Web-Based Lectures


 



Title: Bayesian Biopharmaceutical Applications Using SAS
Presenter: Fang Chen, SAS Institute Inc.
Date and Time: Tuesday, April 11, 2017, 12:00 p.m. – 2:00 p.m. Eastern time
Sponsor: Biopharmaceutical Section

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

Description:
This two-part tutorial first introduces the general purpose simulation MCMC procedure in SAS, then presents a number of pharma-related data analysis examples and case studies. The objective is to equip attendees with useful Bayesian computational tools through worked-out examples that are often encountered in the pharma industry.

The MCMC procedure is a general purpose Markov chain Monte Carlo simulation tool designed to fit a wide range of Bayesian models, including linear or nonlinear models, multi-level hierarchical models, models with nonstandard likelihood function or prior distributions, and missing data problems. The first part of the tutorial provides a brief introduction to PROC MCMC and demonstrates its use with a number of simple applications, such as Monte Carlo simulation, regression models, and random-effects models.

The second part of the tutorial takes a topic-driven approach to cover a number of case studies encountered in the pharmaceutical field. Topics include posterior predictions, borrowing historical information, analysis of missing data, and topics in Bayesian designs and simulations.

This tutorial is intended for statisticians who are interested in Bayesian computation. Attendees should have a basic understanding of Bayesian methods (the tutorial does not allocate time covering basic concepts of Bayesian inference) and experience using the SAS language.

Registration Fees:
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 7, 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 14, 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.



Title: An Update on the PhUSE Standard Analyses and Code Sharing Working Group
Presenters: Mary Nilsson, Eli Lily, and Hanming Tu, Accenture
Date and Time: Tuesday, May 2, 2017, 11:00 a.m. – 12:00 p.m. Eastern time
Sponsors: Biopharmaceutical Section and Section for Statistical Programmers and Analysts

Registration Deadline: Friday, April 28, at 12:00 p.m. Eastern time

Description:
In this webinar, PhUSE Working Group leads will present a summary of recent activity concerning clinical data display standards proposed for the pharma industry, and will also introduce a cross-company repository of statistical programs which support these displays. The main goal for the PhUSE Standard Analyses and Code Sharing Working Group is to “leverage crowd-sourcing to improve the content and implementation of analyses for medical research, leading to better data interpretations and increased efficiency in the clinical drug development and review processes”. Much progress has been made within this working group (which started in 2012), but this effort requires additional resources to fully realize the vision.

The working group is providing recommendations for analyses, tables, figures, and listings for data that are common across therapeutic areas (laboratory measurements, vital signs, electrocardiograms, adverse events, demographics, medications, disposition, hepatotoxicity, pharmacokinetics). Ten white papers are at various stages of development, including 6 that have been finalized. The latest white paper to be published covers analyses and displays for adverse events.

The working group has also created an online platform for sharing code in GitHub. The code repository contains a wealth of scripts that have been written by PhUSE members or donated by other organizations. The main goals for the coming years are 1) to increase the usability, quality, and acceptability of the code in the repository; 2) to continue the creation and maintenance of white papers on recommendations on statistical analyses, including mock tables, figures, and listings; 3) to develop scripts based the recommendations from the white papers; 4) to provide test data for various purposes including script qualification through a newly established Test Data Factory project.

The PhUSE working group looks forward to a potential collaboration with the ASA to make progress on these initiatives.

Registration Fees:
This webinar is free to anyone who would like to attend. However, registration is limited so you must register to receive the access information. The access information will be emailed to everyone who has registered the afternoon of Friday, April 28.

Each registration is allowed one web connection. Audio will be 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



Title: Network Analysis in Cross-sectional Data Using R
Presenter: Eiko Fried
Date and Time: Thursday, October 19, 2017, 12:00 p.m. – 2:00 p.m. Eastern time
Sponsors: Mental Health Statistics Section

Registration Deadline: Tuesday, October 17, at 12:00 p.m. Eastern time

Description:
Analysis of mental health data is usually based on sum-scores of symptoms or the estimation of factor models. Both types of analyses disregard direct associations among symptoms that are well-understood in clinical practice: mental disorders can be conceptualized as vicious circles of problems that are hard to escape. A novel research framework, the network perspective on psychopathology, understands mental disorders as complex networks of interacting symptoms. Despite its comparably recent debut, with conceptual foundations in 2008 and empirical foundations in 2010, the framework has received considerable attention and recognition in recent years.

In this webinar, we will use R to learn about (1) network estimation, (2) network inference, and (3) network stability in cross-sectional data. Regarding network estimation, the state-of-the-art network model for cross-sectional data is the pairwise Markov Random Field or regularized partial correlation network that estimates the conditional dependence relations among items. We will learn to estimate appropriate network models for our data: the Ising Model for binary data, and the Gaussian Graphical Model for metric data. In this first section, we will also cover regularization methods that avoid the estimation of false positive associations in networks. The second topic, network inference, covers graph theoretical measures such as centrality that allow us to interpret networks. What symptoms are most connected with other symptoms? Finally, network stability allows us to gain insight into the robustness of our networks. We conclude the webinar with advanced methods such as the statistical comparison of networks, and how to deal with ordinal and mixed data. Is it noteworthy that network analysis is not limited to psychopathology data, but has been employed to study other psychological constructs such as intelligence, personality traits, and attitudes.

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

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, October 17, with the access information to join the webinar and the link to download and print a copy of the presentation slides.