Web-Based Lectures




Title: Creating Statistical Graphics for Clinical Trial Outcomes Using SAS®
Presenter: Warren Kuhfeld, SAS
Date and Time: Thursday, May 11, 2017, 12:00 p.m. – 1:30 p.m. Eastern time
Sponsor: Section for Statistical Programmers and Analysts

Registration Deadline: Tuesday, May 9, at 12:00 p.m. Eastern time

Description:
ODS Graphics provides functionality for creating statistical graphics. ODS Graphics is available in the Base SAS®, SAS/STAT®, SAS/ETS®, and SAS/QC® products. More than 100 statistical procedures use ODS Graphics, and they produce graphs as automatically as they produce tables. ODS Graphics also provides Base SAS procedures such as PROC SGPLOT that produce plots for exploratory data analysis and for customized statistical displays. If you are a medical, pharmaceutical or life sciences researcher, you have probably analyzed time-to-event data (survival data). The LIFETEST procedure computes Kaplan-Meier estimates of the survivor functions and compares survival curves between groups of patients. You can use the Kaplan-Meier plot to display the number of subjects at risk, confidence limits, equal-precision band, Hall-Wellner band, and homogeneity test p-value. You can control the contents of the survival plot by specifying procedure options with PROC LIFETEST. When the procedure options are insufficient, you can add SG annotation or modify the graph template by using a SAS macro. Survival and other responses to treatment can instead be displayed by using waterfall plots—the horizontal axis displays a baseline, and bars extend above or below the axis showing deviations from the baseline. Life sciences researchers also need to track adverse events (AEs) among clinical trial participants. Reports can include combinations of tables and graphs. PROC SGPLOT uses axis tables to display tabular information next to scatter plots, bar charts, and other graphs. With proper data preparation, you can easily create multipage graphical reports.

This webinar begins with a brief introduction to basic ODS Graphics. Advanced topics include: creating and customizing the Kaplan-Meier plot, adverse events plots, and waterfall plots. Advanced techniques include template modification, axis tables, multipage reports, attribute maps, and SG annotation.

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 now closed.

Access Information
Registered persons will be sent an email the afternoon of Tuesday, May 9, with the access information to join the webinar and the link to download and print a copy of the presentation slides.



Title: How to be a Successful Independent Statistical Consultant
Presenters: Elaine Eisenbeisz, Kim Love, and Karen Grace-Martin
Date and Time: Thursday, June 1, 2017, 2:00 p.m. – 3:30 p.m. Eastern time
Sponsor: Section on Statistical Consulting

Registration Deadline:Tuesday, May 30, at 12:00 p.m. Eastern time

Description:
In this webinar, we will hear from three established self-employed statistical consultants about what it takes to start and run a statistical consulting business. Even if you freelance just a few hours a week on top of your full-time job, you’ve now become a business owner, which requires a whole new set of skills, support, and priorities. In this panel discussion, Elaine Eisenbeisz, Kim Love, and Karen Grace-Martin will answer 13 questions about how they approach client acquisition and service, setting fees, contracts, a support team, and share some wisdom about the challenges and rewards of owning your own statistical consulting business.

About the Presenters:
Kim Love is the owner of and lead consultant at K. R. Love Quantitative Consulting and Collaboration. She has worked as a statistical consultant and collaborator in multiple professional roles, most recently as the associate director of the University of Georgia Statistical Consulting Center. While she enjoys working with clients with varied backgrounds, she particularly enjoys working with those who feel they have a less-than-perfect relationship with statistics. One of her goals is to spread an appreciation of statistics across many fields of study, starting by making it understandable to those who interact with it. She has a B.A. in mathematics (2003) from the University of Virginia, and an M.S. (2004) and Ph.D. (2007) in statistics from Virginia Tech.

Elaine Eisenbeisz is a private practice statistician and owner of Omega Statistics, a statistical consulting firm based in Southern California.Elaine has over 30 years of experience in creating data and information solutions. She designs methodology and analyzes data for studies in the clinical, and biotechnology fields. Additionally, Elaine and Omega Statistics are the go-to resource for ABD students who require assistance with dissertation methodology and analysis. Throughout her tenure as a private practice statistician, Elaine has published work with researchers and colleagues in peer-reviewed journals. Fitting of her eclectic tastes, her current interests include statistical genetics and psychometric survey development. Elaine earned her B.S. in Statistics at UC Riverside and her Master’s Certification in Applied Statistics from Texas A&M. She is currently finishing her graduate studies at Rochester Institute of Technology. Elaine is a member in good standing with the American Statistical Association and a member of the Mensa High IQ Society.

Karen Grace-Martin is the founder and president of The Analysis Factor, which offers statistical consulting, training, and mentorship. She is the host of Statistically Speaking, a unique online membership, which offers professional development and community to statisticians and quantitative researchers. Karen has a master’s degrees in applied statistics and social psychology. Her own career started in psychology research, where her frustration in applying statistics to her data led her to learn more statistics. She was a statistical consultant at Cornell University for seven years before founding The Analysis Factor. She has helped thousands of researchers across many disciplines figure out the best way to analyze their data and then implement it. Before consulting, Karen taught statistics courses for economics, psychology, and sociology majors at the University of California, Santa Barbara and Santa Barbara City College. She has co-written an introductory statistics textbook with Stephen Sweet: Data Analysis with SPSS.

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 Tuesday, May 30, with the access information to join the webinar and the link to download and print a copy of the presentation slides.



Title: Key Multiplicity Issues in Clinical Trials
Presenter: Alex Dmitrienko
Date and Time: Friday, June 2, 2017, 12:00 p.m. – 2:00 p.m. Eastern time
Sponsor: Biopharmaceutical Section

Registration Deadline: Wednesday, May 31, at 12:00 p.m. Eastern time

Description:
The webinar will review key multiplicity issues arising in confirmatory clinical trials with multiple objectives, including multiple endpoints, dose-control comparisons and patient populations, etc. An overview of multiplicity adjustments used in traditional problems with a single source of multiplicity as well as recent advances in this area, including methods for “multidimensional” multiplicity problems (gatekeeping procedures), will be presented. Gatekeeping procedures have attracted much attention in clinical trials with complex multiple objectives due to the fact that they enable trial sponsors to enrich product labels by including information on relevant secondary objectives. The webinar will offer a well-balanced mix of theory and applications with case studies based on real clinical trials and a detailed discussion of regulatory considerations, including the FDA’s recently released draft guidance on multiple endpoints.

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

Register

Access Information
Registered persons will be sent an email the afternoon of Wednesday, May 31, 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).

Register

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.