Short course registration is now closed.

A flat fee allows participants access to all short courses. Recordings of all short courses will be available to registrants through December 31, 2025.

Course Schedule

All courses will be taught online. 

Saturday, November 15

8:30 a.m. to Noon PST

Introduction to causal inference using machine learning methods – Part 1

Instructors: Charles Wolock, PhD, University of Rochester; Marco Carone, PhD, University of Washington

1 p.m. to 4:30 p.m. PST

Introduction to causal inference using machine learning methods – Part 2

Instructors: Charles Wolock, PhD, University of Rochester; Marco Carone, PhD, University of Washington

Sunday, November 16

8:30 a.m. to Noon PST

Enhancing randomized clinical trials with real-world data: A causal inference perspective 

Instructor: Shu Yang, PhD, North Carolina State University

Motivation and Course Description: The 21st Century Cures Act, enacted in 2016, highlights the importance of precision medicine and the utility of real-world data (RWD) to accelerate the development and evaluation of new treatments. It encourages the FDA and other regulatory bodies to consider real-world evidence (RWE) alongside traditional randomized controlled trials (RCTs).

RCTs are widely considered the gold standard for causal inference due to their internal validity. However, they often suffer from practical limitations such as restrictive eligibility criteria and limited sample sizes. In contrast, RWD offers broader population coverage and reflects clinical practice more realistically but is prone to confounding and other biases. Integrating RCTs with RWD offers the potential to combine the strengths of both data sources, achieving internal validity from RCTs and external validity from RWD, thereby enabling more generalizable, efficient, and timely treatment evaluations.

This short course will introduce statistical frameworks and methodologies that facilitate the integration of RCTs and RWD to:

  1. Improve the generalizability of RCT findings to broader patient populations,
  2. Enhance the estimation of treatment effect heterogeneity for precision medicine, and
  3. Address challenges such as covariate shift, unmeasured confounding, and model misspecification through robust statistical and machine learning techniques.

Simulated case studies and hands-on demonstrations using publicly available R packages will support the conceptual and methodological content. A foundational understanding of clinical trials and causal inference is recommended.

1 p.m. to 4:30 p.m. PST

Using covariates for greater precision and power in randomized trials

Instructor: Ting Ye, PhD, University of Washington

Since the FDA released a guidance for industry on “Adjustment for Covariates in Randomized Clinical Trials for Drugs and Biological Products” in May 2023, there has been increased interest in using covariate-adjusted analyses to improve efficiency for demonstrating and quantifying treatment effects. This short course will cover key concepts and useful methods for covariate adjustment with continuous, discrete, and time-to-event outcomes, and illustrate the impact of the methods through case studies. We will also provide a dedicated session with hands-on activities using our R package family RobinCar and RobinCar2 to allow participants to familiarize themselves with the methods and tools.

In Part I of this course, we first provide an overview of the key elements and concepts of covariate adjustment. Then for continuous, discrete, and time-to-event outcomes respectively, we will introduce the state-of-the-art covariate adjustment methods (estimand, estimation, and inference), their pros and cons, and how to account for practical issues (e.g., stratified randomization, sparsity, missing data). We will illustrate the use of the methods through case studies.

In Part II of this course, we introduce our R package family RobinCar and RobinCar2, which is a one-stop and user-friendly platform to apply covariate-adjustment methods for continuous, discrete, and time-to-event outcomes for trials with simple, permuted block, and Pocock-Simon minimization randomization schemes. We present step-by-step walk-through of how to apply the covariate adjustment methods using these R packages. Participants will work on hands-on exercises of covariate adjustment on trial data.