SPEED: Statistical Computing and Statistics in Genomics Part 1 — Contributed Speed
Section on Statistical Computing, Section for Statistical Programmers and Analysts, Section on Statistical Graphics, Section on Statistics in Genomics and Genetics
Bayesian Hyperbolic Multi-Dimensional Scaling Bolun Liu, Departments of Statistics, University of Washington; Tyler McCormick, University of Washington; Adrian E. Raftery, University of Washington; Shane Lubold, University of Washington
Demographic Profile and Factors of Homeownership Disparity in the United States Rachel Richardson, Pacific Northwest National Laboratory - Battelle; David Degnan, Pacific Northwest National Laboratory - Battelle; Anastasiya Prymolenna, Pacific Northwest National Laboratory - Battelle; Natalie Winans, Pacific Northwest National Laboratory - Battelle; Lisa Bramer, Pacific Northwest National Laboratory - Battelle
SPEED: Statistical Computing and Statistics in Genomics Part 2 — Contributed Poster Presentations
Section on Statistical Computing, Section for Statistical Programmers and Analysts, Section on Statistical Graphics, Section on Statistics in Genomics and Genetics
Bayesian Hyperbolic Multi-Dimensional Scaling Bolun Liu, Departments of Statistics, University of Washington; Tyler McCormick, University of Washington; Adrian E. Raftery, University of Washington; Shane Lubold, University of Washington
Demographic Profile and Factors of Homeownership Disparity in the United States Rachel Richardson, Pacific Northwest National Laboratory - Battelle; David Degnan, Pacific Northwest National Laboratory - Battelle; Anastasiya Prymolenna, Pacific Northwest National Laboratory - Battelle; Natalie Winans, Pacific Northwest National Laboratory - Battelle; Lisa Bramer, Pacific Northwest National Laboratory - Battelle
Causal Effects and Their Estimation: A Practical Workflow, from Planning to Application — Professional Development Continuing Education Course
ASA, Section for Statistical Programmers and Analysts
Instructor(s): Clay Thompson, SAS; Michael Lamm, SAS; Yiu-Fai Yung, SAS
When does an effect estimate have a causal interpretation and which effect has an interpretation appropriate for your question? This course provides an overview of causal inference that is designed to answer these types of practical questions when data from an observational or nonrandomized study are analyzed. It describes the differences between possible choices for causal estimands, tools for analyzing a data generating process, and statistical methods that support valid effect estimation. It reviews the definition of causal effects in a potential outcomes framework, discusses estimates for total effects, and describes the decomposition of effects through causal mediation analysis, with an emphasis on dichotomous treatments. Directed acyclic graphs (DAGs) are presented as a tool for representing a data generating process, reasoning about possible data generating processes, and constructing valid estimation strategies. For the estimation of treatment effects, this course discusses the appropriate use of propensity score methods, doubly robust methods, and a regression approach to causal mediation analysis. This course provides a review of the theory behind these methods and then focuses on illustrating their application with examples that use SAS/STAT® software. This material demonstrates a rigorous workflow for causal effect estimation. No prior experience with the methods is assumed.
Moving the Needle on Innovation in Clinical Trial Designs and Strategies: Vignettes of Statistical Leadership and Lessons Learned from a Global Pandemic — Invited Panel
Biopharmaceutical Section, Section for Statistical Programmers and Analysts, Stats. Partnerships Among Academe Indust. & Govt. Committee
Organizer(s): Fanni Natanegara, Eli Lilly; Wei Shen, Eli Lilly
New Challenges in Statistical Learning and Inference for Complex Data — Topic Contributed Papers
Section for Statistical Programmers and Analysts, Section on Statistical Learning and Data Science, Section on Nonparametric Statistics, Section on Statistical Computing
Organizer(s): Ganggang Xu, University of Miami
Chair(s): Hou-Cheng Yang, U.S. Food and Drug Administration
Recent Advances in Statistical Machine Learning — Topic Contributed Papers
Section for Statistical Programmers and Analysts, Section on Statistical Learning and Data Science, Section on Nonparametric Statistics, International Chinese Statistical Association
