Web-Based Lectures





Title: Introductory Overview Lectures in Social Network
Presenters: Daniel Sewell, University of Iowa, Tianxi Li, University of Virginia, and Purnamrita Sarkar, University of Texas at Austin
Date and Time: Friday, April 3, 2020, 2:30pm-4:00pm. Eastern Time
Sponsor: Statistical Learning and Data Science Section

Registration Deadline: Wednesday, April 1, at 12:00 p.m. Eastern time

Description:
The vision for social network analysis is to characterize networked structures of individuals. As we are entering a big-data era when rich information about individual behavior is collected, analyzing the connection and dependence among individuals is drawing great attention, especially in the fields of social media networks, knowledge transmission, organizational studies and even anomaly detection. In this webinar, we will introduce exploratory network data analysis, visualization, static network modeling, and optimization for network based inference. Three experts will illustrate their experiences and knowledge from different perspectives. Practical examples will be discussed. The webinar aims at providing an introduction of modern social network analysis to non-statisticians, statisticians and data scientists with varying statistical knowledge.

Registration:
SLDS Section Members: $0
ASA Members: $15
Nonmembers: $30

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 Wednesday, April 1, with the access information to join the webinar and the link to download and print a copy of the presentation slides.




Title: Hazards of Hazard Ratios in Survival Analysis
Presenter: L. J. Wei, Harvard University
Date and Time: Tuesday, April 7, 2020, 12:00 p.m. – 1:30 p.m. Eastern time
Sponsor: Biopharmaceutical Section

Registration Deadline: Friday, April 3, at 12:00 p.m. Eastern time

Description:
In a longitudinal clinical study to compare two groups, the primary end point is often the time to a specific event (for example, disease progression, death). The hazard ratio estimate is routinely used to empirically quantify the between-group difference under the assumption that the ratio of the two hazard functions being approximately constant over time. Even when this assumption is plausible, such a ratio estimate may not give us a clinically meaningful summary of the group contrast due to lack of a reference value of hazard function from the control arm. Moreover, the clinical meaning of such a ratio estimate is difficult, if not impossible, to interpret when the underlying proportional hazards assumption is violated (namely, the hazard ratio is not constant over time). For this case, the hazard ratio-based tests may not have power to detect the group difference. In this talk, we summarize several critical concerns regarding this conventional practice and discuss alternatives (e.g., via the t-year mean survival time) for quantifying the differences between groups with respect to a time-to-event end point. The data from several recent cancer and cardiovascular clinical trials, which reflect a variety of scenarios, are used throughout to illustrate our discussions. In this talk, we are mostly interested in estimation the treatment effect beyond the hypothesis testing paradigm. An estimation procedure can also be used as a test statistic. On the other hand, most tests in survival analysis, such as the weighted logrank tests, do not have appropriate estimation counterparts. We will also discuss other relevant issues in clinical studies, for example, estimating the duration of response, quantifying long term survival et al.

Presenter:
L.J. Wei is a professor of Biostatistics at Harvard University. Before joining Harvard, he was a professor at University of Wisconsin, University of Michigan, and George Washington University. His main research interest is in the clinical trial methodology, especially in design, monitoring and analysis of studies. He has developed numerous novel statistical methods which are utilized in practice. He received the prestigious Wald Medal in 2009 from the American Statistical Association for his contribution to clinical trial methodology. He is a fellow of American Statistical Associating and Institute of Mathematical Statistics. In 2014, to honor his mentorship, Harvard School of Public Health established a Wei-family scholarship to support students studying biostatistics. His recent research area is concentrated on translational statistics, the personalize medicine under the risk-benefit paradigm via biomarkers and revitalizing clinical trial methodology. He has more than 200 publications and served on numerous editorial and scientific advisory boards. L. J. Wei has extensive working experience in regulatory science for developing and evaluating new drugs/devices.

Registration:
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).

THIS WEBINAR HAS REACHED REGISTRATION CAPACITY.

Access Information
Registered persons will be sent an email the afternoon of Friday, April 3, with the access information to join the webinar and the link to download and print a copy of the presentation slides.




Title: Incorporate External Control Data in New Clinical Trial Design and Analysis
Presenter: Lanju Zhang, PhD, Abbvie Inc
Date and Time: Thursday, June 11, 2020, 12:00 p.m. – 1:30 p.m. Eastern time
Sponsor: Biopharmaceutical Section

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

Description:
Many clinical trials are designed with plenty of external control data available. To reduce the ever-increasing cost and timeline of the clinical drug development, external control data are incorporated in clinical trial design and analyses. Two methods can be applied. The first method is to incorporate summary level information into the new trial through a Bayesian framework. Bayesian methods include power prior approach, commensurate prior approach and mixture prior approach. In this presentation, we will discuss a newly proposed statistical approach which combines elements of all the three approaches mentioned above but admits a closed-form formula for easy implementation. The second method is to incorporate subject level external data through propensity score methods. In this presentation, we will share our experiences of using propensity score matching to create a synthetic control arm. We will demonstrate these approaches with case studies and share our interaction experiences with regulatory agencies.

Bio:
Dr. Lanju Zhang is Director in Statistics and Research Fellow at the department of Data and Statistical Sciences at AbbVie. He leads a group providing statistical support to specialty immunology clinical programs. His research interests include adaptive design, multi-region clinical trials, real world evidence, and nonclinical statistics. He has published three books and more than 40 peer-refereed papers. He is a Fellow of American Statistical Association and an Associate Editor of Journal of Biopharmaceutical Statistics. He received his PhD in Statistics from University of Maryland Baltimore County.

Registration:
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 Tuesday, June 9, with the access information to join the webinar and, if possible, a link to download and print a copy of the presentation slides.




Title: The Leadership Laboratory: Using Observational Study to Develop Leadership Skills for Statisticians
Presenter: Dr. Gary Sullivan
Date and Time: Thursday, June 18, 2020, 12:00 p.m. – 1:30 p.m. Eastern time
Sponsor: Biopharmaceutical Section

Registration Deadline: Tuesday, June 16, at 12:00 p.m. Eastern time

Description:
Where do you start if you want to improve your leadership? Take a leadership course? Read a leadership book? Find an experienced mentor? All of those are good options but consider one more: Leadership practice - both good and bad - is on display every day right before your eyes. Each of you is living in a leadership laboratory where observational study can provide great leadership learning.

You can observe business partners and statistical colleagues applying and practicing communication techniques, negotiation tactics, and influence strategies in your meetings, interactions, and collaborative projects. In this presentation, I will discuss some very simple practices which provide powerful lessons on influence, and I will point out the most important leadership skills for statisticians to study. I will also demonstrate through personal experiences and examples how to identify leadership concepts, assess their application, and use the information to build and improve your skills.

Presenter:
Dr. Gary Sullivan is a Leadership Specialist and Statistical Scientist at Espirer Consulting, LLC where he provides leadership training and guidance primarily to statisticians and data scientists in the pharmaceutical industry. He is the primary developer and instructor for leadership training within the ASA, including courses on the topics of business acumen, cultural competence, decision influence, and executive presence. He is also the co-developer and primary instructor for the Effective Statistician Leadership Program, an on-line leadership program for statisticians in the pharmaceutical industry. He has provided leadership training to over 400 professionals within the pharmaceutical industry and the ASA.

Dr. Sullivan retired from Eli Lilly and Company in 2017 as the Senior Director for Non-Clinical Statistics. He joined Eli Lilly in 1989 and held various technical and administrative roles over his 28 years there. While at Eli Lilly, he co-developed a leadership program for their statistics function and led the administration of that program from 2009–2017. He brought aspects of that program to the American Statistical Association (ASA) and led the development and instruction of the first leadership course at the Joint Statistical Meetings (JSM) in 2014. He has organized and/or taught this course at JSM from 2015 to 2018, and served as the Chair of the ASA Ad Hoc Leadership Committee for 2017-2018.

Dr. Sullivan has organized and participated on leadership panels, presented several leadership presentations, and has authored many articles on leadership development for statisticians. He holds a Bachelor’s degree in Statistics from the University of Pittsburgh, and both a Master’s and Doctorate in Statistics from Iowa State University.

Registration:
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 Tuesday, June 9, with the access information to join the webinar and, if possible, a link to download and print a copy of the presentation slides.