Web-Based Lectures



Title: Introduction to Causal Mediation Analysis Using R
Presenter: Teppei Yamamoto
Date and Time: Thursday, March 9, 2017, 12:00 a.m. – 2:00 p.m. Eastern time
Sponsor: Mental Health Statistics Section

Registration Deadline: Tuesday, March 7, at 12:00 p.m. Eastern time

Description:
Researchers often seek to study not only whether a treatment has a causal effect on an outcome but also how and why such a causal relationship comes about. Causal mediation analysis is a popular method to analyze causal mechanisms using experimental or observational data. In this webinar, we provide an overview of the theoretical framework underpinning the mediation methods and discuss assumptions that play a key role for valid inference about causal mechanisms. We also cover practical issues in using the framework for social, behavioral and medical science applications. A particular focus will be on the R package mediation and how to use it in various applied settings.

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

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



Title: Collaboration Essentials: Structuring One-on-one Meetings
Presenter: Eric Vance and Heather Smith
Date and Time: Thursday, March 16, 2017, 2:00 p.m. – 3:30 p.m. Eastern time 
Sponsor: Section on Statistical Consulting

Registration Deadline: Tuesday, March 14, at 12:00 p.m. Eastern time

Description:
This webinar is for statisticians and data scientists whose goal is to increase their impact on projects by improving the way they collaborate with others. We believe that structuring a problem and then structuring a meeting with a domain expert are first steps toward establishing collaborative working relationships. We will introduce the Q1Q2Q3 process, which is effective structure for any applied statistics or consulting problem. We will also cover the POWER structure, which provides an easy-to-use method for organizing a meeting for success. With these structures in place to support collaboration, statisticians can free their brains to focus on the context of the problem and the details that lead to effective statistical solutions to answer the domain experts’ research, business, or policy questions. We have found that by using such processes and structures, statisticians can increase their impact on projects and improve their collaborative relationships with others.

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, March 14, with the access information to join the webinar and the link to download and print a copy of the presentation slides.



Title: Introduction to Functional Neuroimaging
Presenter: Martin Lindquist
Date and Time: Thursday, April 13, 2017, 11:00 a.m. – 1:00 p.m. Eastern time 
Sponsor: Mental Health Statistics Section

Registration Deadline: Tuesday, April 11, at 12:00 p.m. Eastern time

Description:
Understanding the brain is arguably among the most complex, important and challenging issues in science today. Neuroimaging is an umbrella term for an ever-increasing number of minimally invasive techniques designed to study the brain. These include a variety of rapidly evolving technologies for measuring brain properties, such as structure, function and disease pathophysiology. The analysis of neuroimaging data is an example of a modern ‘big data’ problem, and the data is not only large but also has a complex correlation structure in both space and time. Statistics plays a crucial role in understanding the nature of the data and obtaining relevant results that can be used and interpreted by neuroscientists. In this talk we will focus on methods for performing functional neuroimaging (e.g., functional MRI) and discuss how these techniques can be used to detect areas of the brain activated by a task, determine how different brain regions are connected and communicate with one another, and how brain measurements can be used for prediction and classification purposes.

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

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, April 11, with the access information to join the webinar and the link to download and print a copy of the presentation slides.



Title:
Sequential and Adaptive Analysis with Time-to-Event Endpoints
Presenter: Scott S. Emerson, University of Washington
Date and Time: Tuesday, April 18, 2017, 12:00 p.m. – 2:00 p.m. Eastern time 
Sponsor: Biopharmaceutical Section

Registration Deadline: Friday, April 18, at 12:00 p.m. Eastern time

Description:
A great many confirmatory phase 3 clinical trials have as their primary endpoint a comparison of the distribution of time to some event (e.g., time to death or progression free survival). The most common statistical analysis models include the logrank test (usually unweighted, but possibly weighted) and/or the proportional hazards regression model. Just as commonly, the true distributions do not satisfy a proportional hazards assumption. Providing users are aware of the nuances of those methods, such departures need not preclude the use of those analytic techniques any more than violations of the location shift hypothesis precludes the use of the t test. However, with the increasing interest in the use of adaptive sample size re-estimation, adaptive enrichment, response-adaptive randomization, and adaptive selection of doses and/or treatments, there are many issues (scientific, ethical, statistical, and logistical) that need to be considered. In fact, when considering references to “less well understood” methods in the draft FDA guidance on adaptive designs, it is likely the case that many of the difficulties in adaptive time to event analyses can relate as much to aspects of survival analysis that are “less well understood” as to aspects of the adaptive methodology that has not been fully vetted. In this webinar I discuss some aspects of the analysis of censored time to event data that must be carefully considered in sequential and adaptive sampling. In particular, we discuss how the changing censoring distribution during a sequential trial affects the analysis of distributions with crossing hazards and crossing survival curves, as well as issues that arise owing to the ancillary information about eventual event times that might be available on subjects who are censored at an adaptive analysis.

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

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 Friday, April 14, with the access information to join the webinar and the link to download and print a copy of the presentation slides.



Title:
Intensive Longitudinal Data Analysis Using Mplus
Presenters: Bengt Muthen, Tihomir Asparouhov, and Ellen Hamaker, University of California, Los Angeles
Date and Time: Thursday, April 20, 2017, 12:00 p.m. – 2:00 p.m. Eastern time 
Sponsor: Mental Health Statistics Section

Registration Deadline: Tuesday, April 18, at 12:00 p.m. Eastern time

Description:
This talk discusses new methods for analyzing intensive longitudinal data, such as obtained with ecological momentary assessments, experience method sampling, ambulatory assessments, and daily diaries. Typically, such data have a large number of time points, T = 20-150. Single-level (N=1) as well as multilevel (N > 1) time series models with random effects varying across subjects are handled using a dynamic structural equation model (DSEM) and Bayesian estimation implemented in the Mplus Version 8 software. DSEM for N=1 time series analysis can be used to model the dynamics within a particular individual over time. Additionally, N > 1 multilevel DSEM includes extensions of time series models, such that at level 1 a time series model is used to model the within-person dynamics of a process over time, while at level 2 individual differences in the parameters that capture these dynamics are modeled. DSEM can handle multivariate outcomes as well as latent variables, and random effects can be both predicted from but also predictors of level 2 variables. DSEM is available with auto-regressive and moving-average components both for observed-variable models such as regression and cross-lagged analysis and for latent variable models such as factor analysis, IRT, structural equation modeling, and mixture modeling. DSEM also handles time-varying effect modeling (TVEM) where parameters change not only across individuals but also across time, making it suitable for assessing intervention effects. Several examples are discussed from application areas such as:

  • multilevel AR(1) model with random mean, random AR, and random variance
  • multilevel AR(1) model with measurement error
  • multilevel ARMA(1,1) model
  • multilevel cross-lagged modeling
  • multilevel AR modeling with a trend
  • latent multilevel AR(1) model with multiple indicators
  • latent multilevel VAR(1) model and dynamical networks
  • dynamic SEM
  • dynamic latent class analysis using hidden Markov and Markov-switching AR models

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

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, April 18, with the access information to join the webinar and the link to download and print a copy of the presentation slides.