2016 Traveling Courses
The Council of Chapters Traveling Courses provide low cost, local
courses for ASA Chapters. The Council of Chapters sponsors this
activity by covering speaker travel expenses and honoraria. The Chapter in turns covers advertising, local arrangements
(including hotel and local travel), course materials, and registration (the greater of $25 for each
attendee or $500 to go back to the Council of Chapters).
The Traveling Course committee, made up of at least one member of
each of the six Chapter Districts and ASA staff liaison, chooses the
local Chapters and works with the speakers and Chapters to select dates
for each course. Each course is typically given more than once in one
trip by the speaker. The courses are often awarded according to
geographical proximity to keep travel cost low, with special
consideration given to smaller Chapters and Chapters that have not had a
traveling course recently.
The 2016 course offerings cover the entire year. To allow as much
flexibility in planning the best use of the instructors' travel time, the deadline for applications is December 15, 2015, at 8:00 p.m. Eastern time. Applications may be submitted throughout 2016 but courses will be subject to speaker availability and Travel Course funding.
2016 Traveling Course Offerings:
Guidelines for Using State-of-the-Art Methods to Estimate Propensity Score and Inverse Probability of Treatment Weights When Drawing Causal Inferences
Instructors: Beth Ann Griffin, Lane Burgette, Matthew Cefalu, and Daniel McCaffrey
Estimation of causal effects is a primary activity of many studies. Examples include testing whether a substance abuse treatment program is effective, whether an intervention improves the quality of mental health care, or whether incentives improve retention of military service members. Controlled, random-assignment experiments are the gold standard for estimating such effects. However, experiments are often infeasible, forcing analysts to rely on observational data in which treatment assignments are out of the control of the researchers. This short course will provide an introduction to causal modeling using the potential outcomes framework and the use of propensity scores and weighting (i.e., propensity score or inverse probability of treatment weights) to estimate causal effects from observational data. It will also present step-by-step guidelines on how to estimate and perform diagnostic checks of the estimated weights for testing the relative effectiveness of two or more interventions. Attendees will gain hands-on experience estimating propensity score weights using boosted models in R, SAS and Stata; evaluating the quality of those weights; and using them to estimate intervention effects. Additional topics (if time allows) can also include methods for conducting sensitivity analyses for unobserved confounding and estimation of the effects of time-varying treatments. Attendees should be familiar with linear and logistic regression; no knowledge of propensity scores is expected.
R Programming: From the Classroom to the Real World
Instructor: Jay Emerson
This course both provides an introduction to and review of the core R language and teaches essentials of R programming to R users at a range of levels. It uses real-world data problems accessible to all audiences.
Specifically, there are two target audiences: instructors at all levels who aspire to use R in their courses and for their research and practitioners who seek to add R to their portfolio and become more productive in outside-the-box problem solving
These audiences are more similar than you might expect and can learn from each other in workshop exercises solving real-world data challenges. The distinction between programming (or scripting) with R and using R is an important one. Most people can use R as a tool for a small number of focused tasks that fit neatly into different boxes. This workshop emphasizes problem solving outside-the-box, where no single function or package is likely to be sufficient. The process is as important as the solution, and this approach to the R language is invaluable in the classroom and in the real world.
Introduction to Statistics for Spatio-Temporal Data
Instructor: Christopher Wikle
The course gives a contemporary presentation of spatio-temporal processes and data analysis, bridging classic ideas with modern hierarchical statistical modeling concepts. From understanding environmental processes and climate trends to developing new technologies for mapping public-health data and the spread of invasive-species, there is a high demand for statistical analyses of data that take spatial, temporal, and spatio-temporal information into account. This course presents a systematic approach to key quantitative techniques for the statistical analysis of such data that features hierarchical statistical modeling, with an emphasis on dynamical spatio-temporal models. The material follows the book by Cressie and Wikle, Statistics for Spatio-Temporal Data (2011) - John Wiley and Sons, Hoboken, NJ. Many examples will be included, along with some basic applications from various R packages.
Statistical Issues in Design and Analysis of Pragmatic Clinical Trials
Instructor: Andrea Troxel
There is increasing interest in exploring the comparative effectiveness of therapies for a wide range of conditions. Pragmatic clinical trials offer promise. Features of pragmatic trials include simplicity of interventions or treatments, limited eligibility criteria for participants, streamlined approaches to consent, contact with participants occurring within existing processes, and outcome data obtained through administrative sources such as existing electronic health records, claims data, or registries. Pragmatic trials must still include randomization as a fundamental tool to allow valid comparison of interventions. The desired features of pragmatic trials give rise to a number of design and analytic challenges. This course will review the structure of pragmatic trials, describe some of the special design issues, and offer new approaches to handle them. Examples from actual trials in biomedical research will be provided.
2016 Traveling Course Application
- DEADLINE: December 15, 2015, at 8:00 p.m. Eastern time
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