Key Dates

Program Book


Lanju Zhang, Ph.D.

Data and Statistical Sciences, AbbVie Inc

Address: One North Waukegan Ave, North Chicago, IL 60064

Phone: (847) 938-6267


You are cordially invited to 2017 ICSA Applied Statistics Symposium in Chicago!

The 2017 ICSA Applied Statistics Symposium will be held from Sunday, June 25 to Wednesday, June 28, 2017, at Hilton Chicago Downtown (720 S Michigan Avenue, Chicago, IL 60605, USA). This will be the 26th annual symposium for the International Chinese Statistical Association. The theme of this conference is the Statistics for a new generation: challenges and opportunities, in recognition of the advent of a new generation of statisticians. We hope that this meeting will attract statisticians working in academia, government, and industry; domestic and international statisticians. The 2017 symposium will offer short courses, invited sessions, contributed sessions, and poster presentations, as well as opportunities for networking and recruiting.

June is the most agreeable time for the city of Chicago, which is directly accessible from most cities worldwide. Downtown Chicago provides numerous opportunities of dining, shopping and lodging, etc. In addition, it offers world-class attractions, including the Sky Deck, the Millennium Park, the Navy Pier, and the Shedd Aquarium and numerous museums, all within walk distance from Hilton Chicago.

Keynote Speakers

Barry D. Nussbaum, President, American Statistical Association

Barry D. Nussbaum was the Chief Statistician for the U.S. Environmental Protection Agency from 2007 until his retirement in March, 2016. He started his EPA career in 1975 in mobile sources and was the branch chief for the team that phased lead out of gasoline. Dr. Nussbaum is the founder of the EPA Statistics Users Group. In recognition of his notable accomplishments he was awarded the Environmental Protection Agency's Distinguished Career Service Award.

Dr. Nussbaum has a bachelor's degree from Rensselaer Polytechnic Institute, and both a master's and a doctorate from the George Washington University. In May, 2015, he was elected the 112th president of the American Statistical Association. He has been a fellow of the ASA since 2007. He has taught graduate statistics courses for George Washington University and Virginia Tech and has even survived two terms as the treasurer of the Ravensworth Elementary School PTA.

Invitation to Improve the Statistical Profession: A Discussion of the Current ASA "Asian Initiative"

Xiao-Li Meng, Department of Statistics, Harvard University, MA, USA

Xiao-Li Meng, Dean of the Harvard University Graduate School of Arts and Sciences (GSAS), Whipple V. N. Jones Professor and former chair of Statistics at Harvard, is well known for his depth and breadth in research, his innovation and passion in pedagogy, and his vision and effectiveness in administration, as well as for his engaging and entertaining style as a speaker and writer. Meng has received numerous awards and honors for the more than 150 publications he has authored in at least a dozen theoretical and methodological areas, as well as in areas of pedagogy and professional development; he has delivered more than 400 research presentations and public speeches on these topics, and he is the author of "The XL-Files," a regularly appearing column in the IMS (Institute of Mathematical Statistics) Bulletin. His interests range from the theoretical foundations of statistical inferences (e.g., the interplay among Bayesian, frequentist, and fiducial perspectives; quantify ignorance via invariance principles; multi-phase and multi-resolution inferences) to statistical methods and computation (e.g., posterior predictive p-value; EM algorithm; Markov chain Monte Carlo; bridge and path sampling) to applications in natural, social, and medical sciences and engineering (e.g., complex statistical modeling in astronomy and astrophysics, assessing disparity in mental health services, and quantifying statistical information in genetic studies). Meng received his BS in mathematics from Fudan University in 1982 and his PhD in statistics from Harvard in 1990. He was on the faculty of the University of Chicago from 1991 to 2001 before returning to Harvard as Professor of Statistics, where he was appointed department chair in 2004 and the Whipple V. N. Jones Professor in 2007. He was appointed GSAS Dean on August 15, 2012.

Personalized Treatment:
Sounds heavenly, but where on Earth did they find the right guinea pig for me?

What data are relevant when making a treatment decision for me? What replications are relevant for quantifying the uncertainty of this personalized decision? What does "relevant" even mean here? The multi-resolution (MR) perspective from the wavelets literature provides a convenient theoretical framework for contemplating such questions. Within the MR framework, signal and noise are two sides of the same coin: variation. They differ only in the resolution of that variation - a threshold, the primary resolution, divides them. We use observed variations at or below the primary resolution (signal) to estimate a model and those above the primary resolution (noise) to estimate our uncertainty. The higher the primary resolution, the more relevant our model is for predicting a personalized response. The search for the appropriate primary resolution is a quest for an age old bias-variance trade-off: estimating more precisely a less relevant treatment decision versus estimating less precisely a more relevant one. However, the MR setup crystallizes how the tradeoff depends on three objects: (i) the estimand which is independent of any statistical model, (ii) a model which links the estimand to the data, and (iii) the estimator of the model. This trivial, yet often overlooked distinction, between estimand, model, and estimator, supplies surprising new ways to improve mean squared error. The MR framework also permits a conceptual journey into the counterfactual world as the resolution level approaches infinite, where "me" becomes unique and hence can only be given a single treatment, necessitating the potential outcome setup. A real-life Simpson's paradox involving two kidney stone treatments will be used to illustrate these points and engage the audience.

This talk is based on the following three articles:

Roderick Little, Department of Biostatistics, University of Michigan, MI, USA

Roderick Little is a professor of biostatistics at the University of Michigan. Little received a PhD in statistics from Imperial College, London University in the United Kingdom. His current research interests involve analysis of data with missing values; analysis of repeated measures data with drop-outs; survey sampling, focused on model-based methods for complex survey designs that are robust to misspecification and compared to the resulting inferences to classical methods based on the randomization distribution; and applications of statistics to epidemiology, public health, psychiatry, sample surveys in demography and economics, and medicine. From 2010-12 he served as the inaugural Associate Director for Research and Methodology and Chief Scientist at the U.S. Census Bureau.

Some recent developments in the analysis of data with missing values
Missing data are a common problem in public health research. Methods for handling this problem are briefly reviewed, including (a) pros and cons of different forms of likelihood inference, specifically maximum likelihood, Bayes and multiple imputation; (b) penalized spline of propensity models for robust estimation under the missing at random assumption, and comparisons with other doubly-robust approaches; and (c) subsample ignorable likelihood methods for regression with missing values of covariates. I'll also discuss two aspects of a recent National Research Council study on the treatment of missing data in clinical trials, namely how missing data impacts the choice of estimand, and sensitivity analysis for assessing departures from assumptions of the primary analysis.

Ram C. Tiwari, Ph.D.

Ram C. Tiwari, Ph.D. is the Director for Division of Biostatistics, CDRH, effective June 27, 2016. He joined FDA in April 2008 as Associate Director for Statistical Science and Policy in the Immediate Office, Office of Biostatistics, Office of Translational Sciences, CDER. Prior to joining FDA, he served as Program Director at National Cancer Institute, NIH, and as Professor and Chair, Department of Mathematics, University of North Carolina at Charlotte.

Dr. Tiwari received his MS and PhD degrees from Florida State University in Mathematical Statistics; he is a Fellow of the American Statistical Association, and an Elected Member of the International Statistical Institute. He is a past President of the International Indian Statistical Association. He has published 200+ research papers on a wide range of statistical topics. His current research interests include developing frequentist and Bayesian methods in clinical trials and pre-and-post market drug/device safety evaluation.

Talk title: Bayesian approaches for benefit-risk assessment with examples

Abstract: An important aspect of the drug evaluation process is to have an integrated

benefit-risk assessment to determine, using some quantitative measures, whether the benefit outweighs the risk for the target population. The subject-level benefit-risk response is a five-category random variable with cell counts following a multinomial distribution. Assuming that the cell probabilities follow a Dirichlet distribution, we develop a Bayesian approach for the longitudinal assessment of benefit-risk using the global measures proposed by Chuang-Stein et al. In a more generalized approach, a power prior is used through the likelihood function to discount the information from previous visits. For the subject-level benefit-risk assessment, the cell-probability of the subject, with respect to a reference category, is modeled, on the logarithmic scale, as a generalized linear model using a Dirichlet process as a prior. The model is applied to drug/device clinical trial datasets.

Keywords: Benefit-risk, Dirichlet Distribution, Dirichlet process, Power Prior, Model Selection