Web-Based Lectures





Title: Subgroup Analysis Identification: The Hardest Problem There Is
Presenter: Stephen J. Ruberg, PhD, President, Analytix Thinking, LLC
Date and Time: Tuesday, February 4, 2020, 12:00 p.m. – 2:00 p.m. Eastern time
Sponsor: Biopharmaceutical Section

Registration Deadline: Friday, January 31, at 12:00 p.m. Eastern time

Description:
There have been many cautionary publications warning about findings from subgroup analyses, and rightfully so. Quite often positive subgroup claims are based on exploratory analysis or post hoc assessments after data have been unblinded, and quite often those findings are not reproduced in subsequent research. Consequently, the assessment of subgroups in clinical trials is viewed skeptically and even dismissed. And yet, we know there is inherent heterogeneity of treatment effects, and the push for more tailored therapeutics and personalized medicine demands that we look for subgroups of patient who may have a differential treatment effect – either positive or negative. This talk will explore the balance between the skepticism associated with subgroup analysis and the optimism with subgroup identification. Some perspectives on quantifying confidence in subgroup findings will be presented as well as some different ideas on the notion of defining subgroups. The goal is to make our thinking patient centric and to hopefully improve our approaches for getting the right medicines to the right patients.

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, January 31, with the access information to join the webinar and the link to download and print a copy of the presentation slides.




Title: Review of Precision Medicine in Chronic Illness
Presenter: Eric Laber
Date and Time: Tuesday, February 11, 2020, 12:00 p.m. – 2:00 p.m. Eastern time
Sponsor: Mental Health Statistics Section

Registration Deadline: Friday, February 7, at 12:00 p.m. Eastern time

Description:
The vision for precision medicine is to tailor interventions to the evolving health status of each patient to optimize long term health outcomes. In this webinar we examine the state-of-the-art of precision medicine with a focus on applications in mental health. An outline of the webinar is as follows: we will formalize an optimal treatment regime using the language of potential outcomes, discuss assumptions needed to identify these regimes from randomized or observational data, review existing methods for estimation, introduce SMART and MRT designs for precision medicine, and then close with a discussion of active research areas and open problems.

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

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 Friday, February 7, with the access information to join the webinar and the link to download and print a copy of the presentation slides.




Title: Mini-symposium on Real World Data/Real World Evidence
Presenters: Martin Ho, Weili He, and Diqiong (Joan) Xie
Date and Time: Thursday, March 5, 2020, 12:00 p.m. – 2:00 p.m. Eastern time
Sponsor: Biopharmaceutical Section

Registration Deadline: Tuesday, March 3, at 12:00 p.m. Eastern time

Description:
Real-world data (RWD) generated from clinical practice and utilization of digital health technologies – outside of clinical trials – are regarded as a pragmatic data source with high potential to generate real-world evidence (RWE). There have been increasing interest in using RWD and translating RWD to RWE to support clinical development and life cycle management of medical products. Intriguing statistical challenges include assessment of fit-for-purpose RWD, leadership in improving internal processes and infrastructure, consideration of RW study design that minimizes biases and confounding, and utilization of advanced analytics for analysis of RW studies. In this webinar, speakers from the industry and the FDA will share their different perspectives on this topic. The first speaker will share some latest findings of the ASA Biopharmaceutical Section RWE Working Group, which was chartered in 2017 to facilitate statisticians’ leadership in this area. The second speaker will discuss various RWE examples at AbbVie, along with challenges in RWE research. The talk will focus on addressing challenges using quantitative approaches in translating RWD to robust RWE. The last presentation will talk about the current practice using real-world data in FDA, which includes the types of data we have used. A few examples will be presented from both pre-marketing and post-marketing settings.

Presentation 1: Use Real-World Data to inform design and analysis of clinical studies design in causal inference frameworks
Speaker: Martin Ho Martin.Ho@fda.hhs.gov

Presentation 2: Use of Real-World Evidence and Quantitative Approaches in Medical Research
Speaker: Weili He weili.he@abbvie.com

Presentation 3: Experience with Real-World Data and Evidence in Pre-Market and Post-Market Settings in FDA
Speaker: Diqiong (Joan) Xie Diqiong.Xie@fda.hhs.gov

Presenters:
Martin and Dr. Weili He co-chair the American Statistical Association (ASA) Biopharmaceutical Section Real-World Evidence Scientific Working Group, a group of more than 20 subject matter experts from academia, industry, and regulators. Martin is Associate Director of Science for Patient Inputs and Real-World Evidence, Office of Biostatistics & Epidemiology, U.S. Food and Drug Administration Center for Biologics Evaluation and Research (CBER). At CBER, he leads research efforts and establish review practices regarding quantitative patient inputs, real-world evidence (RWE), and digital health. He also represents CBER to coauthor multiple guidance documents, including PFDD and Digital Health technologies. He is CBER’s methodological lead for guidance development and building review capacities for clinical outcome assessments and patient preference information, as well as site-less clinical trials. Prior to CBER, he served as Associate Director for Quantitative Innovations at Office of Surveillance and Biometrics, FDA, Center for Device and Radiological Health, playing similar roles and leading the real-world performance component of the Center’s Digital Health Precertification Program. He is also the past president of the FDA Statistical Association and Chair of the ASA Medical Device and Diagnostic Section.

Dr. Weili He is a Senior Director, head of Global Medical Affairs Statistics, Data and Statistical Sciences at AbbVie Inc. She has a PhD in biostatistics. Prior to joining AbbVie, she worked in Clinical Biostatistics at Merck & Co., Inc. for over 20 years. Weili has published extensively in the areas of adaptive designs and benefit-risk assessment and is the author of more than 50 peer-reviewed publications in statistical and medical journals and the lead Editor of two books. In her current role at AbbVie in the last few years, Weili has been extensively involved in strategic and methodologic research in real-world data and real-world evidence (RWE), and has been involved in the review and development of numerous real-world studies at AbbVie. Weili is the co-founder and co-chair of the American Statistical Association Biopharmaceutical Section RWE Scientific Working Group, Chair-Elect 2020 of the American Statistical Association Biopharmaceutical Section, an Associate Editor for Statistics in Biopharmaceutical Research, and an elected Fellow of the American Statistical Association.

Dr. Diqiong Xie is a statistical reviewer in Office of Biostatistics in CDER, FDA. With the expertise in propensity score methods and causal inference, she has led numerous research projects in post-marketing drug safety. She is now focusing on oncology drugs review and research. She is heavily involved in using real-world data and evidence in the pre-marketing setting.

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, March 3, at 12:00 p.m. Eastern time, 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).

Register

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.