Web-Based Lectures


November 15, 2017

November 29, 2017



Title: A Jump-start to R for Statistical Programmers and Analysts
Presenter: Kelly McConville, Swarthmore College
Date and Time: Wednesday, October 18, 2017, 12:00 p.m. – 1:00 p.m. Eastern time
Sponsor: Section for Statistical Programmers and Analysts

Registration Deadline: Monday, October 16, at 12:00 p.m. Eastern time

Description:
This webinar will provide an introduction to the statistical programming language, R. It will be geared toward statistical programmers and analysts who are new to R. We'll address why R is an attractive program to use and will provide tips and materials to get started. For each step of the data analysis process (data wrangling, visualization, modeling, etc.), we will go through useful R functions and packages and will see examples of executing these steps in R. In particular, we will use the dplyr package for data wrangling, the ggplot2 package for visualization, and the caret package for modeling. We will also discuss and compare the different coding philosophies within the R community, such as base R versus the tidyverse. We will explore how to develop a reproducible workflow using R Markdown documents, which seamlessly weave together text, code, and output. After this webinar, participants will have the necessary tools to get started in R.

It is recommended that the participants install R or R studio download before the session in order to enhance the learning experience.

Registration Fees:
Member of the Section for Statistical Programmers and Analysts: $0
ASA Member: $59
Nonmember: $74

Each registration is allowed one connection to the webinar. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).

Registration is closed.

Access Information
Registered persons will be sent an email the afternoon of Monday, October 16 with the access information to join the webinar and the link to download and print a copy of the presentation slides.



Title: Network Analysis in Cross-sectional Data Using R
Presenter: Eiko Fried
Date and Time: Thursday, October 19, 2017, 12:00 p.m. – 2:00 p.m. Eastern time
Sponsor: Mental Health Statistics Section

Registration Deadline: Tuesday, October 17, at 12:00 p.m. Eastern time

Description:
Analysis of mental health data is usually based on sum-scores of symptoms or the estimation of factor models. Both types of analyses disregard direct associations among symptoms that are well-understood in clinical practice: mental disorders can be conceptualized as vicious circles of problems that are hard to escape. A novel research framework, the network perspective on psychopathology, understands mental disorders as complex networks of interacting symptoms. Despite its comparably recent debut, with conceptual foundations in 2008 and empirical foundations in 2010, the framework has received considerable attention and recognition in recent years.

In this webinar, we will use R to learn about (1) network estimation, (2) network inference, and (3) network stability in cross-sectional data. Regarding network estimation, the state-of-the-art network model for cross-sectional data is the pairwise Markov Random Field or regularized partial correlation network that estimates the conditional dependence relations among items. We will learn to estimate appropriate network models for our data: the Ising Model for binary data, and the Gaussian Graphical Model for metric data. In this first section, we will also cover regularization methods that avoid the estimation of false positive associations in networks. The second topic, network inference, covers graph theoretical measures such as centrality that allow us to interpret networks. What symptoms are most connected with other symptoms? Finally, network stability allows us to gain insight into the robustness of our networks. We conclude the webinar with advanced methods such as the statistical comparison of networks, and how to deal with ordinal and mixed data. Is it noteworthy that network analysis is not limited to psychopathology data, but has been employed to study other psychological constructs such as intelligence, personality traits, and attitudes.

Registration Fees:
Member of the Mental Health Statistics Section: $60
ASA Member: $90
Nonmember: $110

Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).

Registration is closed.

Access Information
Registered persons will be sent an email the afternoon of Tuesday, October 17, with the access information to join the webinar and the link to download and print a copy of the presentation slides.



Title: Regression Models for Censored Data: When it's NOT a good idea to use PH, AFT and other such models?
Presenter: Sujit Ghosh
Date and Time: Tuesday, November 14, 2017, 12:00 p.m. – 2:00 p.m. Eastern time
Sponsor: Biopharmaceutical Section

Registration Deadline: Friday, November 10, at 12:00 p.m. Eastern time

Description:
In many clinical applications of survival analysis with covariates, majority of practitioners routinely choose to use proportional hazard (PH) based regression models when in fact it may not be appropriate and may even lead to erroneous inference. The commonly used semiparametric assumptions (e.g., AFT, PH and proportional odds, etc.) may turn out to be stringent and unrealistic, particularly when there is scientific background to believe that survival curves under different covariate combinations will cross during the study period. This webinar presents a very flexible class of nonparametric regression models for the conditional hazard function. The methodology presented is known to have three key features: (i) the smooth estimator of the conditional hazard rate has been shown to be a unique solution of a strictly convex optimization problem for a wide range of applications; making it computationally attractive, (ii) the model has been shown to encompass a proportional hazards structure, and (iii) large sample properties including consistency and convergence rates have been established under a set of mild regularity conditions. Following a brief introduction of the newly proposed methodology, the webinar will focus more on illustrating the empirical performances of the methods using several simulated and real case studies. The attendees are encouraged to read the published paper and related R codes will be provided at the webinar.

Registration Fees:
Biopharmaceutical Section Members: $0
ASA Members: $59
Nonmembers: $74

Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).

Register

Access Information
Registered persons will be sent an email the afternoon of Friday, November 10, with the access information to join the webinar and the link to download and print a copy of the presentation slides.



Title: Databases in the Tidyverse
Presenters: Ben Baumer, Smith College Program in Statistical and Data Sciences and Nicholas Horton, Amherst College Department of Mathematics and Statistics
Date and Time: Wednesday, November 15, 2017, 1:00 p.m. – 2:30 p.m. Eastern time
Sponsor: Section on Statistical Consulting

Registration Deadline: Monday, November 13, at 12:00 p.m. Eastern time

Description:
The dplyr package within R provides a flexible and powerful syntax for data wrangling operations. However, data objects within R are typically stored in memory and performance issues may arise as data become large. Database management systems implementing SQL (structured query language) provide a ubiquitous architecture for storing and querying data that is relational in nature. While there has been support for data retrieval in R from relational databases such as MySQL, SQLite, and Postgres, recent advances that have added interfaces between R and SQL enable users to seamlessly leverage storage and retrieval mechanisms while remaining within R. In this webinar, we will review key idioms for data wrangling within dplyr, introduce the backend interfaces for common database systems, provide examples of ways that the dplyr engine translates a data pipeline, and discuss common misconceptions and performance issues.

About the Presenters:
Benjamin S. Baumer is an assistant professor in the Statistical & Data Sciences program at Smith College. He has been a practicing data scientist since 2004, when he became the first full-time statistical analyst for the New York Mets. Ben is a co-author of The Sabermetric Revolution and Modern Data Science with R, and won the 2016 Contemporary Baseball Analysis Award from the Society for American Baseball Research.

Nicholas Horton is Professor of Statistics at Amherst College, with methodologic research interests in longitudinal regression models, missing data methods, and statistical computing. He graduated from the Harvard TH Chan School of Public Health in 1999. Nick has received the ASA's Founders Award, the Waller Education Award, the William Warde Mu Sigma Rho Education Award, and the MAA Hogg Award for Excellence in Teaching. He has published more than 160 papers, co-authored a series of four books on statistical computing and data science, and was co-PI on the NSF funded MOSAIC project. Nick is a Fellow of the ASA, served as a member of the ASA Board, chairs the Committee of Presidents of Statistical Societies and is the past-chair of the ASA Section on Statistical Education. He is a member of the National Academies of Sciences Committee on Applied and Theoretical Statistics and two Academy studies on undergraduate data science education.

Registration Fees:
Member of the Section on Statistical Consulting: $40
ASA Member: $65
Nonmember: $85

Each registration is allowed one web connection and one audio connection. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).

Register

Access Information
Registered persons will be sent an email the afternoon of Monday, November 13, with the access information to join the webinar and the link to download and print a copy of the presentation slides.



Title: Introduction to Data Science
Presenters: Hui Lin, Data Scientist, DuPont Pioneer
Date and Time: Wednesday, November 29, 2017, 12:00 p.m. – 1:00 p.m. Eastern time
Sponsor: Section for Statistical Programmers and Analysts

Registration Deadline: Monday, November 27, at 12:00 p.m. Eastern time

Description:
Do you want to be a data scientist? Do you know how to be a data scientist? Do you understand what data scientist do? With the data science hype picking up stream, many professionals changed their titles to Data Scientist without any of the necessary qualifications which lead to confusion in the market and obfuscation in resumes. This webinar aims to give the audience a better depiction of data science and what data scientists “in the wild” are doing. If you are interested in becoming a data scientist, this webinar will help you better prepared.

Registration Fees:
Member of the Section for Statistical Programmers and Analysts: $0
ASA Member: $59
Nonmember: $74

Each registration is allowed one connection to the webinar. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).

Register

Access Information
Registered persons will be sent an email the afternoon of Monday, November 27 with the access information to join the webinar and the link to download and print a copy of the presentation slides.



Title: Small Data, N-of-1 Trials, and Personalized Medicine
Presenters: Naihua Duan (Columbia University) and Richard Kravitz (UC Davis)
Date and Time: Tuesday, December 5, 2017, 12:00 p.m. – 2:00 p.m. Eastern time
Sponsor: Mental Health Statistics Section

Registration Deadline: Friday, December 1, at 12:00 p.m. Eastern time

Description:
Advances in biomedical science have led to rapid replacement of "one size fits all" therapeutic strategies by a more individualized approach. While big data analytics have been deployed extensively in recent years for applications in personalized medicine, small data studies such as N-of-1 trials have the potential to further advance the methodological underpinnings of personalized medicine.

In this webinar, we will discuss the conceptual framework for small data studies, and the design and implementation of N-of-1 trials – multiple cross-over trials within individual patients to inform each individual’s own clinical or lifestyle decision-making. Broad applications of small data studies, including N-of-1 trials, have become feasible in recent years with advances in personal communication and information technologies. It is timely for the biostatistics community to begin to serve the needs of savvy consumers wanting to take an active role in enhancing their own health, as demonstrated in the extensive practice of self-tracking and self-experimentation among members of QuantifiedSelf.com.

This is an emerging area in which biostatisticians can help transform health care delivery by supplementing the traditional "top down" organization of knowledge production and deployment with a new "bottom up" paradigm, in which biostatistical methods are applied directly in day-to-day clinical care and lifestyle decisions for individual patients. This new paradigm may in turn expand the constituency for biostatistics, empowering and engaging the lay population to participate actively and directly in the practice of creating and harvesting small data using personalized biostatistics tools and apps.

Registration Fees:
Member of the Mental Health Statistics Section: $60
ASA Member: $90
Nonmember: $110

Each registration is allowed one web connection and one audio connection. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).

Register

Access Information
Registered persons will be sent an email the afternoon of Friday, December 1, with the access information to join the webinar and the link to download and print a copy of the presentation slides.