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Saturday, February 20
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.