2016 joint statistical meetings registration guide · 2016-04-18 · with more than 3,400...

July 30–August 4

JSM2016

The largest annual gathering of

statisticians and data scientists

in the world

CONFERENCE

REGISTRATION

GUIDE

11 national and international statistical societies

More than 6,000 attendees from 50+ countries

1,000+ student attendees

80+ exhibitors

More than 600 technical sessions

75+ employers hiring for more than 200 positions

JOIN US! Register today at www.amstat.org/jsmregistration

JSM will be held at McCormick Place, Chicago,

West Building2301 S. Lake Shore Drive,

Chicago, IL 60616

With more than 3,400 individual presentations arranged into ap-proximately 181 invited sessions, 400 contributed sessions, and 500 poster and speed presentations, the 2016 Joint Statistical Meetings will be one of the largest statistical events in the world.

It will also be one of the broadest, with topics ranging from statis-tical applications in numerous industries to new developments in statistical methodologies and theory. And it will include presenta-tions about some of the newer and expanding boundaries of statistics, such as analytics and data science. JSM offers a unique opportunity for statisticians in academia, industry, and government to exchange ideas and explore opportunities for collaboration, as well as for beginning statisti-cians (including current students) to learn from and interact with senior members of the profession.

WELCOME

July 30–August 4

JSM2016

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2 | Registration Guide 2016

JSM HIGHLIGHTS

Meet, mingle with, and listen to such well-known statisticians as:

Yong Chen, Hilary Parker, and Gabriel Chandler mingle at last year’s opening mixer.

Donald Berry, The University of Texas MD Anderson Cancer Center

Nilanjan Chatterjee, The Johns Hopkins University

David Dunson, Duke University

Brad Efron, Stanford University

Constantine Gatsonis, Brown University

Rafael A. Irizarry, Dana Farber Cancer Institute / Harvard

Michael Jordan, University of California at Berkeley

Sally Morton, University of Pittsburgh

Hilary Parker, Stitch Fix

Ross Prentice, Fred Hutchinson Cancer Research Center

Kathryn Roeder, Carnegie Mellon University

John Myles White, Facebook

Hadley Wickham, Rice University / RStudio

Follow us on Twitter @AmstatNews Use

#JSM2016

Joint Statistical Meetings 2016 | 3

Reduced JSM Registration Fees

Reduced Professional Development Fees

Free Student Mixer

Reduced Career Service Fee

State-of-the-Art Exhibit Hall

Networking with Renowned Statisticians

Technical Presentations

Membership in the ASA for $18

STUDENT BENEFITS AND OPPORTUNITIES

SUNDAYFirst-Time Attendee Orientation and Reception12:30 p.m. – 2:00 p.m.

In the EXPO! JSM Opening Mixer & Invited Poster Session Sponsored by Eli Lilly and Microsoft6:00 p.m. – 8:00 p.m.

ASA Awards Celebration and Editor Appreciation6:30 p.m. – 7:30 p.m.

MONDAYASA President’s Invited Address4:45 p.m. – 6:15 p.m.

JSM Student Mixer Sponsored by Monsanto6:00 p.m. – 8:00 p.m.

Korean International Statistical Society Annual Meeting6:00 p.m. – 7:30 p.m.

SPECIAL EVENTS

International Indian Statistical Association Meeting and Mixer6:00 p.m. – 8:30 p.m.

TUESDAYStatistical Society of Canada Reception5:30 p.m. – 7:00 p.m.

ASA President’s Address and Founders & Fellows Recognition8:00 p.m. – 9:30 p.m.

JSM Dance Party9:30 p.m. – Midnight

WEDNESDAYInternational Chinese Statistical Association Annual Members Meeting6:00 p.m. – 9:00 p.m.

Jasmit Shah, Kelly-Ann Dixon Hamil, and Sammy Chebon meet at last year’s JSM Student Mixer in Seattle.

ART SHOWExplore the “art” in Data Art with an exhibit featuring data artists. Positioned just outside the exhibit hall, this new JSM feature will explore the relationship between data and art, which promises to be both amazing and beautiful.


Be sure to catch a speed session! Each will consist of 20 oral presenta-tions of approximately five minutes, followed by a poster session. All poster presentations will include the use of electronic poster boards.

SPEED SESSIONS

Speed session topics for 2016 include the following:

Environmental Statistics

Advances in Nonparametric Statistics

Statistics in Government and Engineering

Statistical Computing and Sports

Statistics for Education and Social Sciences Research

Bayesian Analysis

Advances in Biopharmaceutical Research

Epidemiological Research

Statistics for Health and Health Policy

Advances in Biometrics

Statistical Learning and Data Mining

Business, Finance, and Economic Statistics

Statistical Methods for Clinical Trials and Longitudinal Analysis

Advances in Statistical Genetics

Advances in Survey Research Methodology


The popular Introductory Overview Lectures will return in 2016, with the following four sessions scheduled:

Spatio-Temporal Data Analysis, organized by Christopher Wikle of the University of Missouri

Causal Inference, organized by Judea Pearl of the University of California at Los Angeles

Data Science, organized by David A. van Dyk of Imperial College London

Adaptive Clinical Trial Design, organized by Scott Berry of Berry Consultants

INTRODUCTORY OVERVIEW LECTURES

It is impossible to do justice to the breadth and depth of the scientific program by highlighting just a few highly visible sessions. With 45 parallel sessions taking place during most of the meetings, everyone is guaranteed to find presentations of interest.

Statistics and the Media: Science Journalism Meets Statistics: How Jellybeans

Explain Your Sex Life — Regina Nuzzo,

Freelance Journalist/ Gallaudet University,

JSM 2015

Recent Advances in Interactive Graphics

for Data Analysis: Ggvis: Moving Toward a Grammar of Interactive Graphics — Hadley Wickham, RStudio, JSM 2015

SPEED SESSION Topics on Gen-eral Methodology in Public Health:

Imputing Estrogen Receptor Status in a Population-Based

Cancer Registry: A Sensitivity Analysis — Rebecca Andridge,

The Ohio State University, JSM 2015


ASA Presidential Address and Founders & Fellows RecognitionJessica Utts University of California at Irvine“Appreciating Statistics”Tuesday, August 2, 8:00 p.m.

ASA Deming LectureVincent P. Barabba Market Insight Corporation“Profound Knowledge from a Knowledge Use Perspective”Tuesday, August 2, 4:45 p.m.

COPSS Fisher LectureAlice S. Whittemore Stanford University School of Medicine“Personalizing Disease Prevention: Statistical Challenges” Wednesday, August 3, 4:45 p.m.

NAMED LECTURES

IMS Medallion Lecture Nanny Wermuth

Johannes Gutenberg-University/Chalmers University of Technology “Tracing Pathways of Dependence: How Far Did We Get?”

Monday, August 1, 10:30 a.m.

IMS Medallion Lecture Gerda Claeskens

KU Leuven “Model Averaging and Post-Model Selection”

Wednesday, August 3, 2:00 p.m.

FEATURED SPEAKERS


EXHIBITORSJSM exhibitors provide you the opportunity to observe and learn about state-of-the-art products and services related to the statistical industry. Check out the companies already planning to join us in Chicago:

AAAS Science & Technology Policy Fellowship

ASA-SIAM

Advanced Clinical

American Mathematical Society

Aptech Systems, Inc.

Berry Consultants

Bureau of Economic Analysis

Bureau of Justice Statistics

CRC Press/Taylor & Francis Group

Cambridge University Press

Cengage Learning

Cytel Inc.

Deloitte Consulting LLP

Experis BI & Analytics Practice

Fred Hutchinson Cancer Research Center

Frontline Systems, Inc.

GCE Solutions

Green Key Resources

Hawkes Learning

IBM

Institute of Mathematical Statistics

JMP Software from SAS

JSM 2017

Liberty Mutual Insurance

Microsoft

Minitab

NCSS

NORC

National Center for Education Statistics

National Science Foundation

National Security Agency

North Carolina State University

Oxford University Press

Pearson

Penfield Search Partners

Penn State World Campus

Personify

RStudio

SAS R&D

SAS Books

SAS Education Practice

SIAM

Sage Publishing

Salford Systems

Springer

Stat-Ease, Inc.

StataCorp LP

Statistical Society of Canada

Statistics & Data Corporation

Statistics.com

Statpoint Technologies Inc.

Takeda Pharmaceuticals

Texas A&M University-Kingsville

The Lotus Group LLC

U.S. Census Bureau

U.S. Food and Drug Administration

USDA-National Agricultural Statistics Service

University of Kansas Department of Biostatistics

W.H. Freeman & Co/ Macmillan Learning

WebAssign

Wiley

Wolfram Research

XLSTAT

EXPO HoursJuly 31 1:00 p.m. – 4:30 p.m. Opening Mixer 6:00 p.m. – 8:00 p.m.

August 1 9:00 a.m. – 5:30 p.m.

August 2 9:00 a.m. – 5:30 p.m.

August 3 9:00 a.m. – 2:30 p.m.

Follow us on Twitter @AmstatNews Use #JSM2016


SUNDAY, July 311:00 p.m.Spotlight Chicago Kick-OffEnjoy a taste of Chicago’s most recognizable foods and find out what makes Chicago such a magnetic city!

Enjoy a taste of Chicago in the JSM 2016 EXPO! Spotlight Chicago will feature events throughout the week to give you a little taste of the city in case you are too busy to get away from the convention center. Check out the schedule and stop by Spotlight Chicago to see more!

SPOTLIGHT

CHICAGO

MONDAY, August 19:00 a.m. Chicago Insider TipsWhether you are here for the first or the hundredth time, here is your chance to find out the depths of what Chicago has to offer.

10:00 a.m.JSM Coffee HouseRefresh with a cup of coffee or tea.

11:00 a.m.–3:00 p.m.JSM Photo BoothStop by to create memories with your friends using fun props.

1:30 p.m. Popcorn BreakSponsored by XLSTATEnjoy samples of Chicago’s famous Garrett’s Popcorn.

3:30 p.m. Midwest Microbrew TastingSponsored by Capital One Come by to taste Goose Island microbrews (while supplies last).

TUESDAY, August 210:00 a.m.JSM Coffee House Grab a cup of coffee and take a break!

1:30 p.m. Popcorn BreakSponsored by XLSTATEnjoy samples of Chicago’s famous Garrett’s Popcorn.

3:30 p.m.Sweet Chicago!Experience the sweetness of Chi-cago with root beer float samples made from Filbert’s root beer and Oberweis Dairy’s ice cream, Chicago traditions since 1926 and 1915. Still need something for your sweet tooth? Pick up an Eli’s cheese-cake bite (while supplies last).

WEDNESDAYAugust 310:00 a.m.JSM Coffee HouseRefresh with a cup of coffee or tea.

1:30 p.m. Popcorn BreakSponsored by XLSTATThis is your last chance to enjoy specialty samples of Chicago’s famous Garrett’s Popcorn.

SPOTLIGHT

SPOTLIGHT3:30 p.m. Chicago: Home of the Brownie A delicacy since the 1893 World’s Fair! The Palmer House hotel kitchen created a portable goodie to be enjoyed in boxed lunches at the fair. Enjoy samples from the convention center catering kitchen (while supplies last) and mingle with other attendees.

CHICAGO CHICAGOThis year, attendees will get the chance to make an impact on the local community. Details will be on the website soon.

NEW


In addition to the 45 parallel sessions taking place during most of the meetings, there are other activities you can add to your program for a fee: Professional Development courses, roundtable discussions, the Career Service, and workshops. In short, we expect you to be very busy … and to not mind it at all.

JSM ADD-ONS

Continuing Education: Interactive Model Building

in JMP Pro, Mia Stephens, SAS Institute (JSM 2015)

Continuing Education: Statistical Analysis of Financial Data with R, David Ruppert, Cornell University (JSM 2015)

Roundtable discussions (JSM 2010)


Early LateASA Student Member $65 $90

Student $95 $125

ASA Member $125 $150

Nonmember $175 $200

CAREER SERVICEThis is not your typical career fair! Explore opportunities by interview-ing with top statistical employers, including those from industry, gov-ernment, and academic organizations. Proactively search positions and contact employers of interest to you through our online messag-ing service. Employers will arrange interviews with you directly. All interviews take place in our onsite Career Service center.

WANT TO PARTICIPATE? Add your Career Service

applicant registration when you register for JSM.

Interested in recruiting at JSM? Join the 70+ organizations hiring in Chicago. Check out the Recruiters tab at www.amstat.org/jsmsponsors!

Applicant registration includes ...• Access to the Online Employer Search, including hundreds of job postings

• Access to the online Career Service Message Center, allowing you to contact employers in advance

• Access to the Career Service for onsite interviews

AbbVie

Amazon

Amgen

Bank of America

Boehringer Ingelheim Pharmaceuticals

Capital One

Eli Lilly and Company

FDA

Fred Hutch

Genentech

Incyte Corporation

KPMG

National Institute of Standards and Technology

National Security Agency

Novartis

Pacific Northwest National Laboratory

Sandia National Laboratories

SAS Institute

Seattle Genetics

StataCorp

Uber

University of Florida

University of Mississippi Medical Center

U.S. Census Bureau

W.L. Gore

Walt Disney

PAST EMPLOYERS:

Fees


SPEAKERS WITH LUNCH ANDA.M. and P.M. ROUNDTABLE DISCUSSIONS

Speakers with LunchIf listening to a fascinating talk while having lunch with friends and colleagues sounds good to you, sign up for one of the speakers with lunch events. These lunches also offer great discussion and networking opportunities.

A.M. and P.M. Roundtable DiscussionsFor interesting discussion and a networking event that doesn’t bust your wallet, register for an A.M. roundtable discussion, of-fered Monday through Wednes-day from 7:00 a.m. – 8:15 a.m. Tickets are just $20.

If early morning isn’t your style, P.M. roundtables also offer great discussion and network-ing opportunities and are held Sunday through Wednesday from 12:30 p.m. – 1:50 p.m. Tickets are $45.

Don’t forget to sign up for one (or more) of these opportunities when you fill out the registration form in the back of this guide.

The Speakers with Lunch events and roundtables offer both regular and vegetarian meals. Please be sure to mark your preference.

Tickets for these events will be sold onsite until 2 p.m. the day before the occasion is scheduled if the events are not already sold out.

REGISTER EARLY (June 1 for discounted rates) Use the form in the back of this guide, or register online at www.amstat.org/ jsmregistration.


P.M. ROUNDTABLE DISCUSSIONS Sunday, July 31, 2016

12:30 p.m. – 1:50 p.m.

$45 (includes meal)

Sunday’s LunchHerb-grilled chicken and ber-ries salad; rolls and butter; your choice of iced tea, hot tea, or coffee; and dessert. Chef’s choice of vegetarian menu.

SPAIG Committee SL01Modeling Means and Variances Using Mixed Effects Location Scale Models for Intensive Longitudinal Data Donald Hedeker, The University of Chicago

In this presentation, we will focus on an adolescent smoking study using ecological momentary assessment (EMA) where interest is on character-izing changes in mood variation. I will describe how covariates can influence the mood variances and extend the statistical model by add-ing a subject-level random effect to the within-subject variance speci-fication. This permits subjects to have influence on the mean, or loca-tion, and variability, or (square of the) scale, of their mood responses. Models for both continuous and ordinal outcomes are described and illustrated with examples.

SUNDAY—SPEAKER WITH LUNCH

JSM 2007


A.M. ROUNDTABLE DISCUSSIONS Monday, August 1, 20167:00 a.m. – 8:15 a.m.

$20 each (includes continental breakfast)

Continental BreakfastHouse-made pastries; seasonal fruit; Greek yogurt; and a cup of coffee, tea, or juice.

Section on Bayesian Statistical Science ML01Bayesian Nonparametric Methods for Regression Modeling Athanasios Kottas, University of California at Santa CruzThis roundtable will focus on different methods and applications of semi-parametric and fully nonparametric Bayesian regression modeling.

Section on Statistical Consulting ML02From Criticism to Curios-ity, Changing Our Attitudes in Consulting with Data-Oriented Individuals in Other Fields Jason Brinkley, American Institutes for ResearchThe purpose of this roundtable is to discuss the idea of ‘statistician as critic’ and illustrate how perme-ated the idea of serving as critic is embedded within our culture. As an alternative, it will be suggested that statisticians approach problems with curiosity.

ML03Best Practices for Communicat-ing with FDA CDRH Statistical Reviewers Christopher Mullin, NAMSAWe will discuss how having a solid strategy for presenting and discuss-ing statistical issues, as well as best practices for how to conduct one-self during communications with the FDA review team, can streamline negotiations with the FDA—and, ultimately, grant patients access to safe and effective medical devices as soon as possible.

Section on Statistical EducationML04How to Integrate Open-Access and Open-Source Educational Materials Andrew Bray, Reed CollegeWithin statistics, there are many open access and open source projects, including textbooks, vid-eos, lab materials, activities, and quiz banks. We will provide a

survey of the landscape and then facilitate a discussion about how best to use them in your course and how to contribute your own materials to these projects. Educa-tors who are using open resources and those who are looking to get started are encouraged to attend.

Government Statistics Section ML05Developments in the Analysis of Cognitive Interview Data Gordon Willis, National Cancer InstituteCognitive interviewing is a well-established qualitative method for questionnaire development, testing, and evaluation. However, less attention has been paid to analysis of data obtained through the conduct of cognitive testing. Convening a meeting of experts who have interest and experience in the use of well-defined, reliable analysis procedures would be useful in establishing the state-of-the-science; in reviewing the variety of current practices; and in identifying a research agenda for further developing, evaluating, and promoting analysis procedures. We will discuss this need.

Section on Statistical Learning and Data MiningML06What Can Statistics Learn from Machine Learning? And Vice Versa? Ryan Tibshirani, Carnegie Mellon UniversityMachine learning, being relatively young, has recently gained an enormous amount of attention in both academia and industry. Are

machine learning and statistics the same discipline? Are they fundamentally different, and how? What can statistics learn from machine learning? And vice versa? Come to this roundtable and we will discuss these issues ane more. Suggested reading: “Statistical Modeling: The Two Cultures,” by Leo Breiman, avail-able at https://projecteuclid.org/euclid.ss/1009213726; “Rise of the Machines,” by Larry Wasserman, available at www.stat.cmu.edu/~larry/Wasser-man.pdf; and “50 Years of Data Science,” by David Donoho, available at http://courses.csail.mit.edu/18.337/2015/docs/50YearsDataScience.pdf.

Section on Physical and Engineering Sciences ML07Learn More About the Industrial Statistics Virtual Collaboratory Jennifer Van Mullekom, DupontThis roundtable will describe the Industrial Statistics Virtual Col-laboratory (ISVC), in detail, show you how you can participate, and provide you with details about vari-ous successful collaborative efforts from SPES members. Representa-tives from other organizations are encouraged to attend.

Section for Statistical Programmers and Analysts ML08Quality Assurance for Statistical Programming and Analysis Michael Messner, U.S. Environmental Protection AgencyIn this roundtable, we’ll discuss how quality assurance plans, oversight, and documentation can be built into your daily routine.


Section on Bayesian Statistical Science ML10Bayesian Model Selection Philip Dawid, University of CambridgeThe “compleat Bayesian” approach to model selection is totally straight-forward—so long as we have well-specified proper prior distributions. Many folks worry about sensitivity to the choice of priors and would like a more objective approach. However, switching to improper priors is highly problematic. There have been numerous attempts to get around these problems. How well do they succeed?

Biopharmaceutical Section ML11Design for Dose-Finding Trials (Phase I/II or Post-Marketing Trials)Lei Nie, FDA

The goal of this discussion is two-fold: first, we will discuss the best practice in designing phase I and II trials to recommend good doses

for phase III confirmatory trials; second, we will discuss clinical trial designs for post-marketing trials finding optimal doses.

ML12Best Practices for Interim Analysis in Clinical TrialsJiang Hu, FDAIn this roundtable discussion, we would like to review practice prob-lems and statistical issues involved in interim analysis such as the com-pleteness and integrity of the interim analysis plan, precise performance of analysis for interim data, sample size re-estimation, and handling unexpected circumstances.

ML13“Evidence Synthesis”: Is It Only for Health Technology Assessment, or Should It Be Used to Impact Early Drug Development?Amit Bhattacharyya, GlaxoSmithKlineTo prepare for reimbursement through the Health Technology Assessment (HTA) appraisals in different countries, manufacturers undertake comparing the efficacy of active drugs against competitors in the market. In absence of head-to-head comparison, manufacturers employ an analytical technique to achieve comparison using “evi-dence synthesis.” In this approach, the new asset is compared with other competitors using indirect treatment comparison methods (or NMA, network meta-analysis) to differentiate and position the new entrant to the market. While the evidence synthesis had tremendous value in the HTA submissions, there

is significant opportunity to use the concepts of NMA earlier to inform drug development plans. We plan to discuss and share best practices across the industry in this area of research.

ML14Communicating Our Value: Are You a Tipping Point Maven?Susan Duke, GlaxoSmithKlineAre we, as statisticians, mavens (information brokers), in the words of Tipping Point author, Malcolm Gladwell? Gladwell elucidates three types of people who facilitate the change, or tip, to a new way of doing things: maven, connector, and salesperson. Mavens connect with information (in our world, that’s clinical data); connectors connect people; and salespersons know how to package ideas and persuade. Does this idea resonate with you? Could it be that biostatis-ticians are the informational ‘glue’ that connects within our organiza-tions? The goal of this roundtable is to spend a few minutes introducing these concepts, with the remainder of the time spent discussing partici-pants’ experiences.

Section on Statistical ConsultingML15Statistical Consultants as Catalysts for Organizational ChangeRichard Ittenbach, Cincinnati Children’s HospitalStatistical consultants are being asked to serve as consultants to one another in such important areas as collaborative teamwork, education

P.M. ROUNDTABLE DISCUSSIONS Monday August 1, 201612:30 p.m. – 1:50 p.m.

$45 (includes meal)

Section on Statistics in SportsML09From Pixels to Points: Using Tracking Data to Measure Performance in Professional SportsLuke Bornn, Simon Fraser University

I will explore how player tracking data gives us new insights into per-formance and strategy. In particular, I will show how we can exploit the spatiotemporal information in this data to measure decision-making and defensive impact in much finer detail than was previously possible.

MONDAY—SPEAKER WITH LUNCH


and training of others, and service to lay/professional communities. As such, statistical consultants are increasingly being asked to look inward and serve as resources and catalysts for change within their own departments and institutions. The purpose of this roundtable is to explore characteristics needed for these new consultative roles, mechanisms for remaining on top of the ever-changing landscape of statistical collaboration, and models for working with others to promote optimal and sustainable growth and development within their own departments and institutions.

ML16Strategies for Working with Administrators and Collabora-tors to Ensure Academic and Financial Success of Statistical Consulting Centers Todd Coffey, Washington State UniversityThis roundtable will bring together directors and statisticians working in academic or health care statisti-cal centers to discuss strategies for success and recipes for failure in working with administrators and collaborators to fulfill the mission of its center. Discussion will include whether centers should be funded by extramural monies, metrics on which a center should be evaluat-ed, and justifying a center that was primarily established for mentoring statistical collaborators.

Section on Statistical EducationML17Specifications Grading in a Statis-tics CourseEric Reyes, Rose-Hulman Institute of TechnologyIn her book Specifications Grad-ing: Restoring Rigor, Motivating Students, and Saving Faculty Time, Linda Nilson makes a case that

the current grading systems used throughout academia are flawed and actually motivate students to produce unsatisfactory work. Her proposed solution is a system she calls specifications grading. We will discuss the practical challenges of implementing specifications grad-ing in a statistics course.

ML18LISA 2020: Educating Statistical CollaboratorsJames Rosenberger, Penn State UniversityThe LISA 2020 Program educates statisticians and data scientists from developing countries to become effective collaborative statisticians and helps them create statisti-cal collaboration laboratories to propagate this training to more statisticians and data scientists, col-laborate with researchers to solve problems and make decisions, and engage in statistical outreach to improve the statistical skills and literacy of their community. The goal is to create a network of 20 statistical collaboration laboratories in developing countries by 2020. Find out the current progress of the 5+ stat labs in the network and how you can help sustain the labs or expand the network.

ML19Incorporating Visual Literacy Standards in an Introductory Statistics CourseJill YoungThe focus of this roundtable discus-sion is a project conducted in an introductory college course on business statistics. Students used statistics to analyze e-voting data and learned how to visually repre-sent their analysis. Students were introduced to infographic software and visual literacy competencies. Working in small groups, students

used infographic software to de-velop visual analyses. The instructor and library coordinator established a rubric for students as a frame-work for their visual representation. Students developed and demon-strated knowledge in all seven skill areas defined in the Visual Literacy Competency Standards for Higher Education.

ML20Teaching Statistical Collaboration Eric Vance, LISA-Virginia TechWe will discuss practices for teaching the nontechnical skills essential for success as a statistician such as listening, summarizing, and paraphrasing; asking good questions; explaining statistics to nonstatisticians; structuring effective meetings; teamwork; collaborative processes for approaching any applied statistics problem; oral and written communication; and making ethical decisions.

Government Statistics SectionML21Writing for Scientific PublicationIngegerd Jansson, Statistics SwedenWhat does it take to write for scientific publication? We will discuss how style, presentation, and organization of results matter; the differences between writing a government report, proceedings for a conference, and a scientific paper; how to present results so they are interesting to a wider audience; how to choose the most appropriate journal as the outlet for one’s research; what peer review includes and the formalities one needs to be aware of when submitting to a scientific journal; what kind of support is needed for less-experienced writers; and how the more experienced can help.

Monday’s LunchApplewood smoked turkey cobb salad; rolls and butter; choice of iced tea, hot tea, or coffee; and dessert. Chef’s choice of vegetarian menu.


Section on Statistical GraphicsML22Graphics Software: Favorites and Best PracticesAmelia McNamara, Smith CollegeThere are many tools available for the production of statistical graphics and data visualizations. Most of us are familiar with R, d3.js, Tableau, plot.ly, and even Excel. But there are many other bespoke pack-ages out there. During this round-table, we will discuss our favorite (and not-so-favorite) graphics software packages.

Mental Health Statistics SectionML23Causality in a Social World: Moderation, Mediation, and Spill-OverGuanglei Hong, The University of ChicagoThis roundtable discussion will be organized around the presenter’s research monograph with the above title published by Wiley in July 2015. The book clarifies for applied researchers the theoretical concepts of moderated effects, me-diated effects, and spill-over effects.

ML24Beyond Bonferroni: Large-Scale Inference for Complex Disorders Wesley Thompson, University of California at San DiegoWe will discuss the need for innova-tive statistical approaches to identify polygenetic effects and reduce the proportion of ‘missing heritability.’

Quality and Productivity SectionML25From Statistician to Data Scientist: How to Prepare?Ming Li, REANCONIn this roundtable, we will cover topics that bridge the gap between statisticians and data scientists.

Section on Risk AnalysisML26Statistical Precursors to the ‘New’ Predictive AnalyticsStanley Sclove, University of Illinois at ChicagoTwo aspects of predictive analysis will be considered: choosing a regression equation for prediction and predictive distributions for future observations. Articles from 1920 and the 1960s will be discussed as precursors to modern “predictive analytics” for such problems.

Section on Teaching of Statistics in the Health SciencesML27Training Statisticians to Teach StatisticsJacqueline MiltonThere are many questions regard-ing how to incorporate pedagogi-cal education into doctoral students’ training. Should all doctoral students be required to participate, or only those who are interested in teaching? What type of activities should be involved in this training (developing teaching strategies, learning how to develop course curriculum, etc.)? Should we teach students how to teach in nontradi-tional classrooms (online format, flipped classrooms, etc.)? We will discuss these questions and others, as well as share our ideas and experiences in teaching doctoral students how to teach.

To view complete roundtable descrip-

tions, visit www.amstat.org/meetings/

jsm/2016.


A.M. ROUNDTABLE DISCUSSIONS Tuesday, August 2, 20167:00 a.m. – 8:15 a.m.


Biopharmaceutical SectionTL01Interim Futility Analysis in the Presence of Delayed Effect in Immunotherapy Clinical TrialsXue LinFor immunotherapies, it may take time for the treatment to show an effect due to its working mechanism: It does not work directly on tumor cells, but rather trains the immune system to fight the tumor cells. Our discussion will focus on the impact it has on interim futility analysis and methodologies to adjust futility boundaries, timing of the futility analysis, the impact of loss to follow up on the analysis, etc.

Section on Statistical ConsultingTL02The Presentation of Results in Published MaterialsSusan E. Spruill, Applied Statistics and ConsultingWe’ll talk about how we can educate our colleagues in properly interpreting and publishing the statis-tical analysis of their experiments.

Government Statistics SectionTL03Barriers to and Facilitation of Teaching with the ASA Ethical Guidelines for Statistical Practice Rochelle Tractenberg, Georgetown University Medical CenterThe ASA Ethical Guidelines for Statistical Practice are available on the ASA website to all statistics instructors. Two barriers have been identified in incorporating these guidelines into undergrad, grad, and postgraduate training: time and effort. Facilitators include published syllabi and case studies, as well as that training with the guidelines actu-ally meets or satisfies NSF and NIH ethics training requirements. We’ll discuss those barriers and others.

Health Policy Statistics SectionTL04Learning Health Systems: From Ideas to Reality Rebecca Yates Coley, Johns Hopkins Bloomberg School of Public HealthWhile advances in electronic health

record infrastructure and statistical computing have made it possible to provide dynamic individualized decision support in a clinical setting, many statisticians are frustrated to encounter additional barriers to im-plementing analytic tools in a learn-ing health system framework. We’ll discuss how statistical expertise and leadership skills can be brought to bear on these challenges.

TL05Search for Truth Amidst the Bias: Evaluate the Impact of Unmeasured Confounding in Comparative Observational Studies Xiang Zhang, Eli Lilly and CompanyWe’ll discuss: 1) current prac-tices for evaluating the impact of unmeasured confounding; 2) new approaches that can produce an adjusted effect estimate with re-duced bias by using information on unmeasured confounders obtained external to the study; 3) a best practical guidance flowchart for researchers to execute in their own real-world projects.

Section on Statistics in ImagingTL06Statistical Tools for Clinical Neuroimaging Ciprian Crainiceanu, The Johns Hopkins UniversityCancer, stroke, multiple sclerosis, Alzheimer’s disease, and traumatic brain injury produce changes in the brain. These changes make it dif-ficult to use standard neuroimaging tools for analysis and require new methods and a deeper understand-ing of the specific pathology. We’ll discuss the various problems that remain open as well as the new

tools in R that combine the strength of existent neuroimaging tools with a familiar computational environment.

Section on Statistical Learning and Data MiningTL07Data Science: Bridging Academia and Industry Justin Dyer, Google, Inc.The term “data science” has cropped up to loosely describe the marriage of large-scale computing, Big Data, statistics, and computer science. We’ll discuss this changing area from a variety of perspectives.

Survey Research Methods SectionTL08Alternative Goals for Adaptive Survey DesignPeter Miller, U.S. Census BureauWe’ll discuss alternative goals for implementation of adaptive survey designs and weighing the implica-tions of the choice of goals on other sources of survey error.

Section on Teaching of Statis-tics in the Health SciencesTL09Utilizing Technology Tools Without Detracting Focus from Statistical Concepts Jennifer Daddysman, University of KentuckyAs statistical calculations move to software, it becomes valuable to teach students software applica-tions. However, it is easy for students and instructors to unintentionally shift the focus of the course from learning the concepts to learning the nuances of the software tool. We’ll discuss strategies to shift the focus back to learning statistical concepts.

Continental Breakfast: House-made pastries; seasonal fruit; Greek yogurt; and a cup of coffee, tea, or juice.

WITHDRAWN


P.M. ROUNDTABLE DISCUSSIONS Tuesday August 2, 201612:30 p.m. – 1:50 p.m.

$45 (includes meal)

Economic Outlook LuncheonBusiness and Economic Statistics SectionTL10Implications of Slowing U.S. Growth for the Near-Term OutlookRobert Gordon, Northwestern University

Robert Gordon is the Stanley G. Harris Professor of Economics at North-western University and author of The New York Times bestseller The Rise and Fall of American Growth: The U.S. Standard of Living Since the Civil War. Gordon will discuss productivity growth trends over the past 150 years and their implications for economic growth over the next 25 years. He will emphasize the difference between healthy productivity growth during the “dot.com” decade of 1995–2004 and the disap-pointing productivity growth of barely 1.0 percent per year since 2004. His contrast between innovations over the past three decades and those foreseen for the next few decades will be supplemented with a review of the “headwinds” of inequality, demographics, education, and fiscal reckoning that are in the process of pulling down the feasible growth rate of real median disposable income relative to the growth rate of output per hour.

TUESDAY—SPEAKER WITH LUNCH

Section on Bayesian Statistical ScienceTL11Why Popular Bayesian Nonpara-metric Methods Fail for High-Dimensional Clustering TasksRebecca Steorts, Duke UniversityIt is common to treat record link-age and community detection as clustering tasks. In fact, most generative models for clustering implicitly assume the number of data points in each cluster grows linearly with the total number of data points. Such tasks therefore require models that yield clusters

whose sizes grow sublinearly with the size of the data set. We ad-dress this requirement by defining the \emph{microclustering property} and discussing a new model that exhibits this property. We talk about successes regarding this new approach to applications in official statistics and the Syrian conflict.

Biopharmaceutical SectionTL12Statistical Issues in the Design and Analysis of Rheumatology TrialsYongman Kim, FDAStatistical issues in trials in rheuma-tology conditions such as rheu-matoid arthritis (RA) and psoriatic arthritis (PsA) will be discussed within the purview of clinical devel-opment of drug products. Impacts of design features on statistics—in-cluding patient population, control group, background treatment, rescue, or escape treatment—and key efficacy endpoints will be dis-cussed. In addition, we will discuss statistical methods such as defining the estimand (e.g., ITT or de facto), handling escape and/or missing data, sensitivity analyses, analytic models, multiplicity adjustment for secondary endpoints proposed for inclusion in the product label, etc. Also, we will briefly discuss features of analysis-ready data sets (ADaM) relating to recent work of the RA therapeutic area user guide (TAUG) group of CFAST.

TL13Unmet Medical Needs: Can We Accelerate Drug Approval and Marketing Through Expansion Cohort Trials? Soumi Lahiri, GlaxoSmithKlineThis roundtable invites your ideas, concerns, and experiences on how an expansion cohort trial can be ef-ficiently designed to meet scientific, regulatory, and patient needs.

TL14Challenges in Designing Comparative Clinical Studies for Biosimilar Product Yun Wang, FDA To resolve residual uncertainty in the assessment of the biosimilar product after the analytical, animal, and PK/PD studies, respectively, comparative clinical studies are often conducted for demonstrat-ing biosimilarity. In this roundtable session, we would like to discuss challenges we may face when designing such clinical studies for biosimilar applications.

TL15The Impact of Emerging Science and Technology on Biopharma Statisticians Satrajit Roychoudhury, Novartis Pharmaceutical CompanyThe greatest challenge and op-portunity for drug development as we move into the 21st century is to understand a disease area in all its complexity. The tools for dealing with this complexity demand the adaptation and application of


Tuesday’s LunchHerb grilled chicken and ber-ries salad; rolls and butter; choice of iced tea, hot tea, or coffee; and dessert. Chef’s choice of vegetarian menu.

emerging science and technolo-gies. In this roundtable discussion, attendees will share their experi-ences, challenges, and successes while I encourage them to focus on solutions and opportunities for enrichment of skills in the evolving world of science and technology.

Section on Statistical ComputingTL16Recent Developments in Machine Learning and Biomedicine Michael R. Kosorok, The University of North Carolina at Chapel HillWe will explore several new inno-vations in machine learning such as deep learning and random forests, which are advancing biomedical applications, including biomarker discovery and precision medicine. We will explore how statistical reasoning can sometimes be ap-plied to machine learning tools from computer science and engineering to improve performance in small sample sizes and messy data set-tings. We will also explore several important, open research questions.

TL17Big Data, Computing, and Statistics Lexin Li, University of California at BerkeleyA collection of topics related to Big Data computing and statistics are to be discussed, including data collection (smart devices, the Inter-net), data storage and processing (Hadoop, Spark), parallelization (MapReduce, doParallel), and ana-lytics (statistical machine learning, efficient computational algorithms).

Section on Statistical EducationTL18Making a Powerful First-Day ImpressionAndré Michelle Lubecke, Lander UniversityThe first day of class is an important one. It sets the tone for the entire course. Come learn the details of how I spend my first hour in statis-tics class and share your best ideas for an effective and meaningful first meeting with students.

TL19Introductory Statistics in Two-Year and Four-Year Colleges: More Similar (and More Different) Than Many People Realize Brian Kotz, Montgomery CollegeAt this roundtable, we will discuss some of the needs and practices of two-year college statistics instruction and discuss how the GAISE Report, other educational resources, and targeted programs have improved educational practices. Representa-tives of the ASA/AMATYC Joint Committee of the Education Council will be on hand.

TL20Do We Need a Journal for Undergraduate Statistical Research? Miles Ott, Augsburg CollegeIs there a need for a journal of undergraduate statistical research? Is there an interest in such a journal from both faculty and students? If such a journal were to exist, what kind of submissions would be accepted (theory, simulation, applied/consulting, statistical edu-cation, etc)? How valuable would this type of journal be for statistical education? In this roundtable, we will consider these questions and discuss ideas for the possible cre-ation of a journal of undergraduate statistical research.

TL21Practical Considerations for Teaching Statistics in a Hybrid, Flipped, or Online FormatJane Monaco, The University of North Carolina Gillings School of Global Public HealthThis roundtable will provide an opportunity to discuss practical considerations, best practices, and lessons learned in teaching statistics as technology, online resources, and student expectations continue to evolve.

Section on Statistics in EpidemiologyTL22Adaptive Randomized Trial Designs: New Methods and Software Michael Rosenblum, Johns Hopkins Bloomberg School of Public HealthWe will discuss recent develop-ments in methods and software for the design and analysis of adaptive enrichment designs. Also, we will discuss methods for improving precision of estimators of the aver-age treatment effect by leveraging information in baseline variables; these methods can be used in adaptive designs and nonadaptive trial designs.

Section on Statistical GraphicsTL23The Use of Color in Statistical Graphics Kevin Keen, University of Northern British ColumbiaWe will discuss how selecting op-timal colors for a graphical display is nontrivial and depends on the choice of media.


Health Policy Statistics SectionTL24Calling All Statisticians to Con-sider Becoming Hospital Board of Director: An Impactful Way to Transform Health Care Madhu Mazumdar, Icahn School of Medicine At Mount SinaiI will discuss statisticians sitting on the board of directors (BOD) for a hospital and call on statisticians to apply for these positions. I’ll outline the process of acquiring a BOD position and illustrate the value of this role in terms of developing lead-ership skill and affecting health care transformation by sharing my experi-ence of working with BOD members at Mount-Sinai Health System.

TL25Should Quality Ratings Be Adjusted for Effects of Patient Socioeconomic Characteristics? Alan M. Zaslavsky, Harvard Medical SchoolWhile adjustment of quality mea-sures based on outcomes for patient clinical characteristics is gener-ally accepted, both adjustment of process measures and adjustment for social characteristics remain controversial, all the more so in combination. We will discuss this controversy from policy, scientific, and ethical perspectives.

Mental Health Statistics SectionTL26Machine Learning for Exploratory Analyses of Psychological Data Gitta LubkeIn this roundtable discussion, I will present our recent extension of an existing boosting algorithm designed for multivariate out-comes. Details of the algorithm are described at http://arxiv.org/abs/1511.02025.

TL27Learning About Mechanisms: Causal Mediation Analysis Using RTeppei Yamamoto, MITWe will discuss various method-ological and practical issues in the use of mediation methods in social, behavioral, and medical science applications. The primary focus will be on the R package media-tion and how to use it in various applied settings, but participants are welcome to bring more general questions from their research.

To view complete roundtable descriptions, visit www.amstat.org/meetings/

jsm/2016.

Quality and Productivity SectionTL28Break the Chicken and Egg Cycle: Increasing an Organization’s Analytic Maturity Sarah Kalicin, Intel CorporationThrough this roundtable, partici-pants will discuss organizational challenges and how statisticians can lead efforts to break the chicken and egg cycle for orga-nizations to fully adopt analytic methodologies to improve data-driven decisions.

Survey Research MethodsTL29Machine Learning Applications for Survey Design, Collection, and Adjustment: Going Beyond the Trees to see Clusters, Forests, and Neighbors Trent Buskirk, Marketing Systems GroupIn this roundtable we will discuss various ways machine learning methods have and can be used throughout the survey data-collec-tion process and survey weighting adjustment phases, including ran-dom forests, cluster analysis, and k-nearest neighbors approaches.


A.M. ROUNDTABLE DISCUSSIONS Wednesday, August 3, 20167:00 a.m. – 8:15 a.m.


Biopharmaceutical SectionWL01Assessing Safety of Rare Events for Sparse, Limited, or Extreme DataMike Wright Colopy, UCB BioscienceIn this discussion, we will identify the shortcomings of standard statisti-cal techniques, pros and cons of more advanced techniques, how well companies are prepared for unexpected rare safety signals, and how statisticians can communicate the risks to nonstatisticians.

Section on Statistical EducationWL02Teaching Bayesian Statistics to Undergraduates Jeffrey Witmer, Oberlin CollegeStudents should be exposed to Bayesian reasoning. This can be done at the undergraduate level in at least two ways: (1) including a unit on Bayes in a traditional course or (2) offering a Bayesian course. We’ll discuss these options.

Section on Statistical GraphicsWL03Statistical Graphical Deception Brian Hochrein, Truven Health AnalyticsWe will explore the visual decoding process, which is called “graphical perception.” We will look at what makes a graphical method success-ful or unsuccessful and potentially deceptive to the audience for which it is intended. The goal is to provide graphical techniques and strategies for both data analysis and data communication.

Health Policy Statistics SectionWL04Statistical Careers in Health Policy Layla Parast, RAND CorporationWe will discuss statistical career opportunities in health policy, as well as short-term and long-term differences and similarities between statistical positions related to health policy and academia, consulting, and industry.

WL05Navigating the Long and Wind-ing Road to Validly Interpreting Patient-Reported Outcomes Joseph Cappelleri, Pfizer Inc.This roundtable involves a discus-sion of approaches to enrich interpretation of patient-reported out-comes. I will provide an updated review on two broad approach-es—anchor-based and distributed-based—aimed at enhancing the understanding and meaning of patient-reported outcome scores.

Section on Statistics in ImagingWL06The Key Role of Statistics in Neuroimaging: Challenges and OpportunitiesDuBois Bowman, Columbia UniversityWe will briefly discuss existing approaches to analyze neuroimag-ing data and highlight remaining challenges and opportunities. Among other topics, this roundtable will address prediction methods, Bayesian modeling, functional brain networks, the search for neuroimaging biomarkers, and multimodal imaging.

Section on Statistical Learning and Data MiningWL07Members Choice: Hot Topics in Statistical Learning and Data Mining Glen Wright Colopy, University of OxfordThis roundtable will bring together attendees interested in discussing their current practices in data sci-ence and any innovations required to make a technique excel for a particular problem. Crucially,

attendees are encouraged to share the titles of any reference material they found helpful in getting a new user of the technique up to speed.

Section on Teaching of Statis-tics in the Health Sciences WL08Learning Bayesian Update via Shiny: Understanding Bayesian Methods Through VisualizationJ. Jack Lee, MD Anderson Cancer CenterCompared to the frequentist meth-od, the Bayesian approach offers many advantages. Participants are encouraged to share their experi-ences and successful examples in teaching Bayesian methods to both statisticians and nonstatisticians.

Section on Teaching of Statis-tics in the Health SciencesWL09Online Teaching of Advanced Statistics Courses Usha Govindarajulu, SUNY DownstateWith the advent of online avail-ability, more students would like ma-terials online, even during a regular semester. How should one handle teaching health statistics as a hybrid or mostly online format, and what are the consequences of doing so vs. the old-fashioned way of having them take notes, especially for advanced statistics courses? Should you put all your equations on slides? What if you are used to writing on a blackboard? Should statisticians push back on the ad-ministration about having advanced courses online?

Continental Breakfast: House-made pastries; seasonal fruit; Greek yogurt; and a cup of coffee, tea, or juice.


P.M. ROUNDTABLE DISCUSSIONS Wednesday, August 3, 2016

12:30 p.m. – 1:50 p.m.

$45 (includes meal)

Section on Health Policy StatisticsWL10Advances in Mental Health Measurement Robert Gibbons, The University of Chicago

Mental health measurement has been based primarily on subjective judg-ment and classical test theory. Impairment is determined by a total score, requiring that all respondents be administered the same items. An alterna-tive is adaptive testing in which different individuals may receive different scale items targeted to their specific impairment level. We have developed adaptive depression, anxiety, and mania tests based on multidimensional item response theory. The shift in paradigm is from small fixed-length tests with questionable psychometric properties to large item banks from which an optimal small subset of items is adaptively drawn for each individual, targeted to their level of impairment. Results to date reveal remarkable in-creases in precision of measurement and dramatic decreases in patient bur-den. For example, depressive severity can be measured using an average of only 12 items in 2 minutes anywhere on the planet from a bank of 400 items, yet maintains a correlation of r=0.95 with the 400 item scores. Ap-plications to psychiatric epidemiology, genetics, global health, large-scale screening, and assessment across the lifespan will be discussed.

WEDNESDAY—SPEAKER WITH LUNCH

Biopharmaceutical SectionWL11Consulting: Building Relationships and Skills Jason Connor, Berry ConsultantsWe’ll discuss how consultants can build a professional relationship as well as deliver a solution to a problem. Those new to the field of consulting should attend with ques-tions and be prepared to think about their future in the world of consulting.

WL12Best Practices for Discussing/ Negotiating Endpoints, Hypoth-eses, Sample Size, and Other Study Design Aspects of Clinical Studies with FDA Reviewers Jennifer Mischke, NAMSAStatistical components of study designs are often among the most

challenging discussions between a sponsor and the FDA review team. We’ll talk about how the FDA is focusing on customer service, strong communication, and a bal-ance of data. We will also discuss how understanding these initiatives, having a solid strategy for present-ing and discussing statistical issues, and best practices for conducting oneself during communications with the FDA review team can streamline negotiations with the FDA and, ultimately, grant patients access to safe therapies as soon as possible.

WL13Challenges Implementing CDISC William Coar, Axio Research Data in CDISC format will soon be required for all regulatory submis-sions. The focus of this discussion is to highlight some of the challenges and discuss potential solutions to challenges associated with imple-menting CDISC standards.

Section on Statistical ComputingWL14Computational Challenges in Neuroimaging Data Ana-Maria Staicu, North Carolina State UniversityThe focus of this roundtable will be the trade-off between computa-tional and statistical efficiency in the analysis of neuroimaging data.

Section on Statistical Consulting WL15Building a Successful Private Practice from the Ground Up Kimberly Love, K. R. Love Quantitative Consulting and CollaborationWe’ll discuss the elements of starting one’s own private practice, the

legal aspects, creating a web and social media presence, determining services, developing and retaining a client base, managing finances and creating a network of trusted collaborators, and maintaining a life/work balance.

Section on Statistical EducationWL16Modern Teaching Methods for Graduate Statistics Courses Jana Anderson, Colorado State UniversityThis roundtable is an opportunity for faculty members to discuss dif-ferent teaching methods and their effectiveness for different types of graduate-level statistics courses.

WL17Training Statisticians to be Effective InstructorsJennifer Kaplan, University of GeorgiaWe will discuss potential programs for training PhD students in statistics to be effective classroom teachers. Participants in departments both with and without such programs are invited to share examples and needs, respectively. If there is interest, a discussion of training programs for early career and/or established faculty will also be discussed.

WL18Community Engagement in the Classroom and as a Conduit to Published Research Amy Phelps, Duquesne UniversityJoin others to talk about their CE projects in the classroom and ways we can position student learning and community research results into publishable works.


Wednesday’s Lunch“Black and Bleu” salad consisting of tender butter lettuce, crisp iceberg lettuce, herb-marinated flat iron steak, and Wisconsin bleu cheese; rolls and butter; choice of iced tea, hot tea, or coffee; and dessert. Chef’s choice of vegetarian menu.

WL19Creating a Course on Statistical Learning Sam Behseta, California State University at FullertonWhat are the predominant challeng-es in establishing a single semester course on statistical learning at the upper-division undergraduate or first-year masters’ level? This roundtable considers what topics should be given more weight in the syllabus; how the instructor should design the course if the students come from a diverse academic background; how much of the emphasis should be on theory, programming, or case-studies; and more.

Section on Statistics in EpidemiologyWL20Strategies for Becoming an Effective Statistical Leader of Interdisciplinary Research Teams Renee Moore, Emory UniversityWe will discuss key elements for effectively enhancing interdisciplin-ary experiences by considering both the statistical and nonstatistical elements of these interactions. In ad-dition, we will discuss key elements of successful leadership, including how to address common challeng-es that arise from being part of an interdisciplinary research team.

Government Statistics SectionWL21Prospects for Using Commercial Data for Federal StatisticsZachary SeeskinThis discussion will focus on the specific example of using property tax data available from CoreLogic, Inc. to explore the promise and challenges of using commercial data for federal statistics. We will address the potential value of the CoreLogic data, and commercial data more generally, to reduce respondent burden, study response error, and adjust estimates for nonresponse.

Section on Statistics in MarketingWL22Segmentation Analysis in Market Research Joseph Retzer, ACT Market Research SolutionsAt this roundtable we will discuss various approaches and software useful for implementing segmenta-tion analyses in market research.

Mental Health Statistics SectionWL23Efficient Handling of Binary and Continuous Missing Data in Hierarchical Models Yongyun Shin, Virginia Commonwealth UniversityWe will discuss how to efficiently analyze a hierarchical model given a mixture of continuous and binary data assumed missing at random by maximum likelihood and multiple imputation. Applications include a growth curve model for longitudinal data and a hierarchical generalized linear model for binary outcomes.

Section on Physical and Engineering SciencesWL24Online Experimentation: What It Is and Why It Is So Important Peter Qian, University of Wisconsin-MadisonWe will discuss methods and various applications of online experiments.

Section for Statistical Programmers and AnalystsWL25Is Your Mixed Model Analysis Mixed Up? Phil Gibbs, SASInherently, mixed modeling is computationally intensive and nu-merically tricky. Real-life data rarely works out as well as examples in text books and software manuals. The analysis of split-plot experimen-tal designs and other mixed models often leaves the analyst dazed and confused. Default algorithms

in software might fail to converge for some data sets and models, or might converge to fits that don’t make statistical sense (e.g., nega-tive variance components). In this roundtable, we will discuss experi-ences with these kinds of problems and more.

Quality and Productivity SectionWL26Postdocs in Statistics: No Longer the Unicorn Karl PazdernikWe will discuss the challenges and benefits of being a postdoc both in general and in statistics specifi-cally. Some of the tips and tricks to survival will also be discussed.

Survey Research Methods SectionWL27Field Observation of Survey Data Collection: Experiences and Lessons Learned John Eltinge, Bureau of Labor StatisticsIn this roundtable, participants will be invited to address the follow-ing questions: Does your orga-nization use field observation of data-collection processes? What are the primary goals for your field-observation work? What do you watch and listen for during field-observation work? What are important “lessons learned” your organization has gleaned from field observations?

Section on Statistics and the EnvironmentWL28Statistical Issues in Climate Science Michael Stein, The University of ChicagoWe will discuss the role of statistics and statisticians in all aspects of climate science, from the technical to the political and anything in be-tween that interests the participants.

To view complete round-table descriptions, visit www.amstat.org/meetings/jsm/2016.


Professional Development (PD) is a fundamental component of the professional life of statisti-cians, and it increases the value of their contributions to society. PD is the process of improving and broadening the knowledge, skill, and personal qualities needed to be success-ful in the practice of statistics.

To complement the ASA’s current Continuing Education program, a Personal Skills De-velopment program of courses, workshops, and other training has been developed to meet the needs of members under the ASA Professional Develop-ment umbrella.

PROFESSIONAL DEVELOPMENT

Continuing Education offerings consist of courses and Com-puter Technology Workshops in statistical methodology and practice. Courses are offered in two-day, one-day, and half-day formats Saturday through Tuesday. The ASA provides beverages for mid-morning and mid-afternoon breaks. Com-puter Technology Workshops are offered in two-hour intervals on Wednesday.

Personal Skills Development consists of courses, workshops, and panel discussions on top-ics such as effective communi-cation, collaboration, leader-ship, and influence.

RegistrationTo participate, you must reg-ister for JSM. Lower rates are given to those adding courses and workshops to their registra-tion from May 2 to June 30. Registration depends on seat availability and will be han-dled on a first-come, first-served basis. If seats are available after July 21, onsite registration will be offered.

Course Participation CertificatesThe ASA provides course participation certificates upon request to those who attend an entire course (certificates are not available for Computer Technol-ogy Workshop attendees). Cer-tificates are available to pick up from the course monitor at the conclusion of the course.

Excellence-in-CE AwardCourses that exceed expecta-tions in quality, content, and presentation are recognized with the Excellence-in-CE award from the Advisory Committee on Continuing Education.

DiscountPStat®, GStat, and A.Stat accredited members in good standing with the ASA or SSC will receive a 20% discount on Professional Development courses and workshops.

PR

OFESSIONAL

DEVELOPMENT


M=MEMBERNM=NONMEMBERS=STUDENT(Price in parentheses is for after June 30.)

SATURDAY JULY 30CE_01C (two-day course)8:30 a.m. – 5:00 p.m.Introduction to Bayesian Methods, Computation, and ModelingInstructor(s): Joseph Ibrahim

This is an introductory course in Bayesian modeling and com-putational methods. We will examine the fundamentals of the Bayesian paradigm, including Bayes theorem, deriving posterior distributions, point estimation, interval estimation, hypothesis testing, and model selection. We will discuss Bayesian methods for linear models, generalized linear models, models for longitu-dinal data, and survival models. Bayesian computational methods also will be discussed, including Gibbs sampling and Metropolis sampling. Various case studies and data sets will be discussed in detail using statistical packages such as SAS, WinBUGS, and R. On the second day, advanced topics will be discussed, including hierarchical modeling, missing data, variable selection, prior elicitation, and Bayesian methods for clinical trial design.

FEES: M - $660 ($895) NM - $805 ($1,090) S - $380 ($515)

CE_02C8:00 a.m. – 12:00 p.m.Best Practices in Data Visualization: Present Your Data Clearly, Accurately, and AttractivelyInstructor(s): Teresa Larsen

Do you get questions about your data after you think you presented them clearly? Do people’s eyes

glaze over when you show them your results? In this presentation, learn how to apply simple guide-lines to improve how you display your statistical data (results) visually. No prerequisite is required.

FEES: M - $240 ($325) NM - $315 ($420) S - $145 ($195)

CE_03C8:30 a.m. – 5:00 p.m.Applied Longitudinal AnalysisCosponsor: Biometrics SectionInstructor(s): Garrett Fitzmaurice

The goal of this course is to provide a broad introduction to statistical methods for analyzing longitudinal data. The emphasis is on the practi-cal aspects of longitudinal analysis. The course begins with a review of established methods for longitudinal data analysis when the response of interest is continuous. A general introduction to linear mixed effects models for continuous responses is presented. Next, we discuss how smoothing and semiparametric regression allow greater flexibil-ity for the form of the relationship between the mean response and covariates. We demonstrate how the mixed model representation of penalized splines makes this extension straightforward. Finally, we highlight the main distinctions between marginal models and generalized linear mixed models and discuss the types of scientific questions addressed by each.

FEES: M - $380 ($515) NM - $505 ($680) S - $230 ($310)

CE_04C8:30 a.m. – 5:00 p.m.A Primer to Web Scraping with RInstructor(s): Simon Munzert

We will learn about the basics

of web data collection practice with R. The sessions are hands-on; we will practice every step of the process with R using various examples. We will learn how to scrape content from static and dy-namic web pages, connect to APIs from popular web services such as Twitter to read out and process user data, and set up automatically working scraper programs. This course assumes prior experience using R. Please bring a laptop with the latest version of R and RStudio installed. You’ll be able to down-load accompanying slides, code, and data during the course.

FEES: M - $400 ($535) NM - $525 ($700) S - $250 ($330)

CE_05C8:30 a.m. – 5:00 p.m.Advanced Topics in Survey SamplingCosponsor: Survey Research Methods SectionInstructor(s): Jae-kwang Kim and Wayne Fuller

The proposed course is designed for researchers and practitioners interested in advanced techniques and the theory underlying those techniques. The topics include as-ymptotic theory in survey sampling, regression estimation, optimality in estimation and design, and use of models with survey samples. Partici-pants should have a background in survey sampling and statistical theory. Recent graduates working in survey sampling and graduate students are encouraged to attend. The book Sampling Statistics (2009) by Wayne Fuller is the text for the course.

FEES: M - $380 ($515) NM - $505 ($680) S - $230 ($310)



CE_06C8:30 a.m. – 5:00 p.m.Statistical Analysis of Financial Data with RCosponsor: Business and Economic Statistics SectionInstructor(s): David Matteson and David Ruppert

This course will introduce statisti-cal methods for the analysis of financial data. Examples and case studies will illustrate the application of these methods using the freely available software language R and numerous contributed packages. The first half of the course will include assessing departures from normality, modeling univariate and multivariate data, copula models, and tail dependence. The second half will provide an introduction to univariate and multivariate time series modeling, including autoregressive moving average (ARMA), generalized autoregres-sive conditional heteroscedastic (GARCH), and stochastic volatility (SV) models. The prerequisites are knowledge of calculus, vectors, and matrices; probability mod-els; mathematical statistics; and regression at the level typical of third- or fourth-year undergraduates in statistics, mathematics, engineer-ing, and related disciplines. Prior experience using R is helpful, but not necessary.

FEES: M - $380 ($515) NM - $505 ($680) S - $230 ($310)

CE_07C1:00 p.m. – 5:00 p.m.Introduction to Bayesian Inference with Stan and RInstructor(s): Eric Novik and Benjamin Goodrich

Thousands of users rely on Stan for statistical modeling, data analy-sis, and prediction in the social, biological, and physical sciences,

engineering, and business. Stan is a probabilistic programming language for expressing essen-tially any empirical model that is a differentiable function of the unknown parameters, which can then be estimated using one of the algorithms in the Stan Library. In particular, the Stan Library includes the most advanced implementation of Hamiltonian Monte Carlo, which allows researchers to efficiently draw from the posterior distribution of the unknown parameters given the known data. Alternatively, the mode of the posterior distribution can be found using conventional optimization algorithms. This talk will focus on the R interface to Stan and demonstrate how several popular regression models can be estimated using pre-written Stan code and will briefly outline how more complicated econometric models could be written using the Stan language.

FEES: M - $240 ($325) NM - $315 ($420) S - $145 ($195)

SUNDAY JULY 31CE_01C (two-day course)8:30 a.m. – 5:00 p.m.Introduction to Bayesian Methods, Computation, and ModelingInstructor(s): Joseph Ibrahim

CE_08C8:00 a.m. – 12:00 p.m.An Example-Driven Hands-On Introduction to RCPP Instructor(s): Dirk Eddelbuettel

The focus of this workshop is applying Rcpp to extend R and ac-celerate execution via simple C++ functions. It aims to enable R users to deploy Rcpp for both one-off tasks and experiments implemented in C++. First, we will discuss how to compile simple C++ functions containing just a few lines. This ensures the working environment is set up correctly and provides famil-iarity with the toolchains. Next, we will cover simple package building. This process is aided by environ-ments such as RStudio and helper functions. Time permitting, we will study a recent, simple, small-enough yet complete and meaning-ful package deploying Rcpp in detail. Knowledge of R and general programming is helpful, as isprior C or C++ knowledge.

FEES: M - $240 ($325) NM - $315 ($420) S - $145 ($195)

CE_09C8:30 a.m. – 5:00 p.m.Regression Modeling StrategiesCosponsor: Biometrics SectionInstructor(s): Frank Harrell

This course provides methods for estimating the shape of the



relationship between predictors and response using the widely applicable method of augmenting the design matrix using restricted cubic splines. Even when assump-tions are satisfied, over-fitting can ruin a model’s predictive ability for future observations. Methods for data reduction will be introduced to deal with the common case in which the number of potential predictors is large compared to the number of observations. Methods of model validation (bootstrap and cross validation) will be covered, as will auxiliary topics such as modeling interaction surfaces, efficiently using partial covariable data by using multiple imputation, variable selection, overly influential observations, collinearity, and shrinkage. The methods covered will apply to almost any regression model, including ordinary least squares, logistic regression models, ordinal regression, quantile regres-sion, longitudinal data analysis, and survival models.

FEES: M - $380 ($515) NM - $505 ($680) S - $230 ($310)

CE_10C8:30 a.m. – 5:00 p.m.Introduction to Statistical Learning for Unsupervised ProblemsCosponsor: Section on Statistical Learning and Data MiningInstructor(s): Ali Shojaie

This course will provide a practical introduction to statistical learning methods for unsupervised prob-lems. We will discuss three classes of methods: cluster analysis, dimen-sion reduction, and graphical modeling. Specifically, we will first discuss hierarchical and K-means clustering methods. Then, we will talk about principal component

To view complete course de-scriptions, visit www.amstat.org/meetings/jsm/2016.

analysis and multi-dimensional scal-ing as tools for reducing the ambi-ent dimension of the data. Finally, we will discuss sparse graphical models for analysis of high-dimensional data, including data from Gaussian and non-Gaussian distributions. Throughout, we will emphasize practical application of these methods, as well as their limi-tations in high-dimensional settings, including validation of results of unsupervised learning methods and tools for reproducible research. A number of case studies from finance and biology will be discussed to describe various statistical learning methods. The course will incorporate material from Elements of Statistical Learning by Hastie et al, Introduction to Statistical Learning by James et al, and instructor’s notes from two courses taught at the Summer Institute for Statistical Genetics.

FEES: M - $380 ($515) NM - $505 ($680) S - $230 ($310)

CE_11C8:30 a.m. – 5:00 p.m.Monte Carlo and Bayesian Computation with RInstructor(s): Jim Albert and Maria Rizzo This course describes the use of the statistical system R in Monte Carlo experiments, simulation-based infer-ence, and Bayesian computation. R tools are described for generat-ing random variables, computing criteria of statistical procedures, and replicating the procedure to compute quantities such as mean squared error and prob-ability of coverage. R commands for implementing simulation-based procedures such as bootstrap and permutation tests are outlined. The use of R in Bayesian computation is described, including the program-ming of the posterior distribution

and the use of different R tools to summarize the posterior. Special focus will be on the application of Markov chain Monte Carlo algorithms and diagnostic methods to assess convergence of the algo-rithms. It is assumed the participant will be familiar with the basics of the R system.

FEES: M - $380 ($515) NM - $505 ($680) S - $230 ($310)

CE_12C8:30 a.m. – 5:00 p.m.Managing Statistical Consulting Projects: Lessons from the FrontInstructor(s): Michael Greene and David Steier Using a framework for manag-ing analytics projects developed from our consulting experience, this tutorial aims to offer statistical professionals practical guidance on the process of evaluating, initiating, and delivering statistical consult-ing projects. It will draw on case studies from the following topics: starting an analytics conversa-tion—framing the conversation on analytics to improve decisionmaking and drive value; evaluating the cur-rent state—prioritizing effort based on systematic assessment against a data and analytics maturity model; planning and organizing analytics projects and programs—analytics project planning and organizational models for multi-disciplinary teams; choosing data, analytics techniques, and enabling technologies—using problem constraints to drive archi-tectural and algorithmic choices; managing delivery of analytics projects and programs—iterative prototyping and communication with stakeholders; and what to watch for—common warning signs and opportunities. The intended audience



is anyone undertaking a statistical consulting project starting from the ground up. Prior analytic project management experience is not required, but attendees are encour-aged to bring their own case studies and topics for discussion. This course won the Excellence-in-CE Award at JSM 2015.

FEES: M - $380 ($515) NM - $505 ($680) S - $230 ($310)

CE_13C1:00 p.m. – 5:00 p.m.Making Quantitative Deci-sions During the Clinical Development of a New DrugCosponsor: Biopharmaceutical SectionInstructor(s): Christy Chuang-Stein In this course, we will treat clinical trials as a series of diagnostic tests in which the goal is to estimate the likelihood that a drug has the desired profile. Treating a trial as a diagnostic test translates the concept of power and type-I error rate of the former to the sensitivity and 1-speci-ficity of the latter. Positive predictive value now refers to the probability that a new drug has the desired properties. This analogy facilitates formal incorporation of evidence from a previous trial into the design of a future trial and the subsequent decision criteria, allowing the formu-lation of go/no-go criteria that can address the unique needs of different stages of the clinical testing. Using the above analogy, we will discuss different metrics relevant to decision making at the proof-of-concept, dose-response, and confirmatory stages. We show how appropriate metrics may enable better decisions and illustrate several potential mis-takes trialists should guard against. Examples will be offered throughout the course.

FEES: M - $240 ($325) NM - $315 ($420) S - $145 ($195)

MONDAYAUGUST 1CE_14C8:00 a.m. – 12:00 p.m.Designs for Phase I Oncology TrialsCosponsor: Biometrics SectionInstructor(s): Nolan Wages and Alexia Iasonos

This course will cover dose-finding methodology for Phase I clinical trials in oncology, with a primary focus on model-based designs. Illustrations on how to use model-based methods—such as the continual reassessment method (CRM)—to design, implement, and carry out a Phase I trial in practice will be provided based on real oncology trials. Appropriate design specifications and components of protocol development will be discussed. Recent methodological developments for more advanced topics such as studies involving drug combinations and multiple treatment schedules will also be addressed. Model-based exten-sions for these clinical settings will be illustrated through currently ongo-ing trials. For both the simple and more complex settings, computa-tional considerations and available software will be reviewed.

FEES: M - $240 ($325) NM - $315 ($420) S - $145 ($195)

CE_15C8:00 a.m. – 12:00 p.m.Bootstrap Methods and Permutation Tests for Doing and Teaching StatisticsCosponsor: Section on Statistical EducationInstructor(s): Tim Hesterberg

Early in Stat 101, we teach that robustness is important. Yet later in the course, and too often in practice, we ignore those lessons and use simple means and least-squares regression together with

Normal-based inferences, even though the corresponding assump-tions are violated. Bootstrapping and permutation tests (BPT) let us check the accuracy of common procedures; they are surprisingly inaccurate in the presence of skew-ness. BPT offer better alternatives, but we need to know what we’re doing—the most common bootstrap methods are less accurate than a t-interval for small n. BPT let us more easily do inferences for a wider variety of statistics (e.g., trimmed means, robust regression) and data collected in a variety of ways (e.g., stratification). We’ll look at applica-tions from a variety of fields, includ-ing telecommunications, finance, and biopharm. BPT provide output we may graph in familiar ways (like histograms) to help students and cli-ents understand sampling variabil-ity, standard errors, p-values, and the Central Limit Theorem (CLT)—not just in the abstract, but for the data set and statistic at hand. This course is intended for teachers and practicing statisticians. No familiar-ity with these methods is assumed.

FEES: M - $240 ($325) NM - $315 ($420) S - $145 ($195)

CE_16C8:30 a.m. – 5:00 p.m.Analysis of Clinical Trials: Theory and ApplicationsCosponsor: Biopharmaceutical SectionInstructor(s): Devan Mehrotra, Alex Dmitrienko, and Jeff Maca

This course covers six important top-ics that commonly face statisticians and research scientists conducting clinical research: analysis of strati-fied trials, analysis of longitudinal data with dropouts, analysis of time-to-event data (with emphasis on small trials), crossover trials, multiple comparisons, and interim decision making and adaptive designs. The course offers a well-balanced mix of theory and applications. It presents practical advice from experts and


discusses regulatory considerations. The discussed statistical methods will be implemented using SAS and R software. Clinical trial examples will be used to illustrate the statistical methods. The course is designed for statisticians working in the pharma-ceutical or biotechnology industries and contract research organizations. It is equally beneficial to statisticians working in institutions that deliver health care and government branch-es that conduct health care–related research. Attendees are required to have basic knowledge of clinical trials. Familiarity with drug develop-ment is highly desirable, but not necessary. This course was taught at JSM 2005–2015 and received the Excellence in Continuing Education Award in 2005.

FEES: M - $380 ($515) NM - $505 ($680) S - $230 ($310)

CE_17C8:30 a.m. – 5:00 p.m.Successful Data Mining in PracticeCosponsor: Section on Statistical Learning and Data MiningInstructor(s): Richard De Veaux

This course is a practical introduction to and an overview of data mining. Many of the standard techniques of data mining—including modern regression methods (lasso, etc.), regression trees, neural networks, principal component regression, random forests, and boosting method—will be covered. The course will be problem solving–based, using real case studies from industry to illustrate which methods work well, when, and why. We will emphasize problem formulation, the challenges of the data, and the com-munication back to decision makers to effect maximum impact in the organization. No prerequisites other that a knowledge of the basics of regression are assumed. The appli-cations will come from a variety of industries and include some applica-tions from my personal experiences

as a consultant for companies that deal with such topics as financial services, chemical processing, phar-maceuticals, and insurance.

FEES: M - $380 ($515) NM - $505 ($680) S - $230 ($310)

CE_18C8:30 a.m. – 5:00 p.m.Semiparametric Regression with RCosponsor: Section on Nonparametric StatisticsInstructor(s): Jaroslaw Harezlak and Matt Wand

This short course explains the tech-niques and benefits of semipara-metric regression in a concise and modular fashion. Spline functions, linear mixed models, and Bayesian hierarchical models are shown to play an important role in semipara-metric regression. There will be a strong emphasis on implementation in R and rstan, with most of the course spent doing computing exer-cises. Attendees are encouraged to bring their laptops. This short course is based on the upcoming book Semiparametric Regression with R by Ruppert, Wand, and Harezlak.

FEES: M - $400 ($535) NM - $525 ($700) S - $250 ($330)

CE_19C8:30 a.m. – 5:00 p.m.Indirect Sampling and Hard-to-Reach PopulationsCosponsor: Survey Research Methods SectionInstructor(s): Pierre Lavallee

After an overview of indirect sampling and its ready applica-tion to hard-to-reach populations, the course will describe indirect sampling in the context of network sampling, adaptive cluster sampling, snowball sampling, respondent-driv-en sampling, and multiple frames.

FEES: M - $380 ($515) NM - $505 ($680) S - $230 ($310)

CE_20C1:00 p.m. – 5:00 p.m.Patient-Reported Outcomes: Measurement, Implementa-tion, and InterpretationCosponsor: Biometrics SectionInstructor(s): Joseph Cappelleri and Andrew Bushmakin

Key elements in the development of a patient-reported outcome (PRO) measure will be covered in this course. The core topics of validity and reliability of a PRO measure will be discussed. Exploratory and confirmatory factor analyses—tech-niques to understand the underlying structure of a PRO measure—will be described. The topic of media-tion modeling will be presented as a way to identify and explain the mechanism that underlies an observed relationship between an independent variable and a dependent variable via the inclu-sion of a third variable called the mediator variable. Also discussed will be longitudinal analysis and item response theory. Approaches to interpret PRO results will be elucidated to make results useful and meaningful. Illustrations will be provided mainly through real-life examples and simulated examples. No prerequisites.

FEES: M - $240 ($325) NM - $315 ($420) S - $145 ($195)

CE_21C1:00 p.m. – 5:00 p.m.Confidence Distribution: A New Statistical Inference Approach and Its Applica-tions in Meta-Analysis and Fusion LearningCosponsor: Section on Nonparametric StatisticsInstructor(s): Minge Xie and Regina Liu

We introduce the concept of confi-dence distribution (CD) and teach how to use its development to solve a wide range of problems in fusion learning and meta-analysis.


Specifically, we present several new and effective meta-analysis and fusion learning approaches from real data applications, includ-ing 1) unified framework for meta-analysis and R-package ‘gmeta’; 2) meta-analysis of heterogeneous studies; 3) incorporating external information in meta-analyses; 4) exact meta-analysis approach for discrete data; 5) robust meta-analy-sis; 6) efficient network meta-analy-sis; 7) meta-analysis with no model assumptions; and 8) nonparametric combining inferences. Altogether, they show that CD can yield useful statistical inference tools for many statistical problems where methods with desirable properties have been lacking or not easily available.

FEES: M - $240 ($325) NM - $315 ($420) S - $145 ($195)

TUESDAY AUGUST 2CE_22C8:00 a.m. – 12:00 p.m.Modeling Ordinal Categorical Responses, with Examples Using RInstructor(s): Alan Agresti

This course presents an overview of models for categorical response vari-ables that have a natural ordering of the categories. Topics to be covered include logistic regression models using cumulative logits with propor-tional odds structure, nonpropor-tional odds and partial proportional odds models, other ordinal logistic regression models such as using adjacent-categories logits, and other multinomial response models such as the cumulative probit. Examples presented include social survey data and randomized clinical trials and use R software, with emphasis on the VGAM package.

FEES: M - $240 ($325) NM - $315 ($420) S - $145 ($195)

CE_23C8:00 a.m. – 12:00 p.m.Statistical Analysis of Zero-Inflated Continuous DataCosponsor: Mental Health Statistics SectionInstructor(s): Lei Liu

We’ll review statistical methods to analyze zero-inflated continuous data. We’ll start from the cross-sec-tional zero-inflated continuous data, then look at modeling repeated measures zero-inflated continuous data. Finally, we’ll present applica-tions to real data sets to illustrate our methods. We’ll use alcohol drinking data and correlated medical costs as examples. SAS codes will be provided. Model comparison also will be conducted.

FEES: M - $240 ($325) NM - $315 ($420) S - $145 ($195)

CE_24C8:00 a.m. – 12:00 p.m.Data Analysis in the Presence of Competing RisksInstructor(s): Ronald Geskus

Competing risks are mutually exclu-sive event types. While a marginal analysis quantifies the occurrence of one specific event type over time for the situation that the competing risks were absent, a competing risks analysis considers each event type as a possible end point. We explain when a competing risks analysis is the appropriate approach, what quantities can be defined and esti-mated, and how the results can be interpreted. Basic knowledge of clas-sical survival analysis is assumed.

FEES: M - $240 ($325) NM - $315 ($420) S - $145 ($195)CE_25C8:30 a.m. – 5:00 p.m.Adaptive Methods for Modern Clinical TrialsCosponsor: Section on Bayesian Statistical Science

Instructor(s): Frank Bretz, Byron Jones, and Peter MuellerFrom the perspective of practical-ity, this one-day short course will introduce various adaptive methods for Phase I to Phase III clinical trials. Accordingly, different types of adaptive designs will be introduced and illustrated with case studies. This includes dose escalation/de-escalation and dose insertion based on observed data, adaptive dose-finding studies using optimal designs to allocate new cohorts of patients based on the accumulated evidence; population enrichment designs; early stopping for toxic-ity, futility, or efficacy using group-sequential designs; blinded and unblinded sample size re-estimation; and adaptive designs for confirma-tory trials with treatment or popula-tion selection at interim.

FEES: M - $380 ($515) NM - $505 ($680) S - $230 ($310)

PROFESSIONAL

DEVELOPMENT


CE_26C8:30 a.m. – 5:00 p.m.An Introduction to the Joint Modeling of Longitudi-nal and Survival Data, with Applications in RCosponsor: Biometrics SectionInstructor(s): Dimitris Rizopoulos

This course is aimed at applied researchers and graduate students and will provide a comprehensive introduction to this modeling frame-work. We’ll explain when these models should be used in practice, which are the key assumptions behind them, and how they can be used to extract relevant information from the data. This course assumes knowledge of basic statistical concepts such as standard statistical inference using maximum likelihood and regression models. Basic knowl-edge of R would be beneficial, but is not required.

FEES: M - $380 ($515) NM - $505 ($680) S - $230 ($310)

CE_27C8:30 a.m. – 5:00 p.m.A Statistical Approach to Machine Learning: Boosting, Nearest Neighbors, Random Forests, and Support Vector MachinesInstructor(s): Andreas Ziegler and Marvin Wright This course aims to provide an introduction to some of the most important machine learning ap-proaches currently used. We show that all problems from generalized linear models, and even survival endpoints, can be tackled with machine learning. The focus of the theoretical sessions is the nontech-nical, but intuitive, explanation of the algorithms and the focus of the hands-on laptop sessions is to see the machines operating using R. The combination of simple de-scriptions in a language familiar to

statisticians together with the use of standard statistical software should help to demystify machine learning.

FEES: M - $400 ($535) NM - $525 ($700) S - $250 ($330)

CE_28C1:00 p.m. – 5:00 p.m.Meta-Analysis: Combining the Results of Multiple StudiesCosponsor: Health Policy Statistics SectionInstructor(s): Christopher Schmid and Thomas Trikalinos

We’ll introduce the major prin-ciples and techniques of statistical analysis of meta-analytic data. Examples of published meta-anal-yses in medicine and the social sciences will be used to illustrate the various methods.

FEES: M - $240 ($325) NM - $315 ($420) S - $145 ($195)

CE_29C1:00 p.m. – 5:00 p.m.Statistical Analysis of Network DataInstructor(s): Eric Kolaczyk

This course will provide an over-view of foundational topics relevant to statistical analysis of network data across the disciplines. Mate-rial will be organized according to a statistical taxonomy, with presenta-tion entailing a conscious balance of conceptual and technical aspects. The course will be organized into roughly two halves of equal length. Topics will include manipulation, visualization, and descriptive analysis of network data. Then, the focus will shift to topics pertaining to statistical modeling and inference in network analysis. Examples of network analysis will be drawn from a variety of domain areas, with emphasis on computational

biology and neuroscience and on social networks.

FEES: M - $240 ($325) NM - $315 ($420) S - $145 ($195)

CE_30C1:00 p.m. – 5:00 p.m.Designing Observational Comparative Studies Using Propensity Score Methodol-ogy in Regulatory SettingsCosponsor: Section on Medical Devices and DiagnosticsInstructor(s): Donald B. Rubin and Lilly Yue

This course will introduce the causal inference framework and propensity score methods and highlight the importance of prospective design of observational comparative studies to increase the integrity and interpret-ability of outcome analysis results. Practical issues encountered in the application of the methodology in the regulatory settings will be present-ed, including study design process in regulatory submissions of drug, biologics, and medical devices for both pre-market and post-market stud-ies; specification of treatment effects of interest in treatment comparisons (average treatment effect (ATE) or av-erage treatment effect on the treated (ATT)); covariate identification and inclusion; control group selection/formation; sample size; and power consideration. Some differences for implementing propensity score methodology will be delineated for studies with different purposes, for regulatory submissions, or general comparative effectiveness research. Examples will be based on regulatory review experience. Prerequisite: familiarity with general statistical methods in clinical study design and analysis.

FEES: M - $240 ($325) NM - $315 ($420) S - $145 ($195)


WEDNESDAY AUGUST 3CE_31T8:00 a.m. – 9:45 a.m.Joint Modeling of Longitudi-nal and Survival-Time Data in StataInstructor(s): Yulia Marchenko

Many studies collect both longitudi-nal and survival-time data. Longitu-dinal, panel, or repeated-measures data record data measured repeatedly at different time points. Survival-time or event history data record times to an event of interest such as death or onset of a disease. The longitudinal and survival-time outcomes are often related and should thus be analyzed jointly. Three types of joint analysis may be considered: 1) evaluation of the effects of time-dependent covariates on the survival time; 2) adjust-ment for informative dropout in the analysis of longitudinal data; and 3) joint assessment of the effects of baseline covariates on the two types of outcomes. This workshop will provide a brief introduction into the methodology and demonstrate how to perform these three types of joint analysis in Stata. No prior knowledge of Stata is required, but familiarity with survival and longitu-dinal analysis will prove useful.

FEES: $55($70)

CE_32T8:00 a.m. – 9:45 a.m.Advanced ODS Graphics Examples in SASInstructor(s): Warren Kuhfeld

You can use SG annotation, modify templates, and change dynamic variables to customize graphs in SAS. Standard graph customiza-tion methods include template modification (which most people use to modify graphs that analyti-

COMPUTER TECHNOLOGY WORKSHOPScal procedures produce) and SG annotation (which most people use to modify graphs that procedures such as PROC SGPLOT produce). However, you can also use SG annotation to modify graphs that analytical procedures produce. You begin by using an analytical procedure, ODS Graphics, and the ODS OUTPUT statement to capture the data that go into the graph. You use the ODS document to capture the values of dynamic variables, which control many of the details of how the graph is created. You can modify the values of the dynamic variables, and you can modify graph and style templates. Then you can use PROC SGRENDER along with the ODS output data set, the captured or modified dynamic variables, the modified templates, and SG annotation to create highly customized graphs. This tutorial is based on the free web book at http://support.sas.com/documentation/prod-p/grstat/9.4/en/PDF/odsadvg.pdf. Prior experience with ODS Graph-ics is assumed.

FEES: $55($70)

CE_33T8:00 a.m. – 9:45 a.m.Introduction to Data Mining with CART Classification and Regression TreesInstructor(s): Kaitlin Onthank and Mikhail Golovnya

This tutorial is intended for the applied statistician wanting to un-derstand/apply CART classification and regression tree methodology. Concepts will be illustrated using real-world, step-by-step examples. The course begins with an intuitive introduction to tree-structured analy-sis: what it is, why it works, why it is nonparametric; model-free; and advantages in handling all types of data, including missing values

and categorical. Working through examples, we will review how to read the CART Tree output and set up basic analyses. This session includes performance evaluation of CART trees and covers ways to search for improved results. Once a basic working knowledge of CART has been mastered, the tutorial will focus on critical details for advanced CART applications, including choice of splitting criteria, choosing the best split, using prior probabilities to shape results, refin-ing results with differential misclassi-fication costs, the meaning of cross validation, tree growing, and tree pruning. The course concludes with discussion about the comparative performance of CART versus other computer-intensive methods such as neural networks and statistician-generated parametric models. At-tendees receive six months access to fully functional versions of the SPM Salford Predictive Modeler software suite.

FEES: $55($70)

CE_34T10:00 a.m. – 11:45 a.m.Bayesian Analysis Using StataInstructor(s): Yulia Marchenko

This workshop will demonstrate the use of Bayesian analysis in various applications and introduce Stata’s suite of commands for conducting Bayesian analysis. No prior knowl-edge of Stata is required, but basic familiarity with Bayesian analysis will prove useful.

FEES: $55($70)

CE_35T10:00 a.m. – 11:45 a.m.Small Area Estimation Using SAS SoftwareInstructor(s): Pushpal Mukhopadhyay

This workshop provides an over-view of small area models and


(Price in parentheses is for after June 30.)

introduces SAS strategies to fit them. You will learn the two types of basic small area models, unit-level and area-level, which are illustrated with practical examples. Then, you will learn how to use the GLIMMIX and IML procedures to fit these models and obtain small area predictions and mean squared error for predic-tions with appropriate adjustments for the number of areas. Fully Bayesian approaches are commonly employed for small area estima-tion, too. You will also learn how to use the MCMC procedure to fit hierarchical Bayes models for small area estimation.

FEES: $55($70)

CE_36T10:00 a.m. – 11:45 a.m.Applied Data Mining Analysis: A Step-by-Step Introduction Using Real-World Data SetsInstructor(s): Kaitlin Onthank and Mikhail Golovnya

In this presentation designed for statisticians, we will show how you can quickly and easily create data mining models. We will demonstrate with step-by-step instructions. We will use real-world data mining examples drawn from online advertising and the financial services industries. At the end of this workshop, our goal is for you to be able to build your own data-mining models on your own data sets. This tutorial follows a step-step approach to introduce advanced automation technology—including CART, MARS, TreeNet Gradient Boosting, Random Forests, and the latest multi-tree boosting and bag-ging methodologies by the original creators of CART (Breiman, Friedman, Olshen, and Stone). All attendees will receive six months access to fully functional versions of the SPM Salford Predictive Modeler software suite.

FEES: $55($70)

CE_37T1:00 p.m. – 2:45 p.m.Design Multi-Arm, Multi-Stage Trials with Treatment Selection and Sample Size Re-Estimation in EastInstructor(s): Cyrus Mehta and Lingyun Liu

There has been increasing interest in designing multi-arm, multi-stage trials with treatment selection and sample size re-estimation at interim analysis. Cytel has developed software to facilitate design, simula-tion, and monitoring of such trials in a streamlined manner. There are two main streams of statistical approaches to ensure strong control of type I error. One is the group sequential approach formulated in several publications (including Ma-girr et al 2012, Gao et al 2014). The other is based on the closed testing principle by combining the p-values from different stages as proposed by Posch et al 2005. We’ll review the theory behind these two approaches and demon-strate howt to design such trials in East through case studies.

FEES: $55($70)

CE_38T1:00 p.m. – 2:45 p.m.Weighted GEE Analysis Using SAS/STAT SoftwareInstructor(s): Michael Lamm

This workshop introduces the generalized estimating equation (GEE) procedure (new in SAS/STAT 13.2 release), which supports both the standard and weighted GEE methods for analyzing longitudinal data. You will learn about the dif-ferent mechanisms used to describe why a response is missing and how the missing data mechanism affects inference using the standard and weighted GEE approaches. This workshop illustrates the use of PROC GEE with examples and compares the methods available in

PROC GENMOD and PROC GEE. A basic familiarity with generalized linear models is assumed.

FEES: $55($70)

CE_39T1:00 p.m. – 2:45 p.m.Evolution of Classification: From Logistic Regression and Decision Trees to Bagging/Boosting and Net-lift ModelingInstructor(s): Mikhail Golovnya and Dan Steinberg

We will discuss recent improve-ments to conventional decision tree and logistic regression technology via two case study examples: one in direct marketing and the second in biomedical data analysis. Within the context of real-world examples, we will illustrate the evolution of classification by contrasting and comparing regularized logistic regression, CART, random forests, TreeNet stochastic gradient boost-ing, and RuleLearner. All attendees will receive six months access to fully functional versions of the SPM Salford Predictive Modeler software suite.

FEES: $55($70)

CE_40T3:00 p.m. – 4:45 p.m.Software for Designing Dual-Agent Phase 1 TrialsInstructor(s): Charles Liu and Hrishikesh Kulkarni

East ESCALATE is a widely used tool for simulating and analyzing Phase 1 dose escalation trials. Single-agent designs in this module include the 3+3, the modified Toxicity Probability Interval (mTPI) method, the Continual Reassess-ment Method (CRM), and the Bayesian Logistic Regression Model (BLRM). Two new methods recently introduced in the software are for dual-agent designs: (1) an exten-sion of the BLRM and (2) a product


of independent beta probabilities escalation (PIPE) design. We’ll begin by reviewing the underlying methodology of these new designs, and then present case studies using the software to inform the best choice of design parameters.

FEES: $55($70)

CE_41T3:00 p.m. – 4:45 p.m.Current Methods in Survival Analysis Using SAS/STAT SoftwareInstructor(s): Changbin Guo

After reviewing basic concepts of survival analysis, this tutorial intro-duces two new procedures in SAS/STAT for the analysis of interval-cen-sored data: PROC ICLIFETEST and PROC ICPHREG. The tutorial demon-strates how to use these procedures for estimation and comparison of survival functions and proportional hazards regression. The tutorial then

CE_43P (spans two days)

Part I: Saturday, July 308:00 a.m. – 12:00 p.m.

Part II: Sunday, July 311:00 p.m. – 5:00 p.m.

Nontechnical Skills to Become a More Effective CollaboratorInstructor(s): Eric Vance, Heather Smith, and Doug Zahn

When working with clients and colleagues some statisticians are limited, not by their technical skills, but by their communication skills. This practical workshop will help you become a more effective statistician when working collab-oratively to solve problems and implement solutions. In the first part

turns to the analysis of competing risks data and explains how to use the LIFETEST procedure to conduct nonparametric survival analysis and the PHREG procedure to investi-gate the relationship of covariates to cause-specific failures. A basic understanding of applied statistics is assumed.

FEES: $55($70)

CE_42T3:00 p.m. – 4:45 p.m.Improve Your Regression with Modern Regression Analysis Techniques: Linear, Logistic, Nonlinear, Regularized, GPS, LARS, LASSO, Elastic Net, MARS, TreeNet Gradient Boosting, Random ForestsInstructor(s): Mikhail Golovnya and Dan Steinberg

Using real-world data sets, we will demonstrate advances in nonlinear, regularized linear, and logistic

regression. This workshop will introduce the main concepts behind Leo Breiman’s Random Forests and Jerome Friedman’s GPS (General-ized Path Seeker), MARS (Multivari-ate Adaptive Regression Splines), and Gradient Boosting. With these state-of-the-art techniques, you’ll boost model performance without stumbling over confusing coef-ficients or problematic p-values! All attendees will receive six months access to fully functional versions of the SPM Salford Predictive Modeler software suite.

FEES: $55($70)

PERSONAL SKILLS DEVELOPMENT

of the workshop you learn about and practice how to structure and con-duct effective, efficient collaboration meetings. The second part will guide you through key communication skills essential for success as a statistician. Throughout the workshop you will practice these non-technical skills in groups. You will also learn how to analyze video data taken during meetings with clients to systematically improve your collaboration skills. The methods you learn and practice in this workshop will assist you in your efforts to improve your effectiveness as you serve in all of your profes-sional roles.

FEES: M - $380 ($515) NM - $505 ($680) S - $230 ($310)



CE_44P (spans two days)

Part I: Saturday, July 301:00 p.m. – 6:30 p.m.

Part II: Sunday, July 318:00 a.m. – 12:00 p.m.

Preparing Statisticians for Leadership: How to See the Big Picture and Have More InfluenceInstructor(s): Gary R. Sullivan, Vaneete Kaur Grover, and Matthew James Gurka

This course provides an under-standing of leadership and how statisticians can improve and demonstrate leadership to affect their organizations. It features leaders from all sectors of statistics speaking about their personal journeys and provides guidance on personal leadership development with a focus on the larger organiza-tional/business view and influence. Course participants work with their colleagues to discuss and resolve leadership situations that statisti-cians face. Participants will come away with a plan for developing their own leadership and connect with a network of statisticians who can help them move forward on their leadership journey.

FEES: M - $380 ($515) NM - $505 ($680) S - $230 ($310)

CE_45PSunday, July 312:00 p.m. – 4:00 p.m.

Career Development: Power Careers in StatisticsCosponsor: Committee on Career DevelopmentPanelists: Joan Chmiel, Sou-Cheng Choi, Larry Hedges, Stephen Goranson, and Yijie Zhou

How do you power your career? What makes a career powerful? The ASA Committee for Career Development is aware that under-graduate and graduate students, as well as faculty in statistics or related departments, are interested in exploring potential career opportu-nities both in and outside academia (e.g., industry, government, research organizations). How do statisticians make a difference in the world? Choosing from an ever-wid-ening menu of career choices can be daunting. This is also true for experienced statisticians who are in various stages of their careers and contemplating switching jobs and/or application domains. The committee also recognizes there are many statisticians starting from a master’s or bachelor’s degree and that not everyone will or will want to find a job in a large multinational company. What are the build-ing blocks for a career, beyond finding a starting position? What do employers look for to maximize impact within their organizations? The goal of this panel discussion is to help address practical questions like these and thereby help session attendees with their career planning and development. All the panelists are rising or established leaders in their respective organizations. Their backgrounds and experiences cover a diverse array of statisti-cal applications, which they will describe in their short presentations prior to the panel discussion with the audience.

FREE EVENT.

CE_46P (spans two days)Part I: Monday, August 18:00 a.m. – 12:00 p.m.

Part II: Tuesday, August 21:00 p.m. – 5:00 p.m.

Effective Presentations for Statisticians: Success = (PD)2Instructor(s): Jennifer van Mullekom and Stephanie P. DeHart

This short course, developed and taught by statisticians, will provide an opportunity to learn how to em-ploy different methods and tools in the phases of the Success = (PD)2 framework. The material covered is geared toward scientific presenta-tions and based on the works of Garr Reynolds and Michael Alley, among others. The course will em-phasize the importance of stepping away from the computer to prepare an effective message aimed at your core point guided with a series of questions and tips. The design phase emphasizes the importance of structure, streamlining, and good graphic design accompanied by a series of checklists. Of course, practice makes perfect, so we can-not skip this step. Finally, engag-ing the audience and effectively using the room and equipment is covered in the deliver phase and is complemented with a handy list of dos and don’ts.

FEES: M - $380 ($515) NM - $505 ($680) S - $230 ($310)

To view complete Professional Development course descriptions, visit www.amstat.org/meetings/jsm/2016.


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GREEK TOWN

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W. Jackson Blvd.

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W. Adams St.

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E. Jackson Blvd.

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E. Balbo Ave.

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Millennium Park

The Field Museum

John G. Shedd Aquarium

Willis Tower

McCormick Place West

Sheraton Grand Chicago

Hilton Chicago (HQ)

SHUTTLEbus stop

Conference Chicago at the University Center

Palmer House

Hyatt McCormick Place

Hostelling International Chicago

SHUTTLEbus stop

The Art Institute of Chicago

Union Station

HOTELS

SHUTTLE


GENERAL HOUSING INFORMATION

All hotel rooms are subject to applicable taxes (currently 17.4%, but subject to change)

RATES SINGLE/DOUBLE

TRIPLE/QUAD

U.S. GOVERNMENT

Hilton Chicago Hotel (HQ) $239 $259/$279 $200

Palmer House $239 $259/$279 $200

Hyatt Regency McCormick Place $239 $264/$289 $200

Sheraton Grand Chicago $229 $259/$289 $200

HOUSING

DEADLINE

IS JUNE 29

Economy Housing

Conference Chicago at the University Center525 South State Street, Chicago, IL 60605 [email protected]

A limited number of beds will be available July 30–August 4. Visit www.chicagosummerhousing.com to check avail-ability and rates and make reservations. Please use promo code JSM16.

Reservations must be made with Conference Chicago at the University Center directly. We regret that the ASA cannot make or accept housing reservations.

Hostelling International Chicago at the J. Ira and Nicki Harris Family HostelBook your stay online at www.hichicago.org or by phone at (312) 425-4312. Please use the code JOINT.

All rates include blankets, linens, towels, and a free con-tinental breakfast each morning served from 7–10 a.m. Check-in time is normally 3:00 p.m. to 3:00 a.m.

A valid government ID is required at check-in for all government-rate rooms.

SHUTTLE INFORMATIONShuttle service will be provided between McCormick Place and the Hilton Chicago and Palmer House hotels.

No shuttle service will be provided for the Hyatt Re-gency McCormick Place (connected to the conven-tion center) or from the Sheraton Grand Chicago.

If you have questions about the shuttle or need to make an advanced reservation for a wheelchair-accessible shuttle, call Kushner & Associates at (310) 274-8819, Ext. 213.

Visit www.amstat.org/meetings/jsm/2016 for shuttle schedule and boarding locations.

SHUTTLE SCHEDULESaturday, July 30: 7:15 a.m. – 6:15 p.m.Sunday, July 31: 7:00 a.m. – 8:30 p.m.Monday, August 1: 6:30 a.m. – 6:00 p.m. Tuesday, August 2: 6:30 a.m. – 6:00 p.m. Wednesday, August 3: 6:30 a.m. – 5:30 p.m. Thursday, August 4: 7:00 a.m. – 1:00 p.m.


REGISTRATION INFORMATION

Cancellations/Substitutions/RefundsAll cancellations and substitutions must be submitted in writing. Email: [email protected]; Fax: (703) 684-2037; Mail: JSM Registra-tion, ASA, 732 N. Washington St., Alexandria, VA 22314-1943

Registration fees for participants (speakers/panelists/discussants/chairs/organizers/poster present-ers) are nonrefundable. Substitu-tions may be made at no penalty.

For general registrations and add-on items:

Cancellations received by 5:00 p.m. EDT on June 1, 2016, incur a cancellation fee of 20% of each item cancelled.

Cancellations received by 5:00 p.m. EDT on July 14, 2016, incur a cancellation fee of 40% of each item cancelled.

Cancellations received after 5:00 p.m. EDT on July 14, 2016, will not be refunded.

Make sure to read the Meetings Conduct Policy

at www.amstat.org/ jsmregistration.

THREE WAYS TO REGISTEROnline www.amstat.org/jsmregistration

Mail JSM Registration, 732 North Washington St., Alexandria, VA 22314-1943

Fax (703) 684-2037 (Please fax both sides of form.)

What Can I Do with My Registration? Conference Registrant Guest

Program Book and Conference Bag x

Technical Sessions x

Exhibit Hall x x

Sunday Opening Mixer x x

Tuesday Night Dance Party x x

JSM Proceedings (available online in early 2017) x

Professional Development Offerings $$

Roundtables & Speakers with Lunch $$

Career Service $$

PStat®, GStat, and A.Stat. Discounts on Professional Development OfferingsAccredited members in good standing with the ASA or SSC will receive a 20% discount on Professional Development (PD) courses and workshops. To take advantage of this discount when registering by fax or mail, check the appropriate box in the PD sec-tion indicating your accreditation and calculate your discount where asked. To take advantage of this discount when registering online, select the registration level that con-tains “accredited.” Your discount will be calculated automatically.

PaymentPayment via credit card, check, or money order must accompany reg-istration. We are unable to accept purchase orders. Make your check or money order payable to Ameri-can Statistical Association in U.S. funds drawn on a U.S. bank. The ASA Federal ID is 53-0204661.

Registration ConfirmationsRegistration confirmations will be emailed to all preregistered attend-ees as soon as the registration and payment are processed. Be sure to provide a valid email address and set your spam-blocking filters to allow emails sent from addresses containing “@amstat.org.”

Register by fax: (703) 684-2037 or mail: 732 N. Washington St., Alexandria, VA 22314-1943. Registrations are not accepted by telephone or email.

JSM 2016 REGISTRATION FORM

ASA ID# (if known): ___________________________________________________________________________________________________

First Name_________________________________ Middle Initial_____ Last/Family Name________________________________________

Badge Name (if different from First Name)_______________________________________________________________________________

Company/Organization________________________________________________________________________________________________

Address______________________________________________________________________________________________________________

City________________________________________________________ State/Province__________ ZIP/Postal Code___________________

Country (Non-U.S.)_____________________________________________________________________________________________________

Phone______________________________________ Email____________________________________________________________________

In case of emergency, list the name and phone number of the person we should contact (remains confidential).

Emergency Contact’s Name____________________________________________________________ Phone_________________________________

Membership(s): (check all that apply)

q ASA q ENAR q ICSA q IISA q IMS q ISBA q ISI q KISS q RSS q SSC q WNAR

MEETING REGISTRATION FEES All fees are in U.S. dollars (mark the appropriate box).

Early May 2–June 1

Regular June 2–30

Late July 1–21

Member ♦ q $430 q $475 q $525

New ASA Member ♦♦ q $560 q $605 q $655

Nonmember q $655 q $725 q $800

Student Member ♦ q $105 q $105 q $105

K–12 Teacher q $ 80 q $ 80 q $ 80

Senior Member ♦ q $185 q $185 q $185

MEETING REGISTRATION FEE $________

ADD-ONS (see reverse side)

TOTAL Professional Development Cost $________

TOTAL Roundtable/Speaker Cost $________

TOTAL Guest Cost $________

TOTAL Career Service Cost $________

TOTAL REGISTRATION + ADD-ONS $________

CREDIT CARD OR CHECK PAYMENT INFORMATION (NOTE: We are unable to accept purchase orders as payment.)q Check or money order enclosed payable to American Statistical Association (U.S. funds on a U.S. Bank)Credit Card q Amex q Discover q MasterCard q VISA

Card Number________________________________________________________ Expiration Date____ /____ Security Code__________________

Name of Cardholder________________________________________________________________________________________________________

Cardholder’s Signature_______________________________________________________________________________________________________

See Page 38 for cancellation policy.

REGISTRATION INFORMATION

♦ Must have an active membership in one of the sponsoring societies and indicate it on your registration where asked ♦♦ Includes discounted first-year ASA dues; not available to renewing or recently lapsed members

SOCIAL EVENTS

For STUDENT MEMBER REGISTRANTS ONLY:

q YES! I will attend the Student Mixer on Monday, August 1, at 6:00 p.m.

For PSTAT®/GSTAT:

q YES! I will attend the ASA PStat®/GStat Reception on Wednesday, August 3, at 6:00 p.m.

CHECK ALL THAT APPLY:q I am a participant (speaker/panelist/discussant/chair/organizer/poster presenter).

q I am a first-time JSM attendee.

q I have a disability that requires special services (attach a statement of your needs). We cannot guarantee an accommodation that is not made during early registration or regular registration.

q Update my ASA customer information with this contact information.

q Exclude my information from contact lists managed by the ASA for use by outside entities, including offers for onsite receptions or activities and booth giveaways.

q Exclude my name from the conference attendee roster that will appear on the conference website.

P.M. ROUNDTABLES$45 each; includes meal. Indicate your first and second choices by marking 1 and 2.

Sunday July 31

Monday August 1

Tuesday August 2

Wednesday August 3

SL01___ ML09____ TL10____ WL10____

ML10____ TL11____ WL11____

ML11____ TL12____ WL12____

ML12____ TL13____ WL13____

ML13____ TL14____ WL14____

ML14____ TL15____ WL15____

ML15____ TL16____ WL16____

ML16____ TL17____ WL17____

ML17____ TL18____ WL18____

ML18____ TL19____ WL19____

ML19____ TL20____ WL20____

ML20____ TL21____ WL21____

ML21____ TL22____ WL22____

ML22____ TL23____ WL23____

ML23____ TL24____ WL24____

ML24____ TL25____ WL25____

ML25____ TL26____ WL26____

ML26____ TL27____ WL27____

ML27____ TL28____ WL28____

TL29____

MEAL CHOICE: q Regular q Vegetarian

TOTAL ROUNDTABLES/ SPEAKER COST $_______

GUEST BADGES $60 per guest.Enter names below. Fee includes Sunday Opening Mixer, Tuesday Night Dance Party and entrance into exhibit hall. Session attendance is not included.

I am PStat®, GStat, or A.Stat. accredited by: q ASA q SSCAccredited members of the ASA or SSC (PStat®, GStat, A.Stat.) enjoy a 20% discount on Professional Development offerings.

PROFESSIONAL DEVELOPMENT SUBTOTAL$_________

20% PStat®, GStat, or A.Stat. discount $_________

TOTAL PROFESSIONAL DEVELOPMENT COST $_________

CAREER SERVICEApplicant Options—Includes online access to job postings. To interview onsite, you must register for JSM. Prices are for May 2–June 30/July 1–August 4

ASA Member Nonmember

Student q $65/90 q $95/125

Nonstudent q $125/150 q $175/200

TOTAL CAREER SERVICE COST $______

_________________________________________________ Guest Name

_________________________________________________ Guest Name

_________________________________________________ Guest Name TOTAL GUEST COST $_________

PROFESSIONAL DEVELOPMENT Prices are for May 2–June 30/July 1–21 ROUNDTABLES

COMPUTER TECHNOLOGY WORKSHOPS $55 / 70 EACH

Wednesday, August 3

q CE_31T q CE_35T q CE_39T




PERSONAL SKILLS DEVELOPMENT OFFERINGS

Member Nonmember Student

Saturday, July 30, and Sunday, July 31 (spans two days)

CE_43P q $380/515 q $505/680 q $230/310

Saturday, July 30, and Sunday, July 31 (spans two days)

CE_44P q $380/515 q $505/680 q $230/310

Sunday, July 31

CE_45P Free event. No registration required.

Monday, August 1, and Tuesday, August 2 (spans two days)

CE_46P q $380/515 q $505/680 q $230/310

A.M. ROUNDTABLES$20 each; includes continental breakfast. Indicate your first and second choices by marking 1 and 2.

Monday August 1

TuesdayAugust 2

Wednesday August 3

ML01____ TL01____ WL01____

ML02____ TL02____ WL02____

ML03____ TL04____ WL03____

ML04____ TL05____ WL04____

ML05____ TL06____ WL05____

ML06____ TL07____ WL06____

ML07____ TL08____ WL07____

ML08____ TL09____ WL08____

WL09____

CONTINUING EDUCATION COURSES Member Nonmember Student

Saturday, July 30CE_01C q $660/895 q $805/1090 q $380/515CE_02C q $240/325 q $315/420 q $145/195CE_03C q $380/515 q $505/680 q $230/310CE_04C q $400/535 q $525/700 q $250/330CE_05C q $380/515 q $505/680 q $230/310CE_06C q $380/515 q $505/680 q $230/310CE_07C q $240/325 q $315/420 q $145/195

Sunday, July 31CE_08C q $240/325 q $315/420 q $145/195CE_09C q $380/515 q $505/680 q $230/310CE_10C q $380/515 q $505/680 q $230/310CE_11C q $380/515 q $505/680 q $230/310CE_12C q $380/515 q $505/680 q $230/310CE_13C q $240/325 q $315/420 q $145/195

Monday, August 1CE_14C q $240/325 q $315/420 q $145/195CE_15C q $240/325 q $315/420 q $145/195CE_16C q $380/515 q $505/680 q $230/310CE_17C q $380/515 q $505/680 q $230/310CE_18C q $400/535 q $525/700 q $250/330CE_19C q $380/515 q $505/680 q $230/310CE_20C q $240/325 q $315/420 q $145/195CE_21C q $240/325 q $315/420 q $145/195

Tuesday, August 2CE_22C q $240/325 q $315/420 q $145/195CE_23C q $240/325 q $315/420 q $145/195CE_24C q $240/325 q $315/420 q $145/195CE_25C q $380/515 q $505/680 q $230/310CE_26C q $380/515 q $505/680 q $230/310CE_27C q $400/535 q $525/700 q $250/330CE_28C q $240/325 q $315/420 q $145/195CE_29C q $240/325 q $315/420 q $145/195CE_30C q $240/325 q $315/420 q $145/195

Sponsored by:*American Statistical Association*International Biometric Society (ENAR and WNAR)*Institute of Mathematical StatisticsInternational Chinese Statistical AssociationInternational Indian Statistical AssociationInternational Society for Bayesian AnalysisKorean International Statistical SocietyRoyal Statistical Society*Statistical Society of CanadaInternational Statistical Institute(*indicates the founding societies of JSM)

Register online at www.amstat.org/jsmregistration.

May 2 (11:00 a.m. EDT) Registration and housing open

June 1 Early registration deadline

June 29 Housing deadline

June 30 Regular registration deadline

July 21 Late registration deadline

KEY DATESDon’t miss your chance to participate in

the largest gathering of statisticians and

data scientists held in North America!

Non-Profit Org. U.S. Postage

PAID Alexandria,

Virginia Permit No. 361

American Statistical Association732 North Washington StreetAlexandria, VA 22314-1943 USA

We thank the following JSM 2016 Sponsors for their financial support:

PLATINUM

GOLD

SILVER

It is not too late to show your support for JSM by becoming a 2016 JSM Sponsor.Visit www.amstat.org/jsmponsors to learn more.

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