As part of ENAR's education initiative, our webinars promote continuing education for professional and student statisticians by disseminating cutting-edge knowledge to our membership. An ENAR webinar (or "webENAR") can strengthen your background in methodology and software, provide an opportunity to learn about a topic outside of your primary area of specialization, or deepen your understanding of an area in which you already work. We invite you to participate and benefit from the expertise of some of North America's leading statisticians and biostatisticians.
The Webinar Committee of the ENAR Regional Advisory Board (RAB) is coordinating this ongoing series of 1- to 2-hour webinars given by renowned experts. Registration fees are by membership category, with a reduced fee for student members. The webinars are planned to be broadly available and we encourage groups at your institution or workplace to participate together. WebENARs provide excellent learning opportunities for students and professionals alike.
Registration fees are determined by membership category.
Friday, September 15, 2017
10:00 am – 12:00 pm Eastern
Dr. Tyler J. VanderWeele
Professor of Epidemiology
Departments of Epidemiology and Biostatistics
Harvard School of Public Health
Methodology for assessing direct and indirect effects (i.e. mediation) has been used in the biomedical and social sciences for decades. More recently, theory and methods for mediation have been developed from the causal inference literature to extend traditional methods to more complex settings and to clarify the causal assumptions being made. The talk will (i) provide an overview of concepts, assumptions, and methods for causal mediation analysis, (ii) discuss sensitivity analysis methods to assess robustness of effect estimates to assumptions made, and (iii) present new results and methods on a 4-way decomposition that decomposes a total effect, in the presence of a mediator with which the exposure may interact, into four components: that due to just mediation, that due to just interaction, that due to both mediation and interaction, and that due to neither. The methodology will be illustrated by scientific and policy relevant examples from medicine, psychology, and genetics. Further book-length discussion of this material can be found in: VanderWeele TJ. (2015). Explanation in Causal Inference: Methods for Mediation and Interaction. Oxford University Press.
Register (September 15)
*Registration will close on September 14. You will receive access instructions the day prior to the webinar.
Friday, October 20, 2017
10:00 am – 12:00 pm Eastern
Dr. Christopher Jackson
Senior Statistician, MRC Biostatistics Unit, University of Cambridge
School of Clinical Medicine, Cambridge Institute of Public Health
Multi-state models are stochastic processes which describe how an individual moves between a set of discrete states in continuous time. They have been used for two broad classes of data. Firstly, for "panel data": intermittent observations of the state at a finite series of times, for a set of individuals, where transition times are not known. Secondly, for times to multiple events for a set of individuals, so that the state at any time is known. Combinations or slight variants of these two data types are also possible.
These observation schemes are associated with two distinct modelling frameworks. I will describe them both, concentrating on methods which can be used in general-purpose R packages. Panel data are typically modelled using Markov processes with constant or piecewise-constant transition hazards. Hidden Markov models, such as models with state misclassification, are also discussed. For time-to-event data, multi-state modelling is more reminiscent of an extension to survival analysis. This allows more flexibility, e.g. through semi-parametric or flexible parametric models for the transition hazards.
For each framework I will demonstrate how a range of models can be fitted, and useful outputs produced, with accessible software. The methods will be illustrated by applications in medicine, where the states describe stages of disease or death.
*Registration will close on October 19. You will receive access instructions the day prior to the webinar.
Friday, November 17, 2017
11:00 am – 1:00 pm Eastern
Dr. Mithat Gönen
Chief, Biostatistics Service
Memorial Sloan Kettering Cancer Center
Cancer clinical trials have traditionally been designed specific to a disease site (breast, lung, colon etc). This paradigm is being challenged by the advent of targeted treatments, regimens targeting molecular alterations in cancer cells. Since targeted treatments are not site-specific the trials evaluating them increasingly include multiple sites where the target is expressed. These trials are often called basket trials. In this WebENAR we will present several possible designs for basket trials: parallel design, aggregation design and hierarchical model-based design; comparing their operating characteristics, strengths and weaknesses. Although their applications have mostly been in oncology so far, basket trials can be used in any disease where targeted treatments can be used in molecularly defined subgroups. We will give examples of publicly available software that can be used to design and analyze basket trials.
*Registration will close on November 16. You will receive access instructions the day prior to the webinar.
Friday, December 1, 2017
11:00 am - 1:00 pm Eastern
Telba Irony, PhD
Center for Biologics Evaluation and Research
Regulatory authorities and patient advocacy groups have been paving the way towards engaging patients in medical product development and regulatory review. These efforts gave rise and relevance to the development of the Science of Patient Input, or SPI. SPI consists of scientifically valid qualitative and quantitative methods for capturing patient perspective information to incorporate it into product development and regulatory decision making. Two types of patient input, Patient Reported Outcomes (PRO) and Patient Preference Information (PPI) are expected to be captured in accordance with applicable scientific and statistical standards and best practices, and statisticians have a large role to play.
A PRO is a measurement based on a report of a patient’s health status that comes directly from the patient, without interpretation of the patient’s report by a clinician or anyone else. Some symptoms or other unobservable concepts known only to the patient, such as pain or fatigue, can only be measured by PRO measures.
PPI is a patient’s expression of desirability or value of one course of action or selection in contrast to others. It focuses on assessing the importance, or preferences, that patients place on the benefits, harms and other aspects of treatments.
In this Webinar we will introduce key elements concerning elicitation and use of patient preferences (PPI) to inform regulatory decision making. As an example, we will present a study commissioned by the FDA to elicit obese patients’ preferences in choosing weight-loss devices and show how these preferences can be used to inform regulatory decision making. We will describe the weight-loss device survey and present the survey results, which have been used to develop a decision-aid tool for regulatory reviewers. The tool provides estimates of patients’ benefit-risk tradeoff preferences and also stratifies patients according to their risk-tolerance. We will conclude the Webinar by sharing experiences in using patient preferences in the regulatory process and talking about best statistical practices for eliciting and using patient preference information.
*Registration will close on November 30. You will receive access instructions the day prior to the webinar.
Please contact Elena Polverejan if you have topic suggestions for future webinars.