Hotel Floor Plan | Program-at-a-Glance
Keynote Address | Concurrent Sessions | Poster Sessions
Short Courses (full day) | Short Courses (half day) | Tutorials | Practical Computing Demonstrations | Closing General Session with Refreshments

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Thursday, February 18
Thu, Feb 18, 7:00 AM - 6:30 PM
Registration Desk

SC1 Communicating Data Clearly
Thu, Feb 18, 8:00 AM - 5:30 PM
Diamond I
Instructor(s): Naomi B Robbins, NBR

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Communicating Data Clearly describes how to draw clear, concise, accurate graphs that are easier to understand than many of the graphs one sees today. The course emphasizes how to avoid common mistakes that produce confusing, or even misleading, graphs. Graphs for one, two, three, and many variables are covered, as well as general principles for creating effective graphs.

The course emphasizes principles of effective graphs, rather than how to create graphs with specific software. These principles apply to whatever software the attendees use. Participants will learn to:

• Present data more effectively in all media

• Display data so their structure is more apparent

• Understand principles of effective simple graphs to build upon when creating interactive or dynamic displays

• Become more critical and analytical when viewing graphs

Outline & Objectives

This course begins by reviewing human perception and our ability to decode graphical information. It continues by:

• Ranking elementary graphical perception tasks to identify those that we do the best.

• Showing the limitations of many common graphical constructions.

• Demonstrating newer, more effective graphical forms developed on the basis of the ranking.

• Providing general principles for creating effective graphs, as well as metrics on the quality of graphs.

• Commenting on software packages that produce graphs.

• Comparing the same data using different graph forms so the audience can see how understanding depends on the graphical construction used.

• Discussing Trellis Display (a framework for the visualization of multivariate data) and other innovative methods for presenting more than two variables.

• Presenting some graphical methods for categorical data.

• Discussing some examples of embedded graphs for complex data.

Since scales (the rulers along which we graph the data) have a profound effect on our interpretation of graphs, the section on general principles contains a detailed discussion of scales.

About the Instructor

• Chair of Statistical Graphics Section of ASA

• Author of Creating More Effective Graphs (Chart House, 2013; originally Wiley, 2005).

• Invited to blog for Forbes on Effective Graphs (see

• Speaker on graphs to numerous universities and professional societies

• Presenter to government, corporate and non-profit organizations

• Delivered tutorials and/or presentations at most O’Reilly Strata Conferences

• Speaker at the 61st Deming Conference on December 5, 2005

• Short course instructor at the Joint Statistical Meetings in Seattle on August 7, 2006

• A.B. degree in mathematics from Bryn Mawr College

• M.A. in mathematics from Cornell University

• Ph.D. in mathematical statistics from Columbia University

• Founding member of the New Jersey Chapter of ASA and served as President, Vice-President, Secretary, Treasurer, and Chair of the Advisory Committee

• Member of ASA, the Society for Technical Communication, Chapters of ASTD, IEEE and other professional societies

• Associate Fellow of the Society of Technical Communication

• Formerly a Member of Technical Staff at Bell Laboratories

Relevance to Conference Goals

Attendees will be exposed to graphical techniques, some of which may be new to them. Ideas covered are immediately applicable.

The entire emphasis of the course is to use best graphical practices to communicate quantitative information better.

Effective charts and graphs and understanding data better lead to better decisions which have a positive impact on the company. Communicating data better saves time at meetings.

Better communication of data enhances one’s career and avoids the loss of credibility that comes with using confusing, misleading or deceptive figures.

SC2 Interactive Graphics and Reports with R Markdown and Shiny
Thu, Feb 18, 8:00 AM - 5:30 PM
Diamond II
Instructor(s): Garrett Grolemund, RStudio, Inc.
This course will teach you how to use the Shiny and R Markdown packages to create interactive data products straight from R. The packages create an ideal workflow for sharing data and results with clients and colleagues. This is a beginner's course for intermediate R users.

The R Markdown package builds reports, documents, and presentations straight from your R code. You write your reports in markdown (an easy-to-write, plain text format) and embed R code chunks that create output to be included in the final document. R Markdown documents are fully reproducible (they can be automatically regenerated whenever underlying R code or data changes) and completely dynamic (you can export an R Markdown document as a slide show or an html, pdf, or MS Word file).

The Shiny package uses R to build interactive web apps, an ideal data product to share with consulting clients. Shiny apps reduce iteration and allow clients to explore their own data without relying on the consultant's resources. Clients can use a shiny app to explore data and run analyses---without needing to write or understand R code (you write the code for them when you create the app).

Outline & Objectives

You will learn to write reports with R Markdown and will create your own Shiny web apps. I will alternate brief lectures with hands-on practice: you’ll get plenty of experience actually using R Markdown and Shiny (not just hearing about them!) Outline:

1. Reports - Write reports in markdown. Embed R code chunks and customize how code output appears in the report. Render reports as html, pdf, MS Word docs and slideshows.

2. Interactivity - Add self-contained interactive data visualizations to your reports with htmlwidgets. Learn to use Shiny's reactive programming framework.

3. Web Apps - Layout interactive components into a web app with a customizable user interface. Learn how to share apps on the internet (with a password), or a private intranet.

This class will be a good fit for you if you already use R, but are new to R Markdown and Shiny.

Come ready to learn. You'll need your laptop and the latest versions of R and RStudio. Install the shiny and reportsWS packages ahead of time with install.packages(c("shiny", "devtools")) and devtools::install_github("rstudio/reportsWS"). R Markdown comes with RStudio. Each of these is free and open source, as is R and RStudio.

About the Instructor

The course will be led by RStudio Master Instructor and author Garrett Grolemund, Ph.D. Garrett is a teacher and statistician who has used R to analyze data for over 10 years. He works closely with the developers of Shiny and R Markdown, and is Editor in Chief of the Shiny Development Center web page. Garrett created and maintains the popular lubridate R package and is the author of Hands-On Programming with R as well as Data Science with R, an upcoming book by O'Reilly Media.

Garrett gave a very well-received talk at the 2015 Conference on Statistical Practice titled "Reporting results with R, R Markdown, and Shiny."

Relevance to Conference Goals

This course teaches students two ways to better communicate with customers. Embedded in the methods are benefits that will have a positive impact on the student's organization. The two methods that improve communication are:

1. An easy and quick way to create reports from the code that statisticians use to generate results. This method has the benefits that it

* Creates reproducible reports, which can quickly be regenerated on new data to update an analysis or to prove the scientific validity of the results.

* Lets students export the same report in multiple formats, which saves time and makes content easier to share through different channels.

2. A way to build interactive data products that clients can use to explore their own data and explore the results of the student's analysis. This method has the benefits that it

* Conveys large amounts of information efficiently, and provides a navigation system for accessing the information.

* Shortens cycles of iteration. Clients can use the app to fine tune analyses without the aid of the consultant.

* Lets clients recreate analysis and explore data without needing to know how to program in R.

* Looks impressive.

SC3 Propensity Score Methods: Practical Aspects and Software Implementation
Thu, Feb 18, 8:00 AM - 12:00 PM
Instructor(s): Adin-Cristian Andrei, Feinberg School of Medicine

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Observational studies are becoming increasingly common and complex in a variety of industries, including biomedical, health services, pharmaceutical, insurance, and online advertising. To adequately estimate causal effect sizes, proper control of known potential confounders is critical. Having gained enormous popularity in recent years, propensity score methods are powerful and elegant tools for estimating causal effects. Without assuming prior knowledge of propensity score methods, this short course will use simulated and real data examples to introduce and illustrate important propensity score techniques such as weighting, matching, and subclassification.

Relevant R and SAS software packages for implementing data analyses will be discussed in detail. Specific topics to be covered include guidelines for constructing a propensity score model, creating matched pairs for binary group comparisons, assessing baseline covariate balance after matching, and using inverse propensity score weighting techniques. Illustrative examples will accompany each topic and a brief review of recent relevant developments and their implementation will be discussed.

Outline & Objectives

- Observational Studies: definition, examples, causal effects, confounding control.

- Propensity Scores: definition, properties, modeling techniques.

- Propensity Score Approaches in Observational Studies: weighting, matching, sub-classification; graphical methods to assess covariate balance after matching; R and SAS software implementation of these techniques.

- Guidelines on how to best describe the methodology utilized and the results obtained when presenting to a non-technical audience.

- Brief review of the most recent methods developments and discussion of their potential for immediate use in practice.

Objectives and Scope:
The first objective is to provide an overview of some of the most commonly used propensity score-based methods in observational studies, while focusing on the practical aspects. The second objective is to present the practical implementation of these methods. The third objective is to discuss the advantages and disadvantages associated with these methods.

About the Instructor

Dr. Andrei has obtained a Ph.D. degree in Biostatistics from the University of Michigan in 2005. He is currently a Research Associate Professor in the Feinberg School of Medicine at Northwestern University, where he enjoys successful collaborations in cardiac surgery and cardiovascular outcomes research, of which observational studies are a major component. Dr. Andrei has developed expertise in propensity score techniques, as reflected by national conference presentations and his participation as primary biostatistician in a series of publications that utilize propensity score methods. He has developed practice-inspired and -oriented statistical methods in survival analysis, recurrent events, group sequential monitoring methods, hierarchical methods, and predictive modeling.

In the last 10 years, Dr. Andrei has collaborated with medical researchers in fields such as pulmonary/critical care, organ transplantation, nursing, prostate and breast cancer, anesthesiology and thoracic surgery.

Currently, he serves as Statistical Co-Editor of the Journal of the American College of Surgeons and deputy Statistical Editor of the Journal of Thoracic and Cardiovascular Surgery.

Relevance to Conference Goals

Upon attending this course, participants will become more familiar with propensity score-based methods for estimating causal effects in observational studies. Implementation in R and SAS software will be discussed in great detail, thus permitting participants to integrate these newly-acquired statistical techniques into their professional activities and projects. Learning how to produce simple yet powerful graphics to assess the propensity score model adequacy, check covariate balance and display the results, will undoubtedly benefit every participant. By leveraging their enhanced set of skills, individuals across industries will be adequately positioned to become more effective communicators in their interactions with customers and clients. Continued professional development is key to one’s career growth and can enhance the overall analytical capabilities within their respective organizations and institutions.

SC4 Introduction to Adaptive Designs
Thu, Feb 18, 8:00 AM - 12:00 PM
Crystal II
Instructor(s): Aaron Heuser, IMPAQ International; Minh Huynh, IMPAQ International; Chunxiao Zhou, IMPAQ International

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Unlike conventional studies or clinical trials in which the design is pre-specified, adaptive designs are a class of study designs with adaptive or flexible study characteristics. Things that can vary include the sample size, variable dose or intervention, or flexible number of study arms. This flexibility is guided by examining the accumulated data at an interim point in the study and initiating changes in the design that may make the studies more efficient, more likely to demonstrate an effect of the intervention---if one exists---or more informative (e.g., by providing broader dose-response information). This course will introduce adaptive designs, explore their weaknesses and strengths, illustrate the techniques with examples, and allow attendees to have hands-on experience with a toolkit designed to get started in using adaptive designs.

Outline & Objectives

A. Course Outline
1) Fundamental Principles of Adaptive Designs
2) Three Examples of Adaptive Designs
3) Adaptive Design versus Conventional Designs
a. Advantages of adaptive designs
b. Disadvantages and risks associated with adaptive designs
4) Recent Advances in Adaptive Designs
5) The Basic Adaptive Design Toolkit: What you need to get started
a. Essential design elements for your proposal or protocol designs
b. Software implementation
6) Hands-on Practice

B. Course Objectives
1) Gain familiarity with the basic principles of adaptive designs
2) See of examples of adaptive designs in practice
3) Understand strengths and weaknesses, and when to use and when not to use adaptive designs
4) See the latest application of adaptive designs
5) Obtain a starter toolkit to start using adaptive designs in your work

About the Instructor

Dr. Minh Huynh is a Principal Research Associate and Managing Director at IMPAQ International, LLC. Dr. Huynh was a former Institute Staff Scientist at the NIH Clinical Research Center. Dr. Aaron Heuser is a Senior Research Associate at IMPAQ International, LLC. Dr. Heuser was also a former Mathematical Statistician at the NIH Clinical Research Center in Bethesda, Maryland.

Relevance to Conference Goals

This course is closely related to Theme 2, Data Modeling and Analysis. The course will provide all attendees with theoretical and practical knowledge and techniques related adaptive designs through the application of state-of-the-art methods and findings. Course presentations will cover all aspects of adaptive designs and present information relevant to a broad range of applied statisticians, regardless of industry or field of expertise.

SC5 The Coward's Guide to Conflict
Thu, Feb 18, 8:00 AM - 12:00 PM
Instructor(s): Colleen Mangeot, Cincinnati Children's Hospital Medical Center

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This workshop is based on "The Coward's Guide to Conflict - Empowering Solutions for Those That Would Rather Run Than Fight" by Tim Ursiny. Ever cringed at the thought of having a tough conversation? Do you avoid conflict, knowing deep down you need to do something? This workshop will give you the tools needed to address conflict more confidently and courageously.

Outline & Objectives

This interactive workshop will cover:
1. The Coward in Us All – examine your own reasons for avoiding conflict and the top 10 reasons people avoid conflict
2. Motivate Yourself to Address Conflict – examine the pain and pleasure of conflict
3. Common Causes of Conflict – Proactively avoid conflict
4. Techniques for Handling Conflict

You will complete interactive exercises and learn step by step approaches that will help you work through conflict more easily, gracefully, and courageously.

About the Instructor

Colleen Mangeot's diverse career includes 10 years in the actuarial field, 10 years in coaching and leadership development, and 6 years in biostatistics.
Highlights of her 7 year coaching business include: Successfully working with clients to increase efficiency and sales by 30% or more; Graduate of the world’s largest coach training organization, Coach U; Attained the Professional Coach Certification from the International Coach Federation in 2003; Monthly columnist for the Dayton Business Journal; National speaker with over 200 hours of paid speaking engagements; Contractor with the Anthony Robbins Companies.
She received her MS in Statistics from Miami University, received the NSA National Research Council Fellowship at NIOSH, and worked in statistical quality improvement at the VA. Now, in addition to working in the Biostatistical Consulting Unit at Cincinnati Children’s Hospital Medical Center, she is also an internal coach working with executives to further their careers.
She was a panelist for the invited session at JSM 2013, Secrets to Effective Communication for Statistical Consultants, and presented two sessions at last years’ Conference on Statistical Practice.

Relevance to Conference Goals

This session will develop important communication skills for career advancement, leadership and management effectiveness. We all must face conflict from time to time. The most successful people transform conflict into positive results. The approaches and steps taught in this session will apply equally well in all environments, academia, government and industry. The presenter has personally utilized these in her career in government and industry.

SC6 Bootstrap Methods and Permutation Tests
Thu, Feb 18, 1:30 PM - 5:30 PM
Instructor(s): Tim Hesterberg, Google Inc.

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We begin with a graphical approach to bootstrapping and permutation testing, illuminating basic statistical concepts of standard errors, confidence intervals, p-values, and significance tests.

We consider a variety of statistics (mean, trimmed mean, regression, etc.) and a number of sampling situations (one-sample, two-sample, stratified, finite-population), stressing the common techniques that apply in these situations. We'll look at applications from a variety of fields, including telecommunications, finance, and biopharm.

These methods let us do confidence intervals and hypothesis tests when formulas are not available. This lets us do better statistics (e.g., use robust methods (we can use a median or trimmed mean instead of a mean, for example)). They can help clients understand statistical variability. And some of the methods are more accurate than standard methods.

Outline & Objectives

Introduction to Bootstrapping
General procedure
Why does bootstrapping work?
Sampling distribution and bootstrap distribution

Bootstrap Distributions and Standard Errors
Distribution of the sample mean
Bootstrap distributions of other statistics
Simple confidence intervals
Two-sample applications

How Accurate Is a Bootstrap Distribution?

Bootstrap Confidence Intervals
Bootstrap percentiles as a check for standard intervals
More accurate bootstrap confidence intervals

Significance Testing Using Permutation Tests
Two-sample applications
Other settings

Wider variety of statistics
Variety of applications
Examples where things go wrong, and what to look for

Wider variety of sampling methods
Stratified sampling, hierarchical sampling
Finite population
Time series

Participants will learn how to use resampling methods:
* to compute standard errors,
* to check the accuracy of the usual Gaussian-based methods,
* to compute both quick and more accurate confidence intervals,
* for a variety of statistics and
* for a variety of sampling methods, and
* to perform significance tests in some settings.

About the Instructor

Dr. Tim Hesterberg is a Senior Statistician at Google. He previously worked at Insightful (S-PLUS), Franklin & Marshall College, and Pacific Gas & Electric Co. He received his Ph.D. in Statistics from Stanford University, under Brad Efron.

Hesterberg is author of the "Resample" package for R and primary author of the "S+Resample" package for bootstrapping, permutation tests, jackknife, and other resampling procedures, is co-author of Chihara and Hesterberg "Mathematical Statistics with Resampling and R" (2011), and is lead author of "Bootstrap Methods and Permutation Tests" (2010), W. H. Freeman, ISBN 0-7167-5726-5, and technical articles on resampling. See

Hesterberg is on the executive boards of the National Institute of Statistical Sciences and the Interface Foundation of North America (Interface between Computing Science and Statistics).

He teaches kids to make water bottle rockets, leads groups of high school students to set up computer labs abroad, and actively fights climate chaos.

Relevance to Conference Goals

Resampling methods are important in statistical practice, but have been omitted or poorly covered in may old-style statistics courses. These methods are an important part of the toolbox of any practicing statistician.

It is important when using these methods to have some understanding of the ideas behind these methods, to understand when they should or should not be used.

They are not a panacea. People tend to think of bootstrapping in small samples, when they don't trust the central limit theorem. They are not a panacea. People tend to think of bootstrapping in small samples, when they don't trust the central limit theorem. However, the common combinations of nonparametric bootstrap and percentile intervals is actually accurate than t procedures. We discuss why, remedies, and better procedures that are only slightly more complicated.

These tools also show how poor common rules of thumb are -- in particular, n >= 30 is woefully inadequate for judging whether t procedures should be OK.

SC7 Applied Meta-Analysis Using R
Thu, Feb 18, 1:30 PM - 5:30 PM
Crystal II
Instructor(s): Din Chen, University of North Carolina at Chapel Hill

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In the Big Data era, it has become the norm for the data collected to address a similar scientific question coming from diverse sources of studies. The art and science of synthesizing information from diverse sources to draw a more effective inference is generally referred to as meta-analysis. In recent years, meta-analysis has played an increasingly important role in statistical applications, and its applications to those fields have led to numerous scientific discoveries. This course is then designed for an overview of meta-analysis based on the author's new book, "Applied Meta-Analysis Using R (2013)". This tutorial provides an up-to-date look at and thorough presentation on Big Data, along with meta-analysis models with detailed step-by-step illustrations and implementation using R. The examples are compiled from real applications in public literature, and the analyses are illustrated in a step-by-step fashion using the most appropriate R packages and functions. Attendees will be able to follow the logic and R implementation to analyze their own research data.

Outline & Objectives

Session 1:
• Introduction to R
• Overview to meta-analysis for both fixed-effects and random-effects models in meta-analysis. Real datasets in public health are introduced along with two commonly used R packages of "meta" and "rmeta"
Session 2:
• Meta-analysis models for binary data, such as for risk-ratio, risk difference and odds-ratio
• Methods to quantify heterogeneity and test the significance of heterogeneity among studies in a meta-analysis and then introduce meta-regression with R package of "metafor".

About the Instructor

Dr. Din Chen is a professor at the University of Rochester. He was the Karl E. Peace endowed eminent scholar chair in biostatistics at Georgia Southern University. Professor Chen is also a senior statistics consultant for biopharmaceuticals and government agencies with extensive expertise in clinical trials and bioinformatics. He has more than 100 referred professional publications and co-authored six books on clinical trial methodology and public health applications.
Professor Chen was honored with the "Award of Recognition" in 2014 by the Deming Conference Committee for highly successful 4 advanced biostatistics workshop tutorials at 4 successive Deming conferences on 4 different books that he has written or co-edited. In 2013, he was invited to give a short course at the twentieth Annual Biopharmaceutical Applied Statistics Symposium (BASS XX, 2013) for his contribution in meta-analysis and received a "Plaque of Honor" for his short course.
The tutorial is based on his recently book "Applied Meta-Analysis using R" with Professor Karl E. Peace by Chapman and Hall/CRC in 2013.

Relevance to Conference Goals

1. To give a up-to-date methodology development in meta-analysis and to guide the participants to learn the meta-analysis models

2. To give an overview of R implementations for meta-analysis models with R packages and R programming

3. To emphasize the applied aspects of meta-analysis with real public health data compiled from systematic reviews to help applied statisticians to solve their real-life problems from research and consulting.

SC8 Modern Statistical Process Control Charts and Their Use as a Tool for Analyzing Big Data
Thu, Feb 18, 1:30 PM - 5:30 PM
Instructor(s): Peihua Qiu, University of Florida

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Big Data often take the form of data streams with observations of certain processes collected sequentially over time. Among many purposes, one common task to collect and analyze Big Data is to monitor the longitudinal performance/status of the related processes. To this end, statistical process control (SPC) charts could be a useful tool, although conventional SPC charts need to be modified properly in some cases. This short course discusses traditional SPC charts, including the Shewhart, CUSUM, and EWMA charts, as well as recent control charts based on change-point detection and fundamental multivariate SPC charts under the normality assumption. It also introduces novel univariate and multivariate control charts for cases when the normality assumption is invalid and discusses control charts for profile monitoring. Some examples will be discussed to use conventional control charts or their modifications for monitoring different types of processes with Big Data. Among many potential applications, dynamic disease screening and profile/image monitoring will be discussed in detail. All computations in the examples are solved using R.

Outline & Objectives

Course outline:

This short course discusses both traditional and more recent statistical process control (SPC) charts and how to use them for analyzing big data. More specifically, the course topics include (i) traditional SPC charts, including the Shewhart, CUSUM and EWMA charts, (ii) some recent control charts based on change-point detection, (iii) fundamental multivariate SPC charts under the normality assumption, (iv) novel univariate and multivariate control charts for cases when the normality assumption is invalid, (v) control charts for profile monitoring, and (vi) hands-on examples to use conventional control charts or their modifications for monitoring different types of processes with big data.

Course Objectives:

After taking this short course, participants should be able to apply some basic and more advanced statistical process control (SPC) charts to applications with big data streams. More specifically, participants will learn (i) traditional SPC charts, (ii) some recent control charts in the literature, and (iii) how to use SPC charts to handle applications with big data streams.

About the Instructor

The instructor, Professor Peihua Qiu, is the current editor of Technometrics, which is the flagship journal in industrial statistics, co-sponsored by ASA and American Society in Quality. He has been working on various statistical process control (SPC) problems since 1998, and has made substantial contributions in several SPC areas, including nonparametric SPC, SPC by change-point detection, and profile monitoring. His recent book Qiu (2014, Chapman & Hall) gives a systematic description of both traditional and newer SPC methods. Professor Qiu is the elected fellow of ASA, IMS and ISI. After obtaining his Ph.D. in statistics from University of Wisconsin - Medison, he helped create the Biostatistics Center at the Ohio State University during 1996-1998. Then, he worked as an assistant (1998-2002), associate (2002-2007) and full professor (2007-2013) of the School of Statistics at the University of Minnesota, and taught 13 different courses on various topics. He moved to University of Florida as the founding chair of the Department of Biostatistics in 2013. During his career, Professor Qiu is constantly involved in statistical consulting and collaborative research.

Relevance to Conference Goals

This proposed short course fits well the following two themes of the conference: 1) Big Data Prediction and Analytics, and 2) Data Modeling and Analysis.

Statistical process control (SPC) charts are commonly used in industries, especially in manufacturing industries. However, most SPC charts used in practice are several decades old. For instance, the most commonly used chart is the Shewhart chart proposed by Walter Shewhart in 1931. One main purpose of this course is to let the participants know that 1) many traditional SPC charts could give misleading results if their assumptions are not carefully checked, and 2) some newer SPC charts have been developed in the literature and they often provide more reliable results. In various big data applications, big data often take the form of data streams with observations of certain processes collected
sequentially over time. Another main purpose of the course is to show that SPC charts provide an effective tool for handling such applications. By taking this course, participants will learn statistical techniques that can apply to their daily jobs and better communicate with their clients and customers.

PS1 Poster Session 1 & Opening Mixer sponsored by SAS
Thu, Feb 18, 5:30 PM - 7:00 PM
Ballroom Foyer
Chair(s): V. Ramakrishnan, Medical University of South Carolina

1 The Complex Sample Bag of Little Bootstraps
View Presentation View Presentation Michael Devin Floyd, Washington University in St. Louis
2 A Method for Selecting the Relevant Dimensions for Text Classification in Singular Vector Spaces
Dawit Gezahegn Tadesse, University of Cincinnati
3 Analysis of Survival Functions in Predicting Length of Stay in Florida Hospitals
View Presentation View Presentation Benjamin Ray Webster, University of North Florida
4 Classroom to Collaboration: A Grad Student’s Tips for a Successful Transition
View Presentation View Presentation Brittney Bailey, The Ohio State University
5 Statistical Leadership: More Than Just a Position (Laws of Statistical Leadership)
View Presentation View Presentation William Coar, Axio Research
6 Producing Acceptable Results from Statistical Collaboration
View Presentation View Presentation Adam Michael Edwards, LISA, Virginia Tech
7 Sometimes It's the Little Things: Choosing Row vs. Column Percentages
View Presentation View Presentation Andrew D. Althouse, University of Pittsburgh Medical Center
8 Demonstration of Novel Statistical Procedures to Adjust for Baseline Variables in Estimating Average Treatment Effects with Binary Responses
View Presentation View Presentation Elizabeth Colantuoni, The Johns Hopkins University
9 Bringing Value: Market Share Analysis That Goes Deeper
View Presentation View Presentation John Anthony Craycroft, University of Louisville School of Public Health and Information Sciences
10 Adaptive Design Clinical Trials: A Statistical and Programming Perspective
View Presentation View Presentation Dhawal P. Oswal, Quintiles
11 Introduction of Generalized Weighted Correlation Coefficients and Their Properties
View Presentation View Presentation Mengru Zhang, Stony Brook University
12 Missing Value Assumption in Modeling Repeated Measures Using Generalized Estimating Equations
View Presentation View Presentation Michael P Chen, U.S. Centers for Disease Control and Prevention
13 Use of Multivariate Data Analysis in Optimization of Risk-Based Monitoring of Multicenter Trials
Xiaoqiang Xue, Unaffiliated
14 Missing Data Strategies for Multilevel Models
View Presentation View Presentation Stefany Coxe, Florida International University
15 A Comparison of Alternative Approaches to Analyzing Subgroup Differences in Survival After AIDS Diagnosis When the Proportional Hazards Assumption Does Not Hold
View Presentation View Presentation Felicia Hardnett, U.S. Centers for Disease Control and Prevention
16 A Regression-Based Spatial Capture-Recapture Model for Estimating Species Density
View Presentation View Presentation Purna Saubhagya Gamage, Texas Tech University
17 Combining Statistical and Compartmental Models for Use in Tobacco Product Risk Assessments
View Presentation View Presentation Edward L Boone, Virginia Commonwealth University
18 Visualizing Linked Data Sources for the National Children’s Study
View Presentation View Presentation Edward Mulrow, NORC at the University of Chicago
Exhibits Open
Thu, Feb 18, 5:30 PM - 7:00 PM
Ballroom Foyer

Friday, February 19
Fri, Feb 19, 7:30 AM - 5:30 PM
Registration Desk

Continental Breakfast sponsored by Salford Systems
Fri, Feb 19, 7:30 AM - 8:30 AM
Ballroom Foyer

Exhibits Open
Fri, Feb 19, 7:30 AM - 6:30 PM
Ballroom Foyer

GS1 Keynote Address
Fri, Feb 19, 8:00 AM - 9:00 AM
Chair(s): Jim Rutherford, Chevron Oronite Company, LLC

Communicating the Value of Statistics
View Presentation View Presentation Jessica Utts, University of California, Irvine
CS01 Business Essentials
Fri, Feb 19, 9:15 AM - 10:45 AM
Crystal II
Chair(s): Yaqi Xue, Stony Brook University

9:20 AM Business Essentials That You Need to Know Before Starting Your Career as an Independent Statistical Consultant
View Presentation View Presentation Stephen Simon, P.Mean Consulting
10:05 AM Moving from Statistical Consultant to Trusted Adviser: What Clients Want
View Presentation View Presentation Michael Latta, YTMBA Research & Consulting Coastal Carolina University
CS02 Analytic Architecture and Design
Fri, Feb 19, 9:15 AM - 10:45 AM
Chair(s): Mariangela Guidolin, University of Padua

9:20 AM Causality from Observational Data
View Presentation View Presentation Hrishikesh Vinod, Fordham University
10:05 AM Meta-Analysis Methods in Measuring Brand Ad Effectiveness
View Presentation View Presentation Shyue-Ming Loh, Google Inc.
CS03 Text Analytics
Fri, Feb 19, 9:15 AM - 10:45 AM
Diamond I&II
Chair(s): Sridhar Ramaswamy, Caterpillar Analytics Division

9:20 AM Text Analysis with Survey Data
View Presentation View Presentation Wendy Martinez, Bureau of Labor Statistics
10:05 AM Applied Text Analytics: A Visual Approach
View Presentation View Presentation James W Wisnowski, Adsurgo, LLC
CS04 Data Visualization
Fri, Feb 19, 9:15 AM - 10:45 AM
Chair(s): Sam Behseta, California State University, Fullerton

9:20 AM Effective Data Visualization: Understanding What the Mind Sees
Xan Gregg, JMP Division of SAS
10:05 AM How to Avoid Different Graphical Mistakes
Naomi B Robbins, NBR
Refreshment Break sponsored by Statgraphics
Fri, Feb 19, 10:45 AM - 11:00 AM

CS05 Improving Collaboration
Fri, Feb 19, 11:00 AM - 12:30 PM
Crystal II
Chair(s): Kirsten E. Eilertson, Penn State Statistics Department

11:05 AM The Seven Habits of Highly Effective Statistical Consultants
View Presentation View Presentation Alan Curtis Elliott, Southern Methodist University
11:50 AM Using Video to Improve Your Practice of Statistics
View Presentation View Presentation Eric Vance, LISA, Virginia Tech
CS06 Model Selection and Experimental Design
Fri, Feb 19, 11:00 AM - 12:30 PM
Chair(s): Fenghai Duan, Brown University School of Public Health

11:05 AM New Product Diffusion Models: Theory and Practice
View Presentation View Presentation Mariangela Guidolin, University of Padua
11:50 AM Measuring Brand Ad Effectiveness
View Presentation View Presentation Sen Yuan, Google Inc.
CS07 Emerging Challenges and Methods for Large Databases
Fri, Feb 19, 11:00 AM - 12:30 PM
Diamond I&II
Chair(s): David Corliss, Ford

11:05 AM High-Dimensional Linear Model Stability and Robustness in Large Database Applications
View Presentation View Presentation Michael B Brimacombe, KUMC
11:50 AM An Introduction to High-Performance Statistical Modeling Procedures in SAS
Robert N. Rodriguez, SAS
CS08 Interactivity with R Shiny 1
Fri, Feb 19, 11:00 AM - 12:30 PM
Chair(s): Isabella R. Ghement, Ghement Statistical Consulting Company Ltd

11:05 AM Interactive Data Visualizations in R with Shiny and ggplot2
Garrett Grolemund, RStudio, Inc.
11:50 AM Interactively Building Test Forms from an IRT Perspective: An Application of R and Shiny
View Presentation View Presentation Brandon LeBeau, University of Iowa
Lunch (on own)
Fri, Feb 19, 12:30 PM - 2:00 PM

CS09 Do the Right Thing
Fri, Feb 19, 2:00 PM - 3:30 PM
Crystal II
Chair(s): Abbas F. Jawad, University of Pennsylvania Perelman School of Medicine

2:05 PM Common Pitfalls and Misconceptions in Statistics with Suggested Solutions
View Presentation View Presentation Victoria Cox, Dstl
2:50 PM Just Say No!
View Presentation View Presentation Mary W. Gray, American University
CS10 Addressing Data Issues
Fri, Feb 19, 2:00 PM - 3:30 PM
Chair(s): Gary Chung, Johnson & Johnson

2:05 PM Create Robust Linear Models Using Generalized Regression
Brady Brady, SAS
2:50 PM Longitudinal Data Analysis and Missing Data: Last Observation Stays Put
View Presentation View Presentation Kathleen Jablonski, The George Washington University
CS11 Multivariate Analytic Methods
Fri, Feb 19, 2:00 PM - 3:30 PM
Diamond I&II
Chair(s): Hrishikesh Chakraborty, The University of South Carolina

2:05 PM Multivariate Inverse Prediction (Calibration) Using Standard Software for Mixed Models
View Presentation View Presentation Lynn Roy LaMotte, Louisiana State University Health Sciences Center
2:50 PM Developing, Testing, and Comparing Theories Using Structural Equation Modeling
View Presentation View Presentation Darius Singpurwalla, University of Maryland
CS12 Graphical Design and Software
Fri, Feb 19, 2:00 PM - 3:30 PM
Chair(s): Shelley Brock, Westat

2:05 PM Design of Multi-Panel Graphs
View Presentation View Presentation Richard M. Heiberger, Department of Statistics, Temple University Fox School of Business
2:50 PM Graphical Exploration: A Comparison of Statistical Software Packages
View Presentation View Presentation William Everett Cecere, Westat
CS13 Communication
Fri, Feb 19, 3:45 PM - 5:15 PM
Crystal II
Chair(s): Patricia English, Pfizer La Jolla Labs

3:50 PM Do You Hear What I Hear? An Examination of Effective Communication
View Presentation View Presentation Erin Anika Wiley, Westat
4:35 PM Explaining Statistics to Nonstatisticians
View Presentation View Presentation Heather Smith, California Polytechnic State University
CS14 Event Modeling: Will It Happen and When?
Fri, Feb 19, 3:45 PM - 5:15 PM
Chair(s): Annette M. Bachand, Colorado State University

3:50 PM Multi-State Models with Practical Applications
View Presentation View Presentation Adin-Cristian Andrei, Feinberg School of Medicine
4:35 PM The Practice of Credit Risk Modeling for Alternative Lending
View Presentation View Presentation Keith Shields, Magnify Analytic Solutions
CS15 Interactivity with R Shiny 2
Fri, Feb 19, 3:45 PM - 5:15 PM
Diamond I&II
Chair(s): Steven B. Cohen, RTI International

3:50 PM Statistical Network Analysis with the statnetWeb GUI
View Presentation View Presentation Emily Nicole Beylerian, University of Washington
4:35 PM Can Educational Cyberinfrastructure Empower Nonstatisticians with Bayesian Methodology?
Christopher T Franck, Virginia Polytechnic Institute and State University
CS16 Model Deployment and Diagnostics
Fri, Feb 19, 3:45 PM - 5:15 PM
Chair(s): Zhaohui Su, Quintiles

3:50 PM Statistical Models in Production: A Taxonomy of Deployment Methods
View Presentation View Presentation Neal Fultz, OpenMail
4:35 PM Data Exploration, Model Diagnostics, and Visualization with R
View Presentation View Presentation Till Bergmann, University of California, Merced
PS2 Poster Session 2 & Refreshments
Fri, Feb 19, 5:15 PM - 6:30 PM
Ballroom Foyer
Chair(s): Jessica Jaynes, California State University, Fullerton

1 Multiple Independent Hypothesis Testing of Discrete Data
View Presentation View Presentation Stefanie Rose Austin, Penn State University
2 The Use of Analogies to Help Clinicians and Investigators Better Understand the Principles and Practice of Biostatistics
View Presentation View Presentation Martin L Lesser, Feinstein Institute for Medical Research
3 Pitching Analytics: Recommendations on How to Sell Your Story
Kenneth Sanford, SAS
4 Training for More Effective Communication
View Presentation View Presentation Monica Lee Johnston, M. Lee & Company
5 Transforming Professional Biostatistics Consultations into Undergraduate Research Projects
View Presentation View Presentation Darlene Marie Olsen, Norwich University
6 A Practical Guide for Analyzing Zero-Inflated Count Data
View Presentation View Presentation Yaqi Xue, Stony Brook University
7 Statistical Consulting: Exploring Bayesian Latent Class Models as a Potential Statistical Tool to Estimate Sensitivity and Specificity in Presence of an Imperfect or No Gold Standard
View Presentation View Presentation Jay Mandrekar, Mayo Clinic
8 Using Ranking to Adjust Initial Sampling Weights
View Presentation View Presentation Joseph A. Kufera, National Study Center for Trauma and EMS
9 Case Study on Fitting a Risk Prediction Model for Multiple Competing Outcomes
Haley Hedlin, Stanford University
10 From Linear to Generalized Linear Mixed Models: A Case Study in Repeated Measures
View Presentation View Presentation Darren Keith James, New Mexico State University
11 Simulation of Imputation Effects Under Different Assumptions
View Presentation View Presentation Danny Rithy, California Polytechnic State University
12 A Two-Sample Test for Functional Data Applied to Fine Particulate Matter Measurements on Air
View Presentation View Presentation JAVIER OLAYA, Universidad del Valle
13 Flexible Unified Drug Interaction Models for Estimating a Drug Combination’s Efficacy That Depends on a Specific Variable or Multiple Variables
View Presentation View Presentation Jianjin Xu, Stony Brook University
14 Investigation of Pre-Symptomatic Biomarkers of Sepsis
View Presentation View Presentation Laura Craddock, Dstl
15 A Linear Mixed Model for the Longitudinal Analysis of Difference Scores
View Presentation View Presentation Brandy R. Sinco, University of Michigan School of Social Work
16 Characterizing Subjects with Chronic Obstructive Pulmonary Disease in GOLD Stage 2
View Presentation View Presentation Grace Hyun Kim, University of California, Los Angeles
17 Modeling Weight Change in a Lifestyle Program to Prevent Type 2 Diabetes
View Presentation View Presentation Elizabeth Ely, U.S. Centers for Disease Control and Prevention
18 Predictive Models of Health Expenditure Using Regularization: Do Low-Income and Lower Middle-Income Economies Share Common Predictors?
View Presentation View Presentation Emmanuel Thompson, Southeast Missouri State University
19 A Handy SAS Macro for Producing Descriptive Tables
View Presentation View Presentation Andrew D. Althouse, University of Pittsburgh Medical Center
20 Creating Easy Way to Generate Regression Tables for Research Papers Using New Set of R Functions
Ragheed Fadhil Al-Dulaimi, University of Utah School of Medicine
Saturday, February 20
Exhibits Open
Sat, Feb 20, 7:30 AM - 1:00 PM
Ballroom Foyer

Sat, Feb 20, 7:30 AM - 2:30 PM
Registration Desk

PS3 Poster Session 3 & Continental Breakfast sponsored by Capital One
Sat, Feb 20, 8:00 AM - 9:15 AM
Ballroom Foyer
Chair(s): Jyoti N. Rayamajhi, Eli Lilly and Company

1 Sample Size Calculations Using Techniques from Power Analysis
View Presentation View Presentation Micah Thornton, Southern Methodist University Darwin Deason Institute for Cyber Security
2 Use of Hamming Weights Instead of Uniform Distributions to Analyze a Set of Strings for Randomness
Josh Rendon, Southern Methodist University Darwin Deason Institute for Cyber Security
3 Moving from Academia to Private Practice
View Presentation View Presentation Kim Love-Myers, University of Georgia
4 Choosing English Terms to Describe Correlation and Causality in English
View Presentation View Presentation Jocelyn T. Graf, Proficia
5 Developing an Imputation Method for a Census That Is Distasteful to None and Accepted by All
View Presentation View Presentation Laura T Bechtel, U.S. Census Bureau
6 Practical Limitations of the Test/Validation Analysis Strategy
View Presentation View Presentation Christopher Holloman, Information Control Corporation
7 Introduction and Comparison of Different Predictive Models Using Incomplete and High-Dimension Data
View Presentation View Presentation Jie Yang, Stony Brook University
8 Classification Methods for Ordinal Data and Their Applications in Clinical Research
View Presentation View Presentation Jianjin Xu, Stony Brook University
9 Predictive Accuracy Measures for Binary Responses: Relationships and Impact of Incidence Rate
View Presentation View Presentation Ryan Scolnik, Florida State University
10 Comprehensive Evalutation of Statistical Tests for Two-Location Comparison
Chia-Ling Kuo, University of Connecticut
11 Power and Sample Size Calculations for Interval-Censored Survival Analysis
View Presentation View Presentation John Michael Williamson, U.S. Centers for Disease Control and Prevention
12 Generalizing Results from Randomized Trials to a Target Population via Weighting Methods
View Presentation View Presentation Ziyue Chen, The Ohio State University
13 Calculating Power for an Ordinal Outcome in a Longitudinal Cluster-Randomized Clinical Trial Design with Differential Attrition
View Presentation View Presentation Oksana Pugach, IHRP at UIC
14 Constructing an Index of Multiple Area-Level Deprivation for Auckland, New Zealand
View Presentation View Presentation Arier Chi-Lun Lee, University of Auckland
15 Cumulative Burden of Atrial Fibrillation: An Application Using a Nonlinear Mixed Effects Model
View Presentation View Presentation Jeevanantham Rajeswaran, Cleveland Clinic
16 Combining Multiple Statistical Methods for Measuring Ad Effectiveness
View Presentation View Presentation Buck Fisk, Interspace co ltd
17 A Forecasting Model for Futures Prices Based on Time Series Analysis: Dairy Commodities Data
View Presentation View Presentation Katie Anne Bakewell, University of North Florida
18 Software for Survival and Multistate Analysis in R
Adam King, Cal Poly Pomona
19 A Web-Based System for Randomized Assignment in Clinical Trials Using Minimization
View Presentation View Presentation Sophie Yu-Pu Chen, University of Michigan
CS17 Career Development: Mentoring and Influence
Sat, Feb 20, 9:15 AM - 10:45 AM
Crystal II
Chair(s): Karen Moran Jackson, The University of Texas at Austin

9:20 AM Mentoring Using Motivational Interviewing
View Presentation View Presentation Mary J Kwasny, Northwestern University
10:05 AM Influencing an Organization: Preparation Meets Opportunity
View Presentation View Presentation Sarah Kalicin, Intel Corporation
CS18 Quantile Regression Applications
Sat, Feb 20, 9:15 AM - 10:45 AM
Chair(s): Michael Regier, West Virginia University

9:20 AM Applied Quantile Regression
View Presentation View Presentation Yonggang Yao, SAS
10:05 AM A Comparison of Three Methods for Estimating Percentile Curves for Language Development in Down Syndrome Babies
View Presentation View Presentation Peter W. Forbes, Boston Children's Hospital
CS19 Big Data Tools
Sat, Feb 20, 9:15 AM - 10:45 AM
Chair(s): Michael Wang, The Boeing Company

9:20 AM Machine Learning Variable Selection for Credit Risk Modeling
Jie Chen, Wells Fargo
10:05 AM Quality of Life in NYC
Eunice J Kim, Amherst College
CS20 Statistical Modeling and Bootstrapping
Sat, Feb 20, 9:15 AM - 10:45 AM
Chair(s): Julian Parris, JMP Academic Programs, SAS/University of California, San Diego

9:20 AM Bootstrap in Practice
View Presentation View Presentation Tim Hesterberg, Google Inc.
10:05 AM RealVAMS: An R Package for Fitting a Multivariate Value-Added Model (VAM)
View Presentation View Presentation Jennifer Broatch, Arizona State University
CS21 Maximizing Impact
Sat, Feb 20, 11:00 AM - 12:30 PM
Crystal II
Chair(s): Nancy Wang, Celerion

11:05 AM Acumen: A Critical Skill for All Statisticians
View Presentation View Presentation Sally C. Morton, University of Pittsburgh
11:50 AM Using Humor to Facilitate the Consulting Process
View Presentation View Presentation David R Bristol, Statistical Consulting Services Inc.
CS22 Modeling and Simulation
Sat, Feb 20, 11:00 AM - 12:30 PM
Chair(s): Laura Schweitzer, PricewaterhouseCoopers Advisory Services LLC

11:05 AM Too Saturated: When Too Many Factors Are Too Much in a Supersaturated Design
View Presentation View Presentation Philip Rocco Scinto, The Lubrizol Corporation
11:50 AM Applications of Modeling and Simulation to a Hepatitis Compound Development
View Presentation View Presentation Donghan Luo, Johnson & Johnson
CS23 Data Synthesis and Meta-Analysis
Sat, Feb 20, 11:00 AM - 12:30 PM
Chair(s): Dennis Eggett, Brigham Young University Department of Statsitics

11:05 AM Meta-Analysis Using R
Din Chen, University of North Carolina at Chapel Hill
11:50 AM Political Engineering: Optimal Political Platform Design and the 2004 U.S. Presidential Election
View Presentation View Presentation James J. Cochran, University of Alabama
CS24 Administrative Applications
Sat, Feb 20, 11:00 AM - 12:30 PM
Chair(s): Darryl Penenberg, Receptos

11:05 AM Using Split Modeling and Visualizations to Show Contributing Factors and Predictions for High-Risk Scheduling Activities
View Presentation View Presentation Rhonda Crate, Boeing
11:50 AM R for Record Linkage
View Presentation View Presentation Ahmad Emad, American Institutes for Research
Lunch (on own)
Sat, Feb 20, 12:30 PM - 2:00 PM

T1 Where to Start to Learn the Graphic Basics to R
Sat, Feb 20, 2:00 PM - 4:00 PM
Instructor(s): Min Yoon, Baxalta Inc.

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This two-hour tutorial will introduce the basics of R to beginner users. This free software is powerful and versatile for graphics. This tutorial will go over commonly used packages within R and commands to implement different types of graphs, including histograms, bar graphs, and box plots. To learn a new software program can be daunting; start with this high-level course to get your feet wet!

Outline & Objectives

1. Installing graphic packages into R
2. Importing data including csv and excel data
3. Basic commands to learn the data – types of data and variables
4. Graphs for different types of data:
a. Continuous variables including scatter plot and box plot
b. Categorical/ Ordinal variables – histograms
5. Key to display – Title, Colors, fit many graphs into a page
6. Saving Output graphs in different formats (jpeg, pdf, and etc)

Objectives: To introduce programming of R and to ignite interest beginning R users. This tutorial will able to use simple programming in R to learn the distribution of data and to learn programming to implement code for simple graphs.

About the Instructor

Minjung Yoon is a professional biostatistician in the pharmaceutical industry. She has over 7 years of experience in various phases of clinical trials conducting sample size and power calculations, proposing study design and randomization scheme, writing statistical analysis plans, conducting data analysis and strategic plans for publications, and authoring manuscripts. She has a Master in Public Health from Boston University concentrating in Biostatistics.

Relevance to Conference Goals

This tutorial is relevant to the conference goals since it will help novice users get a little flavor in R in graphics. These basic skills will professionally help applied statisticians effectively communicate data with their clients.

T2 Leader as Coach
Sat, Feb 20, 2:00 PM - 4:00 PM
Instructor(s): Colleen Mangeot, Cincinnati Children's Hospital Medical Center

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This interactive workshop will help leaders empower others for improved performance using the G.R.O.W. (Goals, Reality, Options, Way Forward) coaching model. This world-renown model is used in government, industry, and academia with leaders of all levels to develop direct reports and assist peers and managers in making the best decisions.

Outline & Objectives

Participants will:
1. Learn to distinguish between inside-out and outside-in approaches to performance improvement
2. Learn the G.R.O.W. coaching model
3. Practice the coaching model
4. Identify when the model fails and key distinctions that allow the model to work

About the Instructor

Colleen Mangeot's diverse career includes 10 years in the actuarial field, 10 years in coaching and leadership development, and 6 years in biostatistics. Highlights of her 7 year coaching business include: Successfully working with clients to increase efficiency and sales by 30% or more; Graduate of the world’s largest coach training organization, Coach U; Attained the Professional Coach Certification from the International Coach Federation in 2003; Monthly columnist for the Dayton Business Journal; National speaker with over 200 hours of paid speaking engagements; Contractor with the Anthony Robbins Companies. She received her MS in Statistics from Miami University, received the NSA National Research Council Fellowship at NIOSH, and worked in statistical quality improvement at the VA. Now, in addition to working in the Biostatistical Consulting Unit at Cincinnati Children’s Hospital Medical Center, she is also an internal coach working with executives to further their careers. She was a panelist for the invited session at JSM 2013, Secrets to Effective Communication for Statistical Consultants, and presented two sessions at last years’ Conference on Statistical Practice.

Relevance to Conference Goals

Leaders must have effective ways to communicate with others, whether they be peers, direct reports or managers. This workshop provides a step by step approach to effectively communicating with individuals in a way that allows the leader as well as those the leader is communicating with to grow and learn.

T3 Connecting the Dots Between Social Media and Marketing ROI
Sat, Feb 20, 2:00 PM - 4:00 PM
Instructor(s): Danny Jin, Epsilon Data Management; Eleanor Tipa, Epsilon Data Management
We live in a connected world in which businesses rely on multiple channels to carry out their marketing initiatives. Among them, social media has emerged as a valuable tool that enables marketers to engage with their customers, share product and service information, provide customer service, and reduce customer attrition---all in a personalized way.

With the increased penetration of social media, we’ve seen growing demand for quantitative "proof" of return on investment (ROI) to justify resource allocation decisions. Applied statisticians can play a prominent role to fulfill these needs.

This tutorial will walk attendees through key steps of measuring the impact of social media marketing with three business cases in retail, telecom, and health care. Attendees will learn how to do the following within a social media measurement framework:

- Define a specific measurement objective

- Determine optimal test and control group sizes to ensure adequate power and statistical significance *

- Select appropriate attributes to balance test and control group

- Apply best practice measurement methodology

* Attendees will get a sample sizer tool to help them determine the optimal cell size.

Outline & Objectives

The instructors will share their best practices and lessons learned with three case studies of social media marketing measurement in the retail, telecommunication and healthcare industries. Applied statisticians can gain real world experience in grasping the big picture of social media measurement and following detailed steps with specific techniques to carry out the statistical analysis. Topics to be covered include:

1) Project Planning - building a solid foundation for your social media measurement:

- Identify and prioritize desired learning

- Select your Key Performance Indicators (KPIs) related to your business objective

- Determine your measurement and reporting windows and set up a rollout plan

- Collect and integrate data

2) Statistical Design - ensuring a robust and statistically sound measurement:

- Test and control group size determination

- Balancing attributes and cell assignment

3) Data Analysis - drawing meaningful business conclusions:

- Re-balance the test/control group, if necessary

- Apply the appropriate methodology

- Analysis reporting and recommendations

About the Instructor

Eleanor Tipa, Ph.D.

Eleanor Tipa is a Vice President in the Analytic Consulting Group at Epsilon with over 15 years of experience in marketing analytics across various industries. Her responsibilities include the design, management, and execution of analytic projects that support the optimization of clients’ multi-channel marketing campaigns. Her areas of expertise include the use of advanced statistical techniques in predictive modeling, segmentation and profiling, experimental design, and multi-channel campaign performance measurement.

Eleanor holds a Ph.D. in Statistics from the Pennsylvania State University.

Danny Jin

Danny Jin is a Director in the Analytic Consulting Group at Epsilon with over 8 years of experience in predictive modeling, segmentation, measurement and profiling. Danny’s areas of expertise include modeling techniques, experimental design and multivariate analyses.

Danny holds a M.S. in Applied Statistics from Worcester Polytechnic Institute.

Relevance to Conference Goals

This course will show attendees the industry best practices in measuring social media marketing efforts. The steps that we will go through also demonstrate the vital role applied statisticians can play in quantifying social media marketing ROI. Knowledge of these practices will help broaden participants’ data analysis tool set and give them higher visibility in their organization.

T4 Effective Power and Sample Size Analysis with SAS
Sat, Feb 20, 2:00 PM - 4:00 PM
Crystal II
Instructor(s): John Castelloe, SAS
Sample size determination and power computations are an important aspect of study planning; they help produce studies with useful results for minimum resources. Application areas are diverse, including clinical trials, marketing, and manufacturing. This tutorial presents numerous examples to illustrate the components of a successful power and sample size analysis. The practitioner must specify the design and planned data analysis and choose among strategies for postulating effects and variability. The examples cover proportion tests, t tests, confidence intervals, equivalence and noninferiority, survival analyses, logistic regression, repeated measures, and nonparametric tests. Attendees will learn how to compute power and sample size; perform sensitivity analyses for factors such as variability and type I error rate; and produce customized tables, graphs, and narratives using the POWER and GLMPOWER procedures and the Power and Sample Size application in SAS/STAT software.

Outline & Objectives

1) Overview of concepts
a) Why do power and sample size analysis?
b) Hypothesis testing and confidence intervals
c) Prospective vs. retrospective
2) How-to guide
a) Essential components of study planning
i) Study design
ii) Effects and variability
iii) Planned data analysis
iv) Goals
b) Postulating “effect size” and other parameters
c) Sensitivity analysis
d) Tabular, graphical, and narrative displays
3) Examples
a) Two-sample t test
b) WMW (rank-sum) test
c) Equivalence with lognormal data
d) Noninferiority for two proportions
e) Survival analysis (log-rank test, Cox regression)
f) Logistic regression
g) Repeated measures
h) Confidence interval precision
Attendees will gain an understanding of the essential methods, best practices, and common pitfalls of power and sample size determination.
This is an introductory-level tutorial intended for a broad audience of statisticians. A basic understanding of the theory and practice of statistical inference is assumed.

About the Instructor

John Castelloe is a Senior Research Statistician Developer at SAS and is the developer of power and sample size software in SAS. He has presented many workshops on the topic at statistical conferences and other venues and is the co-author of the chapter “Sample-Size Analysis for Traditional Hypothesis Testing” in the book Pharmaceutical Statistics Using SAS. John received his PhD in Statistics from the University of Iowa in 1998 and joined SAS in 1999.

Relevance to Conference Goals

Over the years, I've heard many people express interest in a tutorial for power and sample size that covers all the basics, highlights best practices and common pitfalls, and focuses on practical issues rather than specific use of software. I've designed this tutorial with exactly these goals in mind, and I think the CSP is an ideal venue for it.

PCD1 Dynamic Documents: A Review of Reproducible Research Tools
Sat, Feb 20, 2:00 PM - 4:00 PM
Diamond I
Instructor(s): Anagha Kumar, MedStar Health Research Institute

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With the demand for data exploration at an all-time high, it is important for statistical analyses to be reproducible. Instead of the copious amount of copying and pasting involved in the generation of a typical statistical report, dynamic documents allow users to embed code and write-up in a document that renders itself immune to the need to copy results. Dynamic documents make reports reproducible, thereby making revisions less onerous and bringing transparency to the mechanism by which an analysis was executed.

PCD2 Predictive Analytics and Quality Control in Health Care
Sat, Feb 20, 2:00 PM - 4:00 PM
Instructor(s): Daniel Griffith, Minitab, Inc.; Eduardo Santiago, Minitab, Inc.
With electronic health records, government regulations, and incentives, data in health care is booming. Health care providers are motivated to use this data to improve the outcomes of patients. We will be exploring this data with different classification techniques to best predict patient outcomes, comparing the pros and cons of each set of models to ensure high-quality predictions. After deploying the model for production use, we need to ensure quality control. Using risk-adjusted control charts [Zhang and Woodall, 2015] will help deliver deep insights into our process. It also will flag us when the model is no longer sufficient and we need to dig back into the prediction stage. In addition, other common issues will be discussed, including how to work with correlated data (which is common in health care) and monitoring the frequency of adverse events.

PCD3 Bayesian Analysis Using Stata
Sat, Feb 20, 2:00 PM - 4:00 PM
Diamond II
Instructor(s): Yulia V. Marchenko, StataCorp LP
This demonstration covers the use of Stata to perform Bayesian analysis. Bayesian analysis is a statistical paradigm that answers research questions about unknown parameters using probability statements. For example, what is the probability that people in a particular state vote Republican or Democrat? What is the probability that a person accused of a crime is guilty? What is the probability that the odds ratio is between 0.3 and 0.5? And many more. Such probabilistic statements are natural to Bayesian analysis because of the underlying assumption that all parameters are random quantities. In Bayesian analysis, a parameter is summarized by an entire distribution of values instead of one fixed value as in classical frequentist analysis. Estimating this distribution, a posterior distribution of a parameter of interest, is at the heart of Bayesian analysis. 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 knowledge of Stata is required, but basic familiarity with Bayesian analysis will prove useful.

PCD4 Applications of Latent Class and Finite Mixture Modeling with Latent GOLD
Sat, Feb 20, 2:00 PM - 4:00 PM
Instructor(s): Jay Magidson, Statistical Innovations

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Over the past 10 years, latent class (LC) modeling has grown rapidly in use across a wide range of disciplines. As more applications are discovered, it is no longer known only as a method of clustering individuals based on categorical variables, but rather as a general modeling tool for accounting for heterogeneity in data. Vermunt and Magidson (2003) defined it more generally as virtually any statistical model where “some of the parameters … differ across unobserved subgroups.”

In our presentation, we use the Latent GOLD program to illustrate a wide variety of applications in which the common thread is that latent classes (segments) are identified that are homogeneous in the sense of having similar response patterns (cluster analysis), having similar growth patterns (latent growth or transition models), or being identical with respect to certain regression coefficients (LC regression models). Such models address modern goals, such as to identify the particular therapy that works best for a particular patient (individualized medicine).

LC models can be applied with categorical or continuous variables or a combination of categorical, continuous, and count variables. Moreover, LC factor (also known as discrete factor) models can be used to group variables similar to factor analysis, but since the latent variables themselves are discrete, they also can be used to identify homogeneous segments of cases.

GS2 Closing General Session
Sat, Feb 20, 4:15 PM - 5:30 PM
The closing session is an important opportunity for attendees to interact with the CSP Steering Committee in an open discussion about how well the overall objectives of the conference were met. CSPSC vice chair, MoonJung Cho, will lead a panel of committee members as they summarize their conference experience. The audience will then be invited to ask questions and provide feedback.

The committee highly values suggestions for improvements gathered during this time. You will have an opportunity to win door prizes and witness awarding of the best student posters. The closing session is also a great time to let members of the CSPSC know if you are interested in helping out with future conferences.