ASA Traveling Course hosted by ASA Princeton-Trenton Chapter: Bayesian Methods and Computing for Evidence Synthesis and Network Meta-Analysis

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ASA Traveling Course hosted by ASA Princeton-Trenton Chapter: Bayesian Methods and Computing for Evidence Synthesis and Network Meta-Analysis

By Princeton-Trenton Chapter of the American Statistical Association

When and where

Date and time

Friday, September 11, 2015 · 9am - 4:30pm EDT


Crowne Plaza Princeton - ​Conference Center 870 Scudders Mill Road Plainsboro Township, NJ 08536

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

By Professor Brad Carlin, University of Minnesota


Friday, September 11, 2015, from 9:00 AM to 4:30 PM

(We'll be at the conference center around 8:30 AM)


Room Einstein

Crowne Plaza Princeton - Conference Center

870 Scudders Mill Rd
Plainsboro Township, NJ 08536.
If you use google map and it happends to lead you to the the Crown Plaza Hotel side (900 Scudders Mill Rd, Plainsboro Township, NJ 08536), just ask the front desk about the Einstein Conference Room, and they should direct you to walk over a very short skybridge to the Einstein Conference Room. You may also use this address (900 Scudders Mill Rd) for parking purpose in case the parking lot on the 870 Scudders Mill Rd side is full.

Note that this is a one-day short course, a lunch buffet is included within each registration, parking is also complimentary.

About our Instructor:

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:


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.

About the organizer

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