The Joint Statistical Meetings, known simply as "JSM", is the largest gathering of statisticians held annually in North American. Faculty and student presenters from the  Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations. Our students often receive top awards and participate in the affiliated career marketplace at the event.

Zhe Sun of the Department of Biostatistics defends her dissertation on “Novel Statistical Methods in Analyzing Single Cell RNA Sequencing Data”.

Md Tanbin Rahman of the Department of Biostatistics defends his dissertation on "Classification and Clustering for RNA-seq Data with Variable Selection". 

Sponsored by the Lifetime Data Science (LiDS)  Section of the American Statistical Association (ASA) with support from Pitt's Department of BIostatistics, this conference will explore the foundations and frontiers of the field, featuring 3 workshops, 3 plenary talks, and 45+ invited sessions on a broad range of topics in lifetime data science. A poster session and reception will be held on May 29, and a banquet will be held on May 30, all at the Wyndham Pittsburgh University Center. 

Drug resistance has emerged to all antimalarial drugs. This talk will discuss some classic problems in management of drug resistance, ways to slow down resistance evolution, and proposed changes to global malaria management strategy in different zones of malaria endemicity.

Min Zhang, Department of Biostatistics, University of Michigan, will present "A general Classification framework to estimate the optimal dynamic treatment regime".

Biostatistics students will be recognized for various achievements and awards during the annual Biostatistics Student Awards ceremony.

The Real-Time Application for Portable Interactive Devices (RAPID) is an Amazon cloud-based system designed which functions as a Saas and Paas system to receive electronic information, analyze it and respond to the sender of the information within 24 hours.  The modular system accepts a variety of data types which may be evaluated by internal federal data analysts in real-time.  The bidirectional design permits the transmission of timely emergency information to health care providers or other individuals during public health emergencies.

CDER scientists chose to develop RAPID, a bidirectional communications and analytics systems platform, to solve many problems noted above during a national emergency and other FDA regulatory requirements.   The key IoT elements in the RAPID instance are to be a bidirectional data communication system, have a cloud base system which is flexible, scalable and provides a data lake of information with analytic tools to evaluate real time and streaming information and provide real time situational awareness to leadership enabling collaborative regulatory decision making and communication of key health data back to individuals in the field.

Shu Wang of the Department of Biostatistics defends her dissertation on "Clustering Methods with Variable Selection for Data with Mixed Variable Types of Limits of Detection". 

Ashley Naimi, Department of Epidemiology, University of Pittsburgh, will present "G Computation for Estimating the Per Protocol Effect of Aspirin on Pregnancy Outcomes".

Di Zhang of the Department of Biostatistics defends her dissertation on “Inference on Win Ratio for Clustered Semi-Competing Risk Data”.

Li Zhu of the Department of Biostatistics defends her dissertation on "Bayesian variable selection model and differential co-expression network analysis for multi-omics data integration".

Dr. Lin will present a spatiotemporal learning approach that leverages a deep learning framework with relevant social theories to help examine the relationship between human-societal activity and their social and geographical contexts, with applications including predicting political protest and opioid overdose events.

Saumyadipta Pyne, Public Health Dynamics Laboratory, and Department of Biostatistics, GSPH, University of Pittsburgh, will present "Statistical Matching for Data Fusion of Complex Phenotypes".

Join faculty, students, alumni and friends of the Pitt Biostatistics for breakfast at ENAR 2019 in Philadelphia. Start the morning off right, network with colleagues, and hear from our new chair, Shyamal Peddada, the latest news and initiatives from the department.  (RSVP by 3/21 appreciated)

The premier biostatistical convention in North America, the ENAR Spring Meeting is the annual meeting of the Eastern North American Region (ENAR) of the International Biometrics Society (IBS). The meeting covers a wide range of topics of interest to both researchers and practitioners, such as data science, genomics clinical trials, neuroimaging, biomarkers, health policy, electronic health records and epidemiology. Faculty and students in the Department of Biostatistics regularly participate in short courses, tutorials and roundtables and give invited talks, contributed talks, and poster presentations.

Laura F. White, PhD, associate professor of Biostatistics, Boston University School of Public Health, will present an approach for understanding the transmission patterns and incidence of TB using routinely collected data.

Annual Biostatistics departmental event for students to showcase their research in the format of posters and oral presentations.

The ASA Student Chapter of Pittsburgh is excited to host a career panel featuring Mattia Ciollaro of Amazon Alexa, Susan Bachman of Bachman Consulting, Sam Ventura of the Pittsburgh Penguins, Diana Cunningham of Highmark, Andrew Althouse of UPMC, and Bronwyn Woods of Duo Security.

Heng Huang, Department of Electrical and Computer Engineering, University of Pittsburgh, will present "Large-Scale Machine Learning Algorithms for Biomedical Data Science".

Dr. Keyes will provide a cross-generational and sociological framework to understand the connection between substance use and mental health among adolescents across birth cohorts from the last 40 years, with a particular focus on gender.

Open to all faculty and students, this informal monthly meeting features short presentations from faculty to introduce their research program and problems, followed by a Q&A. Designed to provide a platform for collaborations and to broaden the scope of science within the school. Gain a broader perspective and understanding of science and public health - with free coffee!

Xingyuan Li of the Department of Biostatistics defends her dissertation on "Modeling Exposure-Time-Response Association in the Presence of Competing Risks". 

Chao Huang, Department of Biostatistics, University of North Carolina at Chapel Hill, will present "Surrogate Variable Analysis for Multivariate Functional Responses in Imaging Data".

Jiebiao Wang is with the Department of Statistics and Data Science, Carnegie Mellon University.

Kedem will present a novel statistical method for estimating p when data only contain values below the designated threshold.

Jingshen Wang, Department of Statistics, University of Michigan - Ann Arbor, will present "Inference on Treatment Effects after Model Selection".

Derek MacFadden, MD, Harvard School of Public Health, will speak on antibiotic resistance as a growing public health threat and use is known to drive the distribution of antibiotic resistance.  He will review the evidence behind other potential contributing factors and the implications on the future burden.
Derek MacFadden, MD, Harvard School of Public Health, will speak on antibiotic resistance as a growing public health threat and use is known to drive the distribution of antibiotic resistance.  He will review the evidence behind other potential contributing factors and the implications on the future burden.Derek MacFadden, MD, Harvard School of Public Health, will speak on antibiotic resistance as a growing public health threat and use is known to drive the distribution of antibiotic resistance.  He will review the evidence behind other potential contributing factors and the implications on the future burden.

Yolanda Larriba, Universidad de Valladolid, will present, "ORI: A framework based on Order Restricted Inference to analyze data in chronobiology."

Presented by Pitt Public Health's Center for Social Dynamics & Community Health and Public Health Dynamics Laboratory, and the Pitt Clinical and Translational Science Institute's Biomedical Modeling Core, this conference will bring together national leaders from across the country to discuss the integration of modeling approaches into the field of behavioral and community health sciences. Through panel discussions and breakout sessions, attendees will learn about existing research, discuss associated challenges and opportunities, and chart a path forward for this emerging field. Registration is required; no cost.

In this hands-on workshop, learn to use FRED (Framework for Reconstructing Epidemiological Dynamics), a flexible, agent-based modeling platform that can be used to model diverse situations, including infectious diseases, chronic conditions, demographic changes at the population level, and the effects of human behavior. Workshop participants will become familiar with the FRED platform, learn the basics of incorporating and simulating agent behavior in simulation models, and create and run their own behavioral models using FRED Web, an online interface in which FRED models can be designed, run, and analyzed.

FRED can be used to predict outcomes of an epidemic, such as epidemic curves and disease incidence, and is a tool for evaluating interventions, such as school closure, social distancing and changes in vaccination behavior.  Using census-based statistically representative synthetic populations, FRED captures the demographic and geographically heterogeneous characteristics of actual populations, including realistic neighborhood, household, school and workplace social networks.

Join us for a Coffee and Collaboration Hour where interested faculty will have the opportunity to give a short, informal presentation to introduce their research program and problems. Open to all students and faculty.

Using statistically representative synthetic populations, FRED (A Framework for Reconstructing Epidemiological Dynamics) is a flexible system that can be used to model diverse situations, including infectious diseases, chronic conditions, demographic changes at the population level, and the effects of human behavior. FRED can be used to predict outcomes of an epidemic, such as epidemic curves and disease incidence, as well as for evaluating interventions, such as school closure, social distancing, and changes in vaccination behavior.

Christopher Keener of the Department of Biostatistics defends his dissertation on "Power and Sample Size Determination for Stepped Wedge Cluster Randomized Trials".

Joanne Beer of the Department of Biostatistics defends her dissertation on "Predicting Clinical Variables from Neuroimages Using Fused Sparse Group Lasso".

John Pleis of the Department of Biostatistics defends his dissertation on "Mixtures of Discrete and Continuous Variables: Considerations for Dimension Reduction".

Zeda Li, Paul H. Chook Department of Information Systems and Statistics, City University of New York, will present, “Adaptive Bayesian Time-Frequency Analysis of Multivariate Time Series”.

Zhou "Ark" Fang of the Department of Biostatistics defends his dissertation on "Integration and Missing Data Handling in Multiple Omics Studies".

Biostatistics guest speaker, Ciprian Crainiceanu, Johns Hopkins University, will review some of the most used and useful sensors in Health Sciences and the ever expanding WIT (Wearable and Implantable Technology) analytic environment.

The premier biostatistical convention in North America, the ENAR Spring Meeting is the annual meeting of the Eastern North American Region (ENAR) of the International Biometrics Society (IBS). The meeting covers a wide range of topics of interest to both researchers and practitioners, such as data science, genomics clinical trials, neuroimaging, biomarkers, health policy, electronic health records and epidemiology. Faculty and students in the Department of Biostatistics regularly participate in short courses, tutorials and roundtables and give invited talks, contributed talks, and poster presentations.

Biostatistics guest speaker, Michael Sohn, University of Pennsylvania, will present, "Statistical Methods in Microbiome Data Analysis."

Biostatistics guest speaker, Lu Tang, University of Michigan, will present, "Fusion Learning in Integrative Data Analysis."

Biostatistics guest speaker, Xiaoxiao Sun, University of Georgia, will present, "Theory Informs Practice: Smoothing Parameters Selection for Smoothing Spline ANOVA Models in Large Samples."  

Yongli Shuai of the Department of Biostatistics defends his dissertation on "Multinomial Logistic Regression and Prediction Accuracy for Interval-Censored Competing Risks Data".

Song Zhang of the Department of Biostatistics defends her dissertation on "Diagnostic Accuracy Analysis for Ordinal Competing Risks Outcomes Using ROC Surface".

Biostatistics Seminar guest speaker, Wen-Chi Wu, Merck & Co., Inc., will present, "Challenges and Prospect in the Drug Development – A Statistician’s Perspective."

Biostatistics Seminar guest speaker, Ying Huang, Fred Hutchinson Cancer Research Center, will present, "Inferential Procedures for the Use of Biomarkers in Treatment Selection".

Biostatistics department chair, SHYAMAL D. PEDDADA, will participate in SCIENCE 2017, on a panel discussion entitled, "Big Data, Machine Learning, Statistics, and Artificial Intelligence" - Spotlight Session 2.

Grand Rounds credit will be given for attendance at one or more of the workshop sessions. Check CourseWeb for details. This event is offered for 8 CPH CE credits.

Biostatistics Seminar guest speaker, Shuangge Steven Ma, PhD, Yale School of Public Health, will present, "Integrating multidimensional omics data for cancer prognosis."

Zhaowen Sun of the Department of Biostatistics defends her dissertation on "Power and Sample Size Determinations in Dynamic Risk Prediction".

Yi Liu of the Department of Biostatistics defends her dissertation on "Novel Test Procedures for Large-scale Bivariate Time-to-event Data on Single and Gene-based Markers, with an Application to a Genetic Study of AMD Progression"

Registration deadline has been extended to April 30, 2017. Click for more information or contact a.haydo@pitt.edu.

Biostatistics Seminar speaker, Kaleab Abebe, PhD, Associate Professor, Dept. of Medicine and Biostatistics, University of Pittsburgh, will present, "Carving out a niche in clinical trials as a collaborating (bio)statistician."

NOTE:  Biostatistics Student Awards Ceremony will be conducted before the seminar – ceremony starts at 3 pm.

Shannon Woolley of the Department of Biostatistics defends her dissertation on "Tests for Random Signs Censoring in Competing Risks"

Biostatistics Seminar speaker, Ali Shojaie, PhD, Associate Professor, Dept. of Biostatistics, University of Washington, will present, “Flexibility in High Dimensions: Sparse Additive Models with Adaptive Truncation via a Convex Hierarchical Penalty”.

Biostatistics Seminar speaker, Dr. Yi Li, Department of Biostatistics, University of Michigan, will present, "Modeling Big Biomedical Data: Challenges and Opportunities."

This week's Biostatistics Seminar will feature Dr. Nicole Lazar, Department of Statistics, University of Georgia, presenting, "Topological Data Analysis for Functional Magnetic Resonance Imaging Data".

This week's Biostatistics Seminar will feature Dr. Jong H. Jeong, Professor and Interim Chair, department of Biostatistics, University of Pittsburgh.

This week's Biostatistics Seminar will feature Dr. Joel B. Greenhouse, Professor of Statistics, Carnegie Mellon University who will present his talk, "Statistical Thinking and Public Health".

This week's Biostatistics Seminar will feature Dr. Lei Shen, Research Advisor, Global Statistical Sciences and Advanced Analytics, Eli Lilly & Company.  His seminar is titled “Subgroup Identification for the Development of
Tailored Therapeutics ".

This week's weekly Biostatistics Seminar will feature Dr. Donglin Zeng, professor at UNC Gillings School of Global Public Health, and the co-director of the Carolina Survey Research Laboratory, speaking on Sequential Multiple Assignment Randomization Trials with Enrichment Design.

This week's weekly Biostatistics Seminar will feature Dr. Daniel J. Schaid, Professor of Biostatistics, Health Sciences Research and Medical Genetics, Mayo Clinic, speaking on Genetic analysis of multiple correlated traits (genetic pleiotropy).

This week's weekly Biostatistics Seminar will feature Joan J. Anson, MS Ed, PC, Director of Career Services, Student Affairs who will present valuable information related to our department's Professionalism Seminar.

Min Qian of Columbia University presents on "Constructing Dynamic Treatment Regimes for Binary Outcome with Partially SMART data."

This week's seminar will be preceded at 3 p.m by the BIOSTAT Student Awards presentation.

Cancer clinical trials are routinely designed on the basis of event-free survival time where the event of interest may represent a complication, metastasis, relapse, or progression. Richard J. Cook of the University of Waterloo addresses a number of statistical issues arising from the use of such endpoints including the interpretation of results based on composite endpoints, the consequences of naïve analysis of event times subject to dual censoring schemes, and the causal interpretation of treatment effects. Related issues in the analysis of longitudinal and survival data will also be highlighted.

Biostatistics 2016 Spring Seminar Speaker Series presents

4/7/2016
Shili Lin, PhD
Ohio State University

Thursdays  |  3:30 pm  |   A115 Crabtree Hall 

Visit publichealth.pitt.edu/calendar or 
publichealth.pitt.edu/biostatistics-seminars  for details.

This special seminar features three presentations by faculty members from several departments.

• Dr. Douglas Landsittel (Professor, Medicine, Biostatistics and Clinical and Translational Science): “Causal inference, propensity scores and patient-centered outcomes research”
• Dr. Kehui Chen (Assistant Professor, Statistics and Psychiatry) will discuss some of her work on network community detection problems. 
• Dr. Ying Ding (Assistant Professor, Biostatistics): “A Journey to Statistical Science: Overview of my Recent and Ongoing Research Projects”

Presented by the ASA Student Chapter with support from the Department of Biostatistics. Free cookies, coffee, and tea will be provided!  Please see the flyer for more detailed information.

Bin Nan, PhD, Dept. of Biostatistics, University of Michigan, will present, " Regression with Covariate Subject to Limit of Detection." 

Cecile Viboud, PhD, Division of International Epidemiology and Population Studies, Fogarty International Center, NIH. 

A115 Crabtree

Russell (Taki) Shinohara will present.

A115 Crabtree Hall

Visit publichealth.pitt.edu/calendar or
publichealth.pitt.edu/biostatistics-seminars  for details.

Biostatistics 2016 Spring Seminar Speaker Series presents

2/25/2016
RUSSELL (TAKI)  SHINOHARA, PHD
Assistant Professor of Biostatistics in Department of Biostatistics and Epidemiology 
University of Pennsylvania, Perelman School of Medicine

"DEVELOPING TISSUE DAMAGE AND REPAIR BIOMARKERS FROM CONVENTIONAL MAGNETIC RESONANCE NEUROIMAGING"

While conventional magnetic resonance imaging (MRI) is measured in arbitrary intensity units, much work has centered on the development of normalization procedures. In this talk, we propose a simple, fast, and robust statistical normalization method which does not require prior segmentation of a reference tissue. This technique allows us to measure changes across the brain using conventional MRI in interpretable statistical units, providing a promising new tool for large multi-center studies and clinical practice in which conventional MRI are used routinely. We then propose methods for the identification of multiple sclerosis (MS) white matter lesions both cross-sectionally and longitudinally at the population level using these statistically normalized images which are now quantitative. Finally, we examine the prospect of quantifying disease activity and tissue damage using our statistical quantitative MRI, derived solely from widely available conventional MRI, compared to contrast-enhanced and advanced quantitative MRI modalities. Our preliminary results indicate that statistical quantitative MRI is significantly less costly, less invasive, and yet may provide more precise and sensitive biomarkers for disease-related changes in MS on MRI. 

Document:  Seminar Flyer - Taki Shinohara.pdf

A115 Crabtree Hall

Learn how preparation and presentation help to ensure better performance during those all-important interviews whether they be for internships, fellowships or full-time positions. 

Visit publichealth.pitt.edu/calendar or 
publichealth.pitt.edu/biostatistics-seminars  for details.

Biostatistics 2016 Spring Seminar Speaker Series presents

 Joan J. Anson, MS ED, PC - Professionalism Seminar - Interviewing Skills

Learn how preparation and presentation help to ensure better performance during those all-important interviews whether they be for internships, fellowships or full-time positions. 

Thursdays  |  3:30 pm  |   A115 Crabtree Hall 

The value of simple models, rapidly built with available data, to aid decision makers during response to catastrophic infectious disease events will be demonstrated through 4 models: 1) Estimating, in near real-time, the impact of the 2009 H1N1 influenza pandemic; 2) Modeling input to aid the decision regarding the production of H3N2v monovalent vaccine; 3) Estimating impact of proposed vaccination for a hypothetical H5N2 influenza pandemic (exercise); and 4) A Risk-Benefit analysis of different options for testing and treating asymptomatic patients potentially infected with contaminated steroid product. These examples will be used to illustrate some basic concepts that must guide modelers as they interact with public health decision makers during responses.

A115 Crabtree

Marnie Bertolet, PhD Pitt Public Health Assistant Professor of Epidemiology; the Clinical and Translational Science Institute; and Biostatistics Thursday, February 4, 2016 3:30 pm, A115 Crabtree Hall Response-Adaptive Trials: The Reality can be Messy Abstract. The Statins for Pulmonary and Cardiac Complications of Chronic HIV (SPARC) pilot clinical trial utilized a drop-the-loser urn response-adaptive design to allow participants higher probability of being randomized to the more effective study arm. A number of real-life complications made this design challenging including: 1) The use of a surrogate outcome measured at week 4 to adapt the randomization probabilities; 2) Some randomizations that occurred prior to the determination of all the previous participants’ surrogate outcomes; and 3) Participants with visits outside the window for the surrogate outcome measure. The theory for these designs assumes that the adaptive randomization is based on the final outcome. This provides two additional challenges: 1) SPARC has multiple final outcomes five months after the surrogate is measured; and 2) The surrogate was not so strongly related to the outcome as expected. Dr. Bertolet will describe how these challenges were addressed and the impacts on the implementation, analysis and results of the SPARC trial. 

A115 Crabtree

Marnie Bertolet will present.

A115 Crabtree Hall

Visit publichealth.pitt.edu/calendar or
publichealth.pitt.edu/biostatistics-seminars  for details.

ABSTRACT:  to come

Biostatistics 2016 Spring Seminar Speaker Series presents

TBD
 
Thursdays  |  3:30 pm  |   A115 Crabtree Hall 

A115 Crabtree

Joan Anson will present.

A115 Crabtree

Joan J. Anson, MS ED, PC - Professionalism Seminar - Cover Letters 

A115 Crabtree Hall

Visit publichealth.pitt.edu/calendar or
publichealth.pitt.edu/biostatistics-seminars  for details.

ABSTRACT:  to come

Biostatistics 2016 Spring Seminar Speaker Series presents

Joan J. Anson, MS Ed, PC
University of Pittsburgh, Pitt Public Health

 
Thursdays  |  3:30 pm  |   A115 Crabtree Hall 

A115 Crabtree

Meredith Wallace will present.

A115 Crabtree

Meredith L. Wallace, PhD, University of Pittsburgh, Dept. of Psychiatry, will present, "Variable Selection for Skewed Model-Based Clustering: Revealing Novel Sleep Phenotype."

A115 Crabtree Hall

Meredith L. Wallace of Pitt's Department of Psychiatry presents a talk entitled "Variable Selection for Skewed Model-Based Clustering: Revealing Novel Sleep Phenotype" as part of the Biostatistics 2016 Spring Seminar Speaker Series.  

Visit publichealth.pitt.edu/calendar for details.

ABSTRACT:  In sleep research, applying mixture models to sleep characteristics captured through multiple data types, including self-reported sleep diary, a wrist monitor capturing nighttime movement (actigraphy), and brain waves (polysomnography), may suggest new phenotypes that reflect actual disease mechanisms rather than self-reported symptoms. However, a direct mixture model application is challenging because: 1) there are many sleep characteristics from which to choose, 2) sleep measures are often highly skewed, even in homogenous samples, and 3) solutions that are interesting clinically often incorporate all three data types. Dr. Wallace will discuss her novel variable selection method that is based on the restricted multivariate skew-normal distribution and which suggests multiple statistically plausible solutions incorporating all data types. She will demonstrate her proposed methods using a sample of older adults and reveal novel sleep phenotypes to help researchers generate hypotheses regarding pathophysiological mechanisms and new personalized treatments to be developed.

Biostatistics 2016 Spring Seminar Speaker Series presents

Meredith L. Wallace, PhD
Assistant Professor, Department of Psychiatry, University of Pittsburgh
 
Thursday, January 21, 2016
3:30 pm, A115 Crabtree Hall

A115 Crabtree

Joan Anson will present.

Sourya Shresthra of the Department of Epidemiology at the Johns Hopkins Bloomberg School of Public Health will present a noon seminar entitled "Characterizing the Role of Influenza in the Epidemiology of Pneumonia." 

Interactions arising from sequential viral and bacterial infections may play important roles in the epidemiological outcome of many respiratory pathogens. Influenza virus has been implicated in the pathogenesis of several respiratory bacterial pathogens commonly associated with pneumonia. However, studies focusing on different scales of resolution have painted an inconsistent picture. Individual-scale animal experiments have unequivocally demonstrated an association, whereas the support in population-level epidemiological studies remains variable and unclear. We use multiple sources of epidemiological data which vary in the time periods they cover and the scales at which they are collected, and a mechanistic transmission model, both within a likelihood-based inference framework to characterize the nature, timing, and magnitude of this interaction. We find support for a strong but short-lived interaction, with influenza infection increasing susceptibility to pneumococcal (and other bacterial) pneumonia by ~100-fold, in line with our understanding from animal challenge experiments. The population-level impact of such an interaction can be modest during non-pandemic seasonal influenza periods (which may in turn make it difficult to detect), but can be substantially larger during pandemic periods (and also more detectable). We argue that these seemingly inconsistent observations at different scales can be explained by this proposed interaction: a strong but short-lived susceptibility enhancement of pneumonia following influenza.