in pharmacogenetics (Heitjan) © 2008 – 2009 University of Pennsylvania School of Medicine

52 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) © 2008 – 2009 University of Pennsylvania School of Medicine

53 Methodology Research (Cont’d)
Multi-center longitudinal clinical trial simulations, using 4 to 6 random effects, typical of longitudinal study in which patients are sampled by cluster and then followed over time (Localio) {using existing PC-based hardware would take 2 to 3 years to complete a single simulation} Estimating the cost-effectiveness of cancer therapies using propensity score methodology (Mitra) Estimating the sensitivity of the hazard ratio to nonignorable treatment assignment in non-randomized studies (Mitra) Evaluating the impact of individual haplotypes on disease in molecular epidemiology studies (Mitra) © 2008 – 2009 University of Pennsylvania School of Medicine

54 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) © 2008 – 2009 University of Pennsylvania School of Medicine

55 © 2008 – 2009 University of Pennsylvania School of Medicine
Who are the Students? Multi-disciplinary backgrounds: Preventive medicine Clinical epidemiology Microbiology Immunology Biology Mathematics Statistics Computer and information sciences Psychology Biochemistry & cell biology Epidemiology (genetics) Electrical engineering Mechanical engineering & management Pharmacology Reflects recognition that biostatistics is fundamentally a multi-disciplinary field © 2008 – 2009 University of Pennsylvania School of Medicine

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

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

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

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

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

61 © 2008 – 2009 University of Pennsylvania School of Medicine
Cohort #5 (Cont’d) Sumedha Chhatre PhD (Urban Planning), University of Louisville, 2000 MS (International Development), University of Pennsylvania, 1993 Joel Greshock MS (Biology), Villanova University, 1998 Rachel Hammond MS (Mathematics), Drexel University, 2004 Michal Magid-Slav MS (Biotechnology), University of Pennsylvania, 2001 MS (Life Science), Weizmann Institute, 1999 Michael Rambo BS (Mathematics), Alabama A&M University, 2001 Hao Wang MS (Statistics), University of California, Davis, 2000 MS (Chemistry), Institute of Chemistry, Chinese Academy of Science, 1994 © 2008 – 2009 University of Pennsylvania School of Medicine

62 © 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 © 2008 – 2009 University of Pennsylvania School of Medicine

63 © 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 © 2008 – 2009 University of Pennsylvania School of Medicine

64 © 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 © 2008 – 2009 University of Pennsylvania School of Medicine

65 © 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 © 2008 – 2009 University of Pennsylvania School of Medicine

66 © 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 © 2008 – 2009 University of Pennsylvania School of Medicine

67 Biostatistics MS Graduates
Name Year Current Employment Paula Martin 2002 AstraZeneca Jeffrey Botbyl 2003 GlaxoSmithKline Shane Raines Shu-Wen Yang Current position unknown John Palcza 2005 Merck Mengye Guo Continuing, Penn Biostatistics PhD © 2008 – 2009 University of Pennsylvania School of Medicine

68 Biostatistics MS Graduates
Name Year Current Employment Wenguang Sun 2005 Continuing, Penn Biostatistics PhD Ye Zhong Albert Einstein College of Medicine Rachel Hammond 2006 Center for Clinical Epidemiology and Biostatistics (CCEB), Penn Roger Mansson Current position unknown Valerie Teal Peter Wahl Healthcore, Inc. © 2008 – 2009 University of Pennsylvania School of Medicine

69 Biostatistics MS Graduates
Name Year Current Employment Huaqing Zhao 2006 Children’s Hospital of Philadelphia Angelo Elmi 2007 Continuing, Penn Biostatistics PhD Michelle Korenblit Towers Perrin Caiyan Li Xiaoli Shi Gilead Chia-Hao Wang © 2008 – 2009 University of Pennsylvania School of Medicine

70 Biostatistics PhD Graduates
Name Year Position Type Current Employment Heping Hu 2004 Industry Merck Li Qin Academia University of Washington Gui-shuang Ying University of Pennsylvania, Dept. of Ophthalmology Jiameng Zhang Genentech Yuehui Wu GlaxoSmithKline Jing Zhao © 2008 – 2009 University of Pennsylvania School of Medicine

71 Biostatistics PhD Graduates
Name Year Position Type Current Employment Clara Kim 2005 Government U.S. FDA Jing Cheng 2006 Academia University of Florida J. Mark Donovan Industry Bristol-Meyers Squibb Benjamin Leiby Thomas Jefferson University Julia Lin Cambridge Health Alliance Tao Liu Brown University © 2008 – 2009 University of Pennsylvania School of Medicine

72 Biostatistics PhD Graduates
Name Year Position Type Current Employment Laurel Bastone 2007 Industry Bristol-Myers Squibb Long Long Gao Centocor Robert Krafty Academia University of Pittsburgh Lingfeng Yang Wyeth © 2008 – 2009 University of Pennsylvania School of Medicine

73 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 © 2008 – 2009 University of Pennsylvania School of Medicine

74 © 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 © 2008 – 2009 University of Pennsylvania School of Medicine

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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 © 2008 – 2009 University of Pennsylvania School of Medicine

76 © 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 © 2008 – 2009 University of Pennsylvania School of Medicine

77 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 © 2008 – 2009 University of Pennsylvania School of Medicine

78 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 © 2008 – 2009 University of Pennsylvania School of Medicine

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

80 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 © 2008 – 2009 University of Pennsylvania School of Medicine

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

82 © 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 © 2008 – 2009 University of Pennsylvania School of Medicine

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

84 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 © 2008 – 2009 University of Pennsylvania School of Medicine

85 Penn’s Progress toward a Research Computing Facility
CRCU, ACC, BMIF, CVI, PGI, ITMAT,CEET, CFAR, etc. Formation of a Hybrid RCF Units High Performance Computing, Databases, LIMS, Clinical Apps, Statistical Genetics “Unit-Specific Applications” Basic Laboratory Units/Applications Clinical Research Units/Applications Basic Science Units/Applications Convergence & Optimization of Operations and Compliance HVAC, Power, Physical Space, & Physical Security “Data Center Facilities” RCF 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” Security, Privacy, Compliance Reporting & Monitoring User Authentication via Federated/Centralized services Coupled w/ Data Layer “Identity Management” 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” © 2008 – 2009 University of Pennsylvania School of Medicine

86 © 2008 – 2009 University of Pennsylvania School of Medicine
Clinical Research Informatics © 2008 – 2009 University of Pennsylvania School of Medicine

87 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 © 2008 – 2009 University of Pennsylvania School of Medicine

88 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 © 2008 – 2009 University of Pennsylvania School of Medicine

89 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) © 2008 – 2009 University of Pennsylvania School of Medicine

90 © 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. © 2008 – 2009 University of Pennsylvania School of Medicine

91 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 © 2008 – 2009 University of Pennsylvania School of Medicine

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

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

94 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/) © 2008 – 2009 University of Pennsylvania School of Medicine

95 Re-used from Global Library
Projects #1 – #6: OC Pilots Project #7: OC MultiCenter (>50 sites) RCT No. of Case Report Forms (CRFs) & No. of Common Data Elements (CDEs) (in parentheses) Development Hours PI Clinical Content Area Developed New Re-used from Global 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 21 (351) 218 4 June, C. Infectious Diseases, Immunology 18 (210) 2 (23) 402 5 FitzGerald, G 10 (85) 10 (102) 134 6 Reilly, M 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 © 2008 – 2009 University of Pennsylvania School of Medicine

96 © 2008 – 2009 University of Pennsylvania School of Medicine

97 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!! © 2008 – 2009 University of Pennsylvania School of Medicine

98 Overarching Strategic Goals
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.) 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 – 2009 University of Pennsylvania School of Medicine

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100 © 2008 University of Pennsylvania School of Medicine

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

102 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 © 2008 – 2009 University of Pennsylvania School of Medicine


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