Special Topic Courses

Please contact the instructor if you have questions about prerequisites or other aspects of the course.

Spring 2025

• ST 790: Advanced Design and Analysis of Experiments
  
  • Instructor: Jon Stallrich
  • Description: The course provides an overview of foundational approaches to ranking experimental designs under an optimality criterion. Approximate design theory and equivalence theorems are introduced and search algorithms are discussed for finding exact optimal designs. New techniques for screening experiments, such as definitive screening designs, OMARS designs, and supersaturated designs, will then be studied. An overview will be given for the sequential design and analysis of computer experiments, which is based around Gaussian processes and Bayesian optimization. Time permitting, the instructor may choose to introduce important design problems with online-controlled experiments, optimal designs for penalized regression, and optimal design for generalized linear models.
• ST 790: Dynamic Treatment Regimes
  
  • Instructor: Marie Davidian
  • Description: This course will provide a comprehensive introduction to methodology for data-based development and evaluation of dynamic treatment regimes. A dynamic treatment regime is a set of sequential decision rules, each corresponding to a key point in a disease or disorder process at which a decision on the next treatment action must be made. Each rule takes patient information to that point as input and returns the treatment s/he should receive from among the available options, thus tailoring treatment decisions to a patient’s individual characteristics. Dynamic treatment regimes formalize how clinicians make decisions in practice by synthesizing evolving information on a patient and are thus of considerable importance in precision medicine. Dynamic treatment regimes are also relevant in other contexts in which sequential decisions on interventions or policies must be made, as in education, engineering, economics and finance, and resource management. Of critical importance is the notion of an optimal treatment regime, one that, if used to select treatments for the patient population, would lead to the most beneficial outcome on average. Methods for estimation of dynamic treatment regimes and in particular optimal treatment regimes from data will be motivated and developed through a formal time-dependent causal inference framework. The gold standard study design for developing and evaluating regimes is the sequential multiple assignment randomized trial (SMART), considerations for which will be discussed. Inference for optimal treatment regimes is a nonstandard statistical problem and is thus notoriously difficult; an introduction to this challenge will be presented. Examples throughout the course will be drawn from cancer and other chronic disease research and research in the behavioral, educational, and other sciences.  Use of the comprehensive R package DynTxRegime to implement many of the methods discussed in the lectures will be introduced. Students completing this course will have a foundation in causal inference and fundamental results and methods for dynamic treatment regimes that will provide the basis for study of the rapidly evolving literature on dynamic treatment regimes and data-driven sequential decision-making in precision medicine.
• ST 295: Introduction to the Foundations of Data Science with R
  
  • Instructor: Elijah Meyer
  • Description:  In this course, we will discover patterns in data through the use of effective data visualization tools, exploratory data analysis, and modeling techniques. We will gain experience working with real-world messy data, and use data tidying and data wrangling techniques to make the data suitable for exploration. An emphasis will be placed on creating reproducible and shareable work. This course highlights the importance of and uses version control software for individual and collaborative work. The course will focus on the R statistical computing language.