Our Events in 2015

September, 2015 

  • The First UMBC–Stanford Workshop on Clinical Trials and Regulatory Science : 

Precision Medicine: Challenges and Opportunities from Regulatory, Clinical and Statistical Perspectives 

Date:              Sept. 19, 2015 (Saturday)

Location:      UMBC Physics 101 and Public Policy 105  

                      University of Maryland, Baltimore County

                      1000 Hilltop Circle, Baltimore, MD 21250

The inaugural 2015 UMBC-Stanford Workshop features plenary lectures by two prominent speakers, Dr. Robert Temple of the US Food and Drug Administration to present “Enrichment Designs in Precision Medicine” and Dr. Eric Gibson of Novartis Pharmaceuticals Corporation to discuss “Precision Medicine: Challenges and Opportunities from Industry Perspective”. Following the plenary talks, a floor panel discussion will be held in late morning. After lunch, the afternoon program will feature parallel invited and contributed sessions. This one-day workshop at UMBC immediately follows the 2015 FDA-Industry workshop in Washington D.C., providing networking opportunities for registered participants who will be served lunch in addition to coffee breaks.

Objectives: The UMBC-Stanford Workshop series brings together regulators, academic researchers, and industry professionals to explore prominent issues of common concern, particularly those related to laboratory and clinical development and regulation of biopharmaceutical products.

Target audience: Clinicians, statisticians, regulatory scientists, and other drug development professionals.

Organizing committee: Yi Huang (UMBC) • Nagaraj Neerchal (UMBC) • Bimal Sinha (UMBC) • Tze Lai (Stanford) • Philip Lavori (Stanford) • Ying Lu (Stanford) • Jie Chen (Novartis) • Joseph Heyse (Merck)

Registration: $100 for people registered online (http://med.stanford.edu/biostatistics/events/) before Sep. 14, 2015; $125 afterwards.  Check payment is accepted for onsite registration between 8:00 am – 12:00 noon, Sept. 19, 2015.

For more Information:

Please contact Dr. Yi Huang, Department of Mathematics and Statistics, University of Maryland Baltimore County (UMBC), 1000 Hilltop Circle, Baltimore, MD 21250.

E-Mail:  yihuang@umbc.edu.

The flyer for this event is here.


  • Bayesian Methods and Computing for Evidence Synthesis and Network Meta-Analysis

By Professor Brad Carlin, University of Minnesota

Please mark your calendar – Professor Bradley P. Carlin from the University of Minnesota will present a one-day short course titled, "Bayesian Methods and Computing for Evidence Synthesis and Network Meta-Analysis " on Friday, September 11, 2015, from 9:00 AM to 4:30 PM at 

Crowne Plaza Princeton - Conference Center
870 Scudders Mill Rd
Plainsboro Township, NJ 08536.

Please complete your online registration here: http://ptasatravelingcourse2015.eventbrite.com/

Dr. Brad Carlin is Mayo Professor in Public Health and Professor and Head of the Division of Biostatistics at the University of Minnesota. He has published more than 150 papers in refereed books and journals, and has co-authored three popular textbooks: “Bayesian Methods for Data Analysis” with Tom Louis, “Hierarchical Modeling and Analysis for Spatial Data” with Sudipto Banerjee and Alan Gelfand, and "Bayesian Adaptive Methods for Clinical Trials" with Scott Berry, J. Jack Lee, and Peter Muller. He is a winner of the Mortimer Spiegelman Award from the APHA, and from 2006-2009 served as editor-in-chief of Bayesian Analysis, the official journal of the International Society for Bayesian Analysis (ISBA). He has received uninterrupted NIH support as PI for his methodological work continuously since 1992. Prof. Carlin has extensive experience teaching short courses and tutorials, and has won both teaching and mentoring awards from the University of Minnesota. For more information, please visit his website: http://www.biostat.umn.edu/~brad/


​As the era of "big data" arrives in full force for health care and pharmaceutical development, researchers in these areas must turn to increasingly sophisticated statistical tools for their proper analysis.  Bayesian statistical methods, while dating in principle to the publication of Bayes' Rule in 1763, have only recently begun to see widespread practical application due to advances in computation and software.  This one-day short course introduces Bayesian methods, computing, and software, and goes on to elucidate their use in evidence synthesis and network meta-analysis (NMA).   Broad application of these methods has been driven by an increased need for quantitative health technology assessment (HTA), especially comparative effectiveness research (CER).  In particular, Bayesian methods facilitate borrowing of strength across treatments, trials, and outcomes (say, both safety and efficacy), as well as provide a natural framework for filling in missing data values that respect the underlying correlation structure in the data.  We include descriptions and live demonstrations of how the methods can be implemented in BUGS, R, and versions of the BUGS package callable from within R.  

Core Bayesian topics

  • Introduction to Bayesian inference:  point and interval estimation, model choice
  • Bayesian computing:  MCMC methods; Gibbs sampler; Metropolis-Hastings algorithm,recent developments (including STAN and non-MCMC methods)
  • Computer demo session:  Illustration of key features of BUGS for challenging models
  • Evidence Synthesis and NMA topics:
    • Essentials of NMA:  Fixed and random effect meta-analysis for binomial responses, prior selection, computer implementation, network diagrams, homogeneity and consistency, case studies
    • Bayesian evidence synthesis for safety and efficacy:  Contrast-based vs. arm-based approaches,ranking of treatments, adjusting for confounders, BUGS implementation, mixed outcome types (e.g. binary vs. continuous), aggregate vs. individual-level patient data,  arm- and contrast-based methods for checking consistency, incorporating non-randomized data
    • Application to drug safety analysis:  NMA for detecting safety signals from routinely collected adverse event data, hierarchical modeling, handling multiplicity, Berry-Berry approach, extensions to non-normal data, case study and graphical displays.


August, 2015 

  • Networking, Canoe/Kayak/Hike, Lunch, Free Gifts, Raffle for Prizes

Dear Colleagues and Friends,

Are you interested in networking with other statisticians while having lots of fun?

On Saturday August 29, join us for the Canoeing/Kayaking/Hiking and Lunch Networking event. There will also be free gifts and raffle for prizes.

10:30AM (plan to arrive early, we will be on site at 10): arrive at Princeton Canoe Rental (free parking across street, hiking routes close by, 4-seat canoe $16/canoe first hour, $8 second hour, Kayak $13 first hour, more rates check www.princetoncanoe.com, $5 allowance for kids)

12:00PM: group at lunch location (5-minute driving from canoe rental, free parking, ask for the group of ASA at front, cost: $10/adult, kids’ meal cost on us)

Please kindly RSVP (http://whoozin.com/CCJ-DGJ-JYTN) so we can get an estimate of headcount.


Important Notes:

  1. Canoe/Kayak Location: Princeton Canoe and Kayak Rental (483 Alexander St, Princeton, NJ)
  2. Lunch Location: Super Star East Buffet (311 Nassau Park Blvd. Princeton, NJ 08540. In the same shopping center as Wal-Mart and Home Depot, next to Sam's Club)
  3. Bring water shoes and pack an extra change of clothes
  4. Hiking routes are alongside the canal, so you can choose to hike if not canoe/kayak.
  5. Locations close to Princeton University and many shopping/dining places (e.g., Princeton Shopping Center, Market Fair, Quakerbridge Mall). Princeton Art Museum is free to public and open 10-5 on Saturdays. Great place to spend weekends with family.
  6. In case of bad weather, a rain date will be announced.


Organized By:

Princeton-Trenton Chapter of American Statistical Association


June, 2015 

  • Real World Evidence: Opportunities, Challenges and Approaches - 2015 Seminar

    The American Statistical Association Princeton-Trenton (ASA P-T) Chapter and Department of Statistics, Rutgers University are proud to present the 2015 Seminar on the topic of real world evidence on June 24 from 1:00 PM - 4:30 PM at the Auditorium in CoRE Building at Rutgers Busch Campus, Piscataway. Top-notched researchers and industry experts will share their current research on statistical theory and/or applications and drug development experience. Dr. David DeMets from the University of Wisconsin-Madison, Dr. Jason Roy from the University of Pennsylvania, and Dr. Ella Nkhoma from Bristol-Myers Squibb are among the invited speakers. Statisticians, researchers, and data analysts currently engaged in trial design and analyses are encouraged to attend this exciting event. 

    David L. DeMets, Ph.D.

    Max Halperin Professor of Biostatistics

    University of Wisconsin-Madison

    Big Data, Big Opportunities, Big Challenges

    Since the 1950’s, biostatisticians have been successfully engaged in biomedical research, from laboratory experiments to observational studies to randomized clinical trials. We owe some of that success to the early pioneers, especially those biostatisticians who were present at the National Institutes of Health (NIH). They created a culture of scientific collaboration, working on the methodology as needed to solve the biomedical research problems in design, conduct and analysis. Over the past 5 decades, we have experienced a tremendous increase in computational power, data storage capability and multidimensionality of data, or “big data”. Some of this expansion has been driven by genomics. At present, we have the opportunity to contribute to the design and analysis of genomic data, data stored in the electronic health record and continued needs of clinical trials for greater efficiency. However, with these opportunities, we have serious challenges starting with the fact that we need to develop new methodology to design and analyze the “big data” bases. The demand for quantitative scientists exceeds the supply and there is no strategic national plan to meet these demands. Federal funding for biomedical research has been flat and likely to remain so for several years, impacting both the ability to train additional quantitative scientists and provide them with research funding for new methodologies. We face new or more public scrutiny, demanding that our data and analysis be shared earlier and earlier, even as the data are being gathered such as in clinical trials. Litigation is now part of our research environment. We will examine some of these issues and speculate on ways forward.


    Jason Roy, Ph.D.

    Associate Professor of Biostatistics

    University of Pennsylvania Perelman School of Medicine

    A Bayesian Nonparametric Approach to Marginal Structural Models for Point Treatments and a Continuous Outcome

    Marginal structural models (MSM) are a general class of causal models for specifying the average effect of treatment on an outcome. These models can accommodate discrete or continuous treatment, as well as treatment effect heterogeneity (causal effect modification). The literature on estimation of MSM parameters have been dominated by semi-parametric methods, such as inverse probability of treatment weighted (IPTW) estimation.  Likelihood-based methods have received little development, probably in part due to the need to integrate out confounders from the likelihood and due to reluctancy to make parametric modeling assumptions. In this paper we develop a fully Bayesian MSM that maintains much of the flexibility of semi-parametric methods while delivering joint posterior distributions of the causal parameters. We take a Bayesian nonparametric approach, using a combination of a dependent Dirichlet process for the outcome distribution and Gaussian process for the mean to model the observed data. The performance of the methodology is evaluated in several simulation studies. The method is applied to data from a study on the effect of antiretroviral therapy on the neurocognitive performance of HIV-infected subjects.


    Ella Nkhoma, Ph.D.

    Associate Director of Epidemiology

    Bristol-Myers Squibb Co.

    Using real world data for drug safety evaluation

    This talk will cover how we can leverage real world data to understand drug safety during the development cycle and after launch.

  • Registration Instructions

    There is a $30 charge for this event, with light lunch included (lunch will be provided on site from 12:00 PM, and the talk starts from 1:00 PM). Event-parking will also be provided on-site. A pre-registration is REQUIRED by June 21, 2015. Please submit your registration at http://realworldevidence.eventbrite.com.

    Please note that seating is limited to 80 attendees. If you register and are unable to attend please advise us as soon as possible so that others on the waiting list can attend. 


May, 2015 

The ASA Princeton-Trenton Chapter is pleased to announce a spring fun event – wine tasting at a local winery. Please join us on Saturday, May 16, 2015 at Old York Cellars. This is a great opportunity to explore our local attractions and also network with your fellow statisticians.

If you are interested, please email Xiaohui (Ed) Luo with your name, affiliation, email and phone number by 5/9/2015.

We hope to see you there!

Where:         Old York Cellars (http://www.oldyorkcellars.com/)
                    80 Old York Rd., Ringoes, NJ 08551

When:           3:00pm to 4:00pm
                    Saturday, May 16, 2015

Cost:            $12 per person: includes six wines and a souvenir glass

Contact    Xiaohui (Ed) Luo (edmundluo@gmail.com)


April, 2015 

  • R Short Course: Epidemiological Computing and Graphics in Public Health with "R"

Friday, April 24, 2015, 8:30am-4pm
Room 2A
Rutgers School of Public Health
683 Hoes Lane West
Piscataway, NJ 08854

A 1-day course, sponsored by the
Office of Public Health Practice
Providing hands-on experience with:

R basics, and more, for data professionals
Real NJ Public Health applications & data
Quality color graphics

For more details, please refer to the R Course flyer.

Registration Link: