2015 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 2015 course offerings cover the entire year. To allow as much
flexibility in planning, the best use of the presenter's travel time and
Chapter funds the deadline for application is January 15, 2015. Applications may be submitted throughout 2015 but courses will be subject to speaker availability and Travel Course funding.
2015 Traveling Course Offering:
Working with R to Analyze and Plot Data
This workshop focuses on a data centric introduction to using R, in a reproducible way, incorporating lots of data graphics and exploratory data analysis. The sections will center around contemporary data examples, showing participants how to work their way through the analysis, and answer questions about the data. Introduction to R and reproducibility. Participants get started with R, learn how to organize a work project and use knitr to incorporate code into documentation to produce pdf, html or Word documents.
Design and Analysis of Research Studies Using Generalized Linear Mixed Models
Course presents applications of generalized linear mixed models (GLMMs). Focus is especially on GLMMs for design and analysis of experiments with non-normal data. Material is at an applied level, accessible to those familiar with linear models.Participants will learn that GLMMs are an encompassing family and understand the differences and similarities in estimation and inference within the family. We discuss issues in working with correlated, non-normal data, such as overdispersion, marginal and conditional models, and model diagnostics. We present GLMMs for common non-normal response variables – count, binomial and multinomial, time-to-event, continuous proportion – in conjunction with common designs – blocked, split-plots, repeated measures. Numerous examples will be presented.The afternoon continues with GLMM applications and associated issues, including comparison of estimation methods, computation of power and sample size, model selection, and inferential tasks with and without adjustments.Numerous examples will be used to illustrate all topics. Examples use tools in SAS/STAT and R, but the principles should be applicable to any GLMM-capable software.
Bayesian Methods and Computing for Evidence Synthesis and Network Meta-Analysis
As the era of "big data" arrives in full force for health care and pharmaceutical development, researchers in these areas must turn to increasingly sophisticated statistical tools for their proper analysis. Bayesian statistical methods, while dating in principle to the publication of Bayes' Rule in 1763, have only recently begun to see widespread practical application due to advances in computation and software. This one-day short course introduces Bayesian methods, computing, and software, and goes on to elucidate their use in evidence synthesis and network meta-analysis (NMA). Broad application of these methods has been driven by an increased need for quantitative health technology assessment (HTA), especially comparative effectiveness research (CER). In particular, Bayesian methods facilitate borrowing of strength across treatments, trials, and outcomes (say, both safety and efficacy), as well as provide a natural framework for filling in missing data values that respect the underlying correlation structure in the data. We include descriptions and live demonstrations of how the methods can be implemented in BUGS, R, and versions of the BUGS package callable from within R.
Using Propensity Scores to Effectively Design and Analyze Observational Studies
This course describes and demonstrates effective strategies for using propensity score methods to address the potential for selection bias in observational studies comparing the effectiveness of treatments or exposures. We review the main analytical techniques associated with propensity score methods (matching, weighting, multivariate regression adjustment and stratification using the propensity score, sensitivity analysis for matched samples) and describe key strategic concerns related to effective propensity score estimation, assessment and display of covariate balance, choice of analytic technique, and communicating results effectively. Although we will focus on established approaches to dealing with design and analytical challenges, we conclude the session by reviewing some literature regarding recent methodological advances in propensity scores and application of propensity score methods to problems in health policy research.
2015 Traveling Course Application
- DEADLINE: January 15, 2015
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