Online Program

Statisticians and data scientists must communicate and collaborate with domain experts from many different fields in academia, business, and government. Learning more effective communication and collaboration skills will enable us to maximize our professional impact in these areas. In this short course, participants will learn and practice essential skills that will enable them to improve their communication and collaboration to add more value to their projects, customers, and organizations. We introduce the ASCCR framework that describes our current best practices for five aspects of statistical consulting and collaboration (Attitude-Structure-Content-Communication-Relationship). We will focus especially on the communication skills of asking great questions; listening, paraphrasing, and summarizing; and explaining statistics to non-statisticians to create shared understanding with our clients and collaborators. Participants will practice these skills via team exercises, role-plays, video coaching, and individual reflections to become more effective communicators and collaborators, enabling them to have greater impact in their roles as statisticians and data scientists.

Outline & Objectives

Our objective is to help participants improve their communication and collaboration skills so they can achieve greater impact. This short course will be useful for all levels from beginning to advanced. Prerequisites are a desire to improve one’s personal effectiveness and openness to try new methods and ways of thinking in the practice of statistics and data science.

1 Welcome, team assignments, and warm-up exercises
2 Introduction to ASCCR Frame
3 Attitude of effective collaboration (checklist and exercise)
4 POWER structure (Prepare-Open-Work-End-Reflect) produces effective meetings
5 Best practices for opening meetings (Eric and Heather mock role play, video review, then participants role play)
6 Q1Q2Q3 approach (reflection exercise)
7 Triangle of Statistical Communication
a. Asking Great Questions (participant role play)
b. Listening, Paraphrasing, Summarizing (video clip review)
c. Explaining Statistics to Non-statisticians (video clip and role play)
d. Creating Shared Understanding
8 Strengthening Relationships (reflection exercise)
9 Best practices for ending meetings (participants role play)
10 Individual plans for improving communication and collaboration.

About the Instructor

For the past 13 years, Dr. Eric Vance has been the director of LISA (Laboratory for Interdisciplinary Statistical Analysis) where he has trained 285 statisticians and data scientists to move between theory and practice to collaborate with 9700+ domain experts to apply statistics and data science to answer their research, business, or policy questions. He has taught workshops and webinars on collaboration in nine countries, including several in collaboration with Heather Smith at CSP and JSM. This workshop gets better every time they teach it.

Heather Smith has 30 years of experience consulting with academic, industrial, service, and government clients in the United States, Europe, and Asia. She began this work as a statistical consultant at Westat, Inc. For 23 years she has been a faculty member in the Statistics Department at Cal Poly San Luis Obispo where she consults with academic and private sector researchers and teaches a wide variety of applied statistics courses, including courses in statistical communication and consulting. She has offered over a dozen workshops, short courses, and webinars on these topics, and has trained hundreds of statistical collaborators.

Relevance to Conference Goals

This short course is relevant to Theme 1 and 4. Participants will learn new skills and practical tips to apply whenever they interact with other people. Participants will explicitly learn how to better communicate and collaborate with their clients and customers. Skills learned in the course will equip participants to have a positive impact on their organization and an upward career trajectory. Participants will return to their jobs with new ideas, techniques, and strategies to improve their ability to communicate and collaborate effectively, resulting in a greater impact on their organizations and increasing the overall impact of statistics and data science.

A version of this course was taught at the 2018 CSP and received a high average rating of 4.63 out of 5 (n=8 responding out of 22 participants). The official qualitative feedback we received: “This course is essential for any statistician who needs to collaborate with people in other disciplines, or sell their business to clients. I very strongly recommend it.” Unofficial feedback was very positive as well. A version of this course was also taught at 2020 CSP, but we don’t recall receiving any official feedback.

This is an introductory course to provide a hands-on introduction to Python, the well-known open-source programming language for analytics. We will start with an introduction to Jupyter Notebook and Python basics, then the most popular data science libraries (Numpy and Pandas), data visualization libraries (Matplotlib, Seaborn), and machine learning library (Sklearn).

We will introduce a Predictive Analytics Life Cycle Process through a case study to methodically expose attendees to best practices and Python’s rich set of data science libraries, providing hands-on experience and know-how. Lastly, we will use the course material to develop a predictive model from raw data (data TBD). Python code will be provided.

Outline & Objectives

1. Introduction to Jupyter Notebook and Python Basics
2. Introduction to Data Science Libraries: NumPy and Pandas
3. Introduction to Data Visualization and Interactive Data Visualization Libraries: Matplotlib, Seaborn, Plotly, and Clufflinks
4. Introduction to A Life Cycle of Predictive Analytics Process through A Case Study and Machine Learning Library Sklearn
a). Data Exploratory Analysis and Data Pre-Processing
b). Supervised Learning: Regression (Linear, Multiple Linear, Polynomial Regression, Decision Tree, and Random Forest)
c). Supervised Learning: Classification (KNN, Logistic Regression, Decision Tree, and Random Forest)
d). Unsupervised Learning: K-mean Clusters and Principal Component Analysis (Dimensionality Reduction)
5. Using all the above to develop a Predictive Model (data TBD): start from Raw Data Exploratory Analysis, Data Visualization, Data Preparation, Feature Engineering, and Model Building (using Logistic Regression, Decision Tree, Random Forest and Model Performance Evaluation)

About the Instructor

Mrs. Mei Najim is currently teaching Programming for Analytics (R & Python) part time at The University of Chicago. Mei has 16 years of hands-on analytics experience in claim management, underwriting, pricing, reserving, and catastrophe risk management in the insurance industry and collections analytics in the banking industry. Since 2007, she has been mainly working and leading various levels of predictive analytics projects to develop analytics capability for financial organizations. She has frequently presented at conferences to share her expertise. Mei holds a BS degree in Actuarial Science from Hunan University and two MS degrees, one in Applied Mathematics and the other in Statistics, from Washington State University. Mei is a member of the American Statistical Association and a Certified Specialist in Predictive Analytics (CSPA) of the Casualty of Actuary.

Relevance to Conference Goals

The objective is to provide attendees with practical knowledge about using Python programming to analyze data and develop a life cycle predictive analytics through the application of state-of-the-art statistical methods and machine learning algorithms.

Real world data and evidence (RWD&E) have been increasingly used in drug development and regulatory decision-making since the passage of the 21st Century Cures Act on December 2016 and the issuance of the FDA’s RWE framework in December 2018. Whereas pharmaceutical companies use RWD&E to support clinical development activities and to seek evidence to inform health technology assessment (HTA) decisions, the healthcare community uses RWD&E to develop guidelines and decisions to support medical practice and to assess treatment patterns, costs and outcomes of interventions. Although high performance computing tools, artificial intelligence and machine learning algorithms have been conveniently applied to RWD, there are still substantial challenges in deriving RWE from RWD and in using the RWE in drug development and healthcare decision-making. This short course aims to provide the audience with practical interdisciplinary approaches and applications using RWD&E in product development, regulatory decision-making, and healthcare delivery, with case studies given throughout the presentation.

Outline & Objectives

Course learning objectives: The audience will learn the commonly used as well as cutting-edge decision-analytics approaches that are tailored for specific questions in product development, and regulatory and healthcare decision-making. Case studies are given throughout the presentation of the short course to illustrate the applications of the methods.

1. Introduction
2. Real World Data
3. Statistical and Machine Learning Methods for Healthcare Decision Analysis
4. Disease Diagnosis, Patient Heterogeneity and Adherence
5. Health Technology and Health Economic Assessment
6. Risk Models and Outcome Prediction
7. Benet-Risks Assessment
8. Causal Inference Using Real World Data
9. Analysis of Data Generated from Mobile Devices
10. Public Health Surveillance and Pharmacovigilance
11. Real World Data to Support Clinical Development
12. Pragmatic Trials and CER Trials

About the Instructor

Presenters' background:
1. Tze Leung Lai, PhD: Ray Lyman Wilbur Professor of Statistics and of Biomedical Data Science in the School of Medicine and of the Institute for Computational & Mathematical Engineering (ICME) in the School of Engineering, Stanford University, Director of Financial and Risk Modeling Institute, and Co-Director of Center for Innovative Study Design at the Stanford School of Medicine, IMS and ASA Fellow.
2. Jie Chen, PhD: Senior Vice President and head of Biometrics, Overland Pharmaceuticals and a visiting member of the Center for Innovative Study Design, Stanford University.
3. Richard Baumgartner, PhD: Sr. Principal Scientist with Biometrics Research Department, Biostatistics and Research Decision Sciences (BARDS), Merck and Co.

Relevance to Conference Goals

This short course will provide the best practice of statistics in the areas of real-world data and evidence to support drug development and regulatory decision making.