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, February 24, 2017
10:00 am – 12:00 pm Eastern
Dr. Michael O’Kelly
Dr. Bohdana Ratitch
Dr. Ilya Lipkovich
Center for Statistics in Drug Development
Most experiments have missing data. When there are missing data, it is useful to provide sensitivity analyses to allow the reader of the account of the research to assess the robustness to the missing data of any conclusions made. Using the pattern-mixture framework, a variety of assumptions can be implemented with regard to categories of missing outcomes. Assumptions that would tend to undermine the alternative hypothesis can be especially useful for assessing robustness of conclusions. Multiple imputation (MI) is one quite straightforward way of implementing such pattern-mixture approaches. While MI is a standard tool for continuous outcomes, recently researchers have come up with ways of implementing MI for other outcomes, such as time-to-event and recurrent-event outcomes. This webinar describes a number of these new applications of the MI idea. The strengths and weaknesses of these approaches are described and illustrated via examples and simulations.
Register (February 24)
*Registration will close on February 23. You will receive access instructions the day prior to the webinar.
Friday, April 21, 2017
11:00 am – 1:00 pm Eastern
Dr. Layla Parast
The use of surrogate markers to estimate and test for a treatment effect has been an area of popular research. Given the long follow-up periods that are often required for treatment or intervention studies, appropriate use of surrogate marker information has the potential to decrease required follow-up time. However, previous studies have shown that using inadequate markers or making inappropriate assumptions about the relationship between the primary outcome and the surrogate marker can lead to inaccurate conclusions regarding the treatment effect. Many of the available methods for identifying, validating and using surrogate markers to test for a treatment effect tend to rely on restrictive model assumptions and/or focus on uncensored outcomes. In this course, I will describe different approaches to quantify the proportion of treatment effect explained by surrogate marker information in both a non-survival outcome setting and censored survival outcome setting. One described approach will be a nonparametric method that can accommodate a setting where individuals may experience the primary outcome before the surrogate marker is measured. I will illustrate the procedures using an R package available on CRAN to examine potential surrogate markers for diabetes with data from the Diabetes Prevention Program.
*Registration will close on April 20. You will receive access instructions the day prior to the webinar.
Friday, May 19, 2017
10:00 am – 12:00 pm Eastern
Kenneth G. Kowalski, MS
Kowalski PMetrics Consulting, LLC
Wenping Wang, PhD
Novartis Pharmaceuticals Corporation
This WebENAR will be presented in two parts. The first part will focus on a commentary presented by Ken Kowalski discussing the overlap between statisticians and pharmacometricians working in clinical drug development. Individuals with training in various academic disciplines including pharmacokinetics, pharmacology, engineering and statistics, to name a few, have pursued careers as pharmacometricians. While pharmcometrics has benefitted greatly from advances in statistical methodology, there is considerable tension and skepticism between biostatisticians and pharmacometricians as they apply their expertise to drug development applications. This talk explores some of the root causes for this tension and provides some suggestions for improving collaborations between statisticians and pharmcometricians. The talk concludes with a plea for more statisticians to consider careers as pharmacometrics practitioners. The second part of the WebENAR will highlight a case study presented by Wenping Wang illustrating the application of pharmacokinetic-pharmacodynamic modeling of the time to first flare to support dose justification of Canakinumab in a sBLA submission. The case study will conclude with some observations regarding team interactions between statisticians and pharmacometricians that resulted in a successful sBLA submission.
*Registration will close on May 18. You will receive access instructions the day prior to the webinar.
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.
Please contact Elena Polverejan if you have topic suggestions for future webinars.